TQC · 2024

Finding a high (or low) energy state of a given quantum Hamiltonian is a potential area to gain a provable and practical quantum advantage. A line of recent studies focuses on Quantum Max Cut, where one is asked to find a high energy state of a given antiferromagnetic Heisenberg Hamiltonian. In this work, we present a classical approximation algorithm for Quantum Max Cut that achieves an approximation ratio of 0.595, outperforming the previous best algorithms of Lee (0.562, generic input graph) and King (0.582, triangle-free input graph). The algorithm is based on finding the maximum weighted matching of an input graph and outputs a product of at most 2-qubit states, which is simpler than the fully entangled output states of the previous best algorithms

Port-based teleportation (PBT) is a form of quantum teleportation in which no corrective unitary is required on the part of the receiver. Two primary regimes exist - deterministic PBT in which teleportation is always successful, but is imperfect, and probabilistic PBT, in which teleportation succeeds with probability less than one, but teleportation is perfect upon a success. Two further regimes exist within each of these in which the resource state used for the teleportation is fixed to a maximally entangled state, or free to be optimised. Recently, works resolved the long-standing problem of efficiently implementing port-based teleportation, tackling the two deterministic cases for qudits. Here, we provide algorithms in all four regimes for qubits. Emphasis is placed on the practicality of these algorithms, where we give polynomial improvements in the known gate complexity for PBT, as well as an exponential improvement in the required number of ancillas (albeit in separate protocols). Our approach to the implementation of the square-root measurement in PBT can be directly generalised to other highly symmetric state ensembles. For certain families of states, such a framework yields efficient algorithms in the case that the Petz recovery algorithm for the square-root measurement runs in exponential time.

Understanding and approximating extremal energy states of local Hamiltonians is a central problem in quantum physics and complexity theory. Recent work has focused on developing approximation algorithms for local Hamiltonians, and in particular the ``Quantum Max Cut'' (QMaxCut) problem, which is closely related to the antiferromagnetic Heisenberg model. In this work, we introduce a family of semidefinite programming (SDP) relaxations based on the Navascues-Pironio-Acin (NPA) hierarchy which is tailored for QMaxCut by taking into account its SU(2) symmetry. We show that the hierarchy converges to the optimal QMaxCut value at a finite level, which is based on a characterization of the algebra of SWAP operators. We give several analytic proofs and computational results showing exactness/inexactness of our hierarchy at the lowest level on several important families of graphs. We also discuss relationships between SDP approaches for QMaxCut and frustration-freeness in condensed matter physics and numerically demonstrate that the SDP-solvability practically becomes an efficiently-computable generalization of frustration-freeness. Furthermore, by numerical demonstration we show the potential of SDP algorithms to perform as an approximate method to compute physical quantities and capture physical features of some Heisenberg-type statistical mechanics models even away from the frustration-free regions.

Port-based teleportation (PBT) is a variant of quantum teleportation that, unlike the canonical protocol by Bennett et al., does not require a correction operation on the teleported state. Since its introduction by Ishizaka and Hiroshima in 2008, no efficient implementation of PBT was known. We close this long-standing gap by building on our recent results on representations of partially transposed permutation matrix algebras and mixed quantum Schur transform. We construct efficient quantum algorithms for probabilistic and deterministic PBT protocols on n ports of arbitrary local dimension, both for EPR and optimized resource states. We describe two constructions based on different encodings of the Gelfand-Tsetlin basis for n qudits: a standard encoding that achieves O(n) time and O(nlog(n)) space complexity, and a Yamanouchi encoding that achieves O(n^2) time and O(log(n)) space complexity, both for constant local dimension and target error. We also describe efficient circuits for preparing the optimal resource states.

We define a general formulation of quantum PCPs, which captures adaptivity and multiple unentangled provers, and give a detailed construction of the quantum reduction to a local Hamiltonian with a constant promise gap. The reduction turns out to be a versatile subroutine to prove properties of quantum PCPs, allowing us to show: (i) Non-adaptive quantum PCPs can simulate adaptive quantum PCPs when the number of proof queries is constant. In fact, this can even be shown to hold when the non-adaptive quantum PCP picks the proof indices simply uniformly at random from a subset of all possible index combinations, answering an open question by Aharonov, Arad, Landau and Vazirani (STOC '09). (ii) If the q-local Hamiltonian problem with constant promise gap can be solved in 𝖰𝖢𝖬𝖠, then 𝖰𝖯𝖢𝖯[q] is in 𝖰𝖢𝖬𝖠 for any constant q. (iii) If 𝖰𝖬𝖠(k) has a quantum PCP for any k=poly(n), then 𝖰𝖬𝖠(2) = 𝖰𝖬𝖠, connecting two of the longest-standing open problems in quantum complexity theory. Moreover, we also show that there exists (quantum) oracles relative to which certain quantum PCP statements are false. Hence, any attempt to prove the quantum PCP conjecture requires, just as was the case for the classical PCP theorem, (quantumly) non-relativizing techniques.

We explore the power of the unbounded Fan-Out gate and the Global Tunable gates generated by Ising-type Hamiltonians in constructing constant-depth quantum circuits, with particular attention to quantum memory devices. We propose two types of constant-depth constructions for implementing Uniformly Controlled Gates. These gates include the Fan-In gates defined by x>|b> —> |x>|b+ f(x)> for x in 0,1^n and b in 0,1, where f is a Boolean function. The first of our constructions is based on computing the one-hot encoding of the control register |x>, while the second is based on Boolean analysis and exploits different representations of f such as its Fourier expansion. Via these constructions, we obtain constant-depth circuits for the quantum counterparts of read-only and read-write memory devices — Quantum Random Access Memory (QRAM) and Quantum Random Access Gate (QRAG) — of memory size n. The implementation based on one-hot encoding requires either O(n log(n)łogłog(n)) ancillae and O(n log(n)) Fan-Out gates or O(n log(n)) ancillae and 6 Global Tunable gates. On the other hand, the implementation based on Boolean analysis requires only 2 Global Tunable gates at the expense of O(n^2) ancillae.

First-quantized plane-wave representations provide a very promising approach for quantum algorithms for solid state materials. Pseudopotentials provide a method of further reducing the complexity by avoiding the need to simulate highly localized core orbitals. The complicated functional form of pseudopotentials constitutes a major challenge for the design of quantum algorithms. In this work we provide new techniques to efficiently implement pseudopotentials in quantum algorithms, with orders of magnitude improvement in complexity. Our methods include a high-accuracy QROM interpolation of the exponential function, combined with QROM for the pseudopotential parameters and coherent arithmetic. Moreover, we generalize prior methods to enable the simulation of materials defined by non-cubic unit cells. Finally, we combine these techniques to estimate the resources for block encoding required for simulating commercially relevant instances of heterogeneous catalysis.

The Heisenberg limit (HL, with estimation error scales as 1/n) and the standard quantum limit (SQL, 1/sqrt(n)) are two fundamental limits in estimating an unknown parameter in n copies of quantum channels and are achievable with full quantum controls, e.g., quantum error correction (QEC). It is unknown though, whether these limits are still achievable in restricted quantum devices when QEC is unavailable, e.g., with only unitary controls or bounded system sizes. In this talk, I will discuss various new limits for estimating qubit channels under restrictive controls. The HL is proven to be unachievable in various cases, indicating the necessity of QEC in achieving the HL. Furthermore, a necessary and sufficient condition to achieve the SQL is determined, where a novel unitary control protocol is identified to achieve the SQL for certain types of noisy channels, and a constant floor on the estimation error is proven for other cases.

The preparation of thermal states of matter is a crucial task in quantum simulation. In this work, we prove that an efficiently implementable dissipative evolution recently introduced by Chen et al. thermalizes into its equilibrium Gibbs state in time scaling polynomially with system size at high enough temperatures for any Hamiltonian that satisfies a Lieb-Robinson bound, such as local Hamiltonians on a lattice. Furthermore, we show the efficient adiabatic preparation of the associated purifications or ``thermofield double" states. To the best of our knowledge, these are the first results rigorously establishing the efficient preparation of high temperature Gibbs states and their purifications. In the low-temperature regime, we show that implementing this family of Lindbladians for inverse temperatures logarithmic in the system's size is polynomially equivalent to standard quantum computation. On a technical level, for high temperatures, our proof makes use of the mapping of the generator of the evolution into a Hamiltonian and the analysis of the stability of its gap. For low temperature, we instead perform a perturbation at zero temperature of the Laplace transform of the energy observable at fixed runtime, and resort to circuit-to-Hamiltonian mappings akin to the proof of universality of quantum adiabatic computing. Taken together, our results show that the family of Lindbladians of Chen et al. efficiently prepares a large class of quantum many-body states of interest, and have the potential to mirror the success of classical Monte Carlo methods for quantum many-body systems.

Sampling from the output distributions of quantum computations comprising only commuting gates, known as instantaneous quantum polynomial (IQP) computations, is believed to be intractable for classical computers, and hence this task has become a leading candidate for testing the capabilities of quantum devices. Here we demonstrate that for an arbitrary IQP circuit undergoing dephasing or depolarizing noise, the output distribution can be efficiently sampled by a classical computer after a critical O(1) depth. Unlike other simulation algorithms for quantum supremacy tasks, we do not require assumptions on the circuit's architecture, on anti-concentration properties, nor do we require Ømega(łog(n)) circuit depth. We take advantage of the fact that IQP circuits have deep sections of diagonal gates, which allows the noise to build up predictably and induce a large-scale breakdown of entanglement within the circuit. Our results suggest that quantum supremacy experiments based on IQP circuits may be more susceptible to classical simulation than previously thought.

Harnessing quantum effects in metrology, such as entanglement and coherence, enables enhanced sensitivity in measuring parameters. Despite this, decoherence and time-dependent errors can undermine Heisenberg-limited amplification. To navigate these challenges in realistic experiments for phase estimation of a two-level unitary gate, we introduce a suite of quantum metrology algorithms. These algorithms capitalize on the universality of quantum signal transformation to decouple two interdependent phase parameters into largely orthogonal ones, ensuring that time-dependent errors in one do not compromise the accuracy of learning the other. Our approach combines provably optimal classical estimation with nearly optimal quantum circuit design, achieving unparalleled accuracy of 10−4 radians in standard deviation for estimating extremely small angles in superconducting qubit experiments with low-depth (< 10) circuits. This accuracy surpasses existing alternatives by two orders of magnitude and is adaptable to ion trap gates. We prove both theoretically and numerically the optimality of our algorithm against time-dependent phase error in φ. Remarkably, in the low circuit depth limit, our method’s estimation variance on the time-sensitive parameter φ scales faster than the asymptotic Heisenberg limit as a function of depth, Var(φˆ) ∼ 1/d4. Crucially, our method’s efficacy is rigorously affirmed through an analysis against the quantum Fisher information, a feature lacking in prior art. This analysis underpins our protocol’s ability to achieve unmatched precision, leveraging quantum resources more effectively than has been possible before.

The well-conditioned multi-product formula (MPF), proposed by [Low, Kliuchnikov, and Wiebe, 2019], is a simple high-order time-independent Hamiltonian simulation algorithm that implements a linear combination of standard product formulas of low order. While the MPF aims to simultaneously exploit commutator scaling among Hamiltonians and achieve near-optimal time and precision dependence, its lack of a rigorous error bound on the nested commutators renders its practical advantage ambiguous. In this work, we conduct a rigorous complexity analysis of the well-conditioned MPF, demonstrating explicit commutator scaling and near-optimal time and precision dependence at the same time. Using our improved complexity analysis, we present several applications of practical interest where the MPF based on a second-order product formula can achieve a polynomial speedup in both system size and evolution time, as well as an exponential speedup in precision, compared to second-order and even higher-order product formulas. Compared to post-Trotter methods, the MPF based on a second-order product formula can achieve polynomially better scaling in system size, with only poly-logarithmic overhead in evolution time and precision.

The quantum analogue of the equality function, known as the quantum state identity problem, is the task of deciding whether n unknown quantum states are equal or unequal, given the promise that all states are either pairwise orthogonal or identical. Under the one-sided error requirement, it is known that the permutation test is optimal for this task, and for two input states this coincides with the well-known Swap test. Until now, the optimal measurement in the general two-sided error regime was unknown. Under more specific promises, the problem can be solved approximately or even optimally with simpler tests, such as the circle test. This work attempts to capture the underlying structure of (fine-grained formulations of) the quantum state identity problem. Using tools from semi-definite programming and representation theory, we (i) give an optimal test for any input distribution without the one-sided error requirement by writing the problem as an SDP, giving the exact solutions to the primal and dual programs and showing that the two values coincide; (ii) propose a general G-test which uses an arbitrary subgroup G of S_n, giving an analytic expression of the performance of the specific test, and (iii) give an approximation of the permutation test using only a classical permutation and n−1 Swap tests.

Shallow-depth quantum circuits with gates of bounded fan-in have been demonstrated to achieve computational advantages over shallow-depth classical circuits, even allowing for unbounded fan-in (AC0). Despite their versatility, these computational models are known to be less powerful than Polynomial Threshold Function (PTF) circuits, which serve as a natural model for neural networks and exhibit enhanced expressivity and computational capabilities. We prove that PTF circuits with a constant number of layers, when biased (having the activation region of their gates limited), fail to solve certain computational (relational) problems that quantum circuits of constant depth can solve. Furthermore, we prove such a separation for a family of problems, one for each prime qudit dimension. We prove all of these separations via correlation bounds for average-case hardness. We also establish a tight lower bound on the size of biased PTF circuits that can solve a specific relational problem *exactly*. This allows us to significantly reduce the estimated resource requirements for potential demonstrations of quantum advantage. The main challenges in this area of research arise in establishing the classical lower bounds, and in designing non-local games with quantum-classical gaps in the winning strategy in order to go beyond qubits to higher dimensions. To address the former, we have formulated novel switching lemmas specifically designed for multi-output biased PTF circuits, and have developed a way to assess the difficulty of deriving exact solutions. Our contribution towards the latter is grounded in a novel assortment of non-local games, characterized by an exponential difference between their classical and quantum success probabilities. Finally, our technical developments could be of more general and independent interest.

We show an area law in the mutual information for the maximally-mixed state Ω in the ground space of general Hamiltonians, which is independent of the underlying ground space degeneracy. Our result assumes the existence of a `good' approximation to the ground state projector (a good AGSP), a crucial ingredient in former area-law proofs. Such approximations have been explicitly derived for 1D gapped local Hamiltonians and 2D frustration-free and locally-gapped local Hamiltonians. As a corollary, we show that in 1D gapped local Hamiltonians, for any eps>0 and any bi-partition Lcup L^c of the system, beginalign* I^eps_max(L:L^c)_Ømega łe bigO łog (|L|) + łog(1/eps), endalign* where |L| represents the number of sites in L and I^eps_max(L:L^c)_Ømega represents the eps-emphsmoothed maximum mutual information with respect to the L:L^c partition in Ω. From this bound we then conclude I(L:L^c)_Ømega łe bigOłog(|L|) – an area law for the mutual information in 1D systems with a logarithmic correction. In addition, we show that Ω can be approximated up to an eps in trace norm with a state of Schmidt rank of at most poly(|L|/eps). Similar corollaries are derived for the mutual information of 2D frustration-free locally-gapped local Hamiltonians.

The ability of quantum computers to directly manipulate and analyze quantum states stored in quantum memory allows them to learn about aspects of our physical world that would otherwise be invisible given a modest number of measurements. Here we investigate a new learning resource which could be available to quantum computers in the future – measurements on the unknown state accompanied by its complex conjugate ρ⊗ρ*. For a certain shadow tomography task, we surprisingly find that measurements on only copies of ρ⊗ρ* can be exponentially more powerful than measurements on ρ⊗K, even for large K. This expands the class of exponential advantages using only a constant overhead quantum memory, or minimal quantum memory, and we provide a number of examples where the state ρ* is naturally available in both computational and physical applications. In addition, we precisely quantify the power of classical shadows on single copies under a generalized Clifford ensemble and give a class of quantities that can be efficiently learned. The learning task we study in both the single copy and quantum memory is physically natural and corresponds to real-space observables with a limit of bosonic modes, where it achieves an exponential improvement in detecting certain signals under a noisy background. In addition to quantifying a fundamentally new and powerful resource in quantum learning, we believe the advantage may find applications in improving quantum simulation, learning from quantum sensors, and uncovering new physical phenomena.

A recent line of work has shown the unconditional advantage of constant-depth quantum computation, or QNC0, over NC0, AC0, and related models of classical computation. Problems exhibiting this advantage include search and sampling tasks related to the parity function, and it is natural to ask whether QNC0 can be used to help compute parity itself. Namely, we study AC0◦QNC0—a hybrid circuit model where AC0 operates on measurement outcomes of a QNC0 circuit—and we ask whether Par ∈ AC0◦QNC0. We believe the answer is negative. In fact, we conjecture AC0◦QNC0 cannot even achieve Ω(1) correlation with parity. As evidence for this conjecture, we prove the following: • When the QNC0 circuit is ancilla-free, we show this model can achieve only negligible correlation with parity, even when AC0 is replaced with any function having LMN-like decay in its Fourier spectrum. • For the general (non-ancilla-free) case, we show via a connection to nonlocal games that the conjecture holds for any class of postprocessing functions that has approximate degree o(n) and is closed under restrictions. Moreover, this is true even when the QNC0 circuit is given arbitrary quantum advice. By known results, this confirms the conjecture for linear-size AC0 circuits. • Another approach to proving the conjecture is to show a switching lemma for AC0◦QNC0. Towards this goal, we study the effect of quantum preprocessing on the decision tree complexity of Boolean functions. We find that from the point of view of a decision tree, nonlocal channels are no better than randomness: a Boolean function f precomposed with an n-party nonlocal channel is together equal to a randomized decision tree with depth no greater than DTdepth[f]. Taken together, our results suggest that while QNC0 is surprisingly powerful for search and sampling, that power is “locked away” in the global correlations of its output, inaccessible to simple classical computation for solving decision problems.

I'll introduce a framework for constructing quantum codes defined on spheres, taking inspiration from classical spherical codes. The framework is applied to bosonic coding, obtaining multimode extensions of the cat codes that can outperform previous constructions while requiring a similar type of overhead. Our polytope-based cat codes consist of sets of points with large separation that at the same time form averaging sets known as spherical designs, a property sufficient to guarantee protection against photon losses.

The Esscher Transform is a tool of broad utility in various domains of applied probability. It provides the solution to a constrained minimum relative entropy optimization problem. In this work, we study the generalization of the Esscher Transform to the quantum setting. We examine a relative entropy minimization problem for a quantum density operator, potentially of wide relevance in quantum information theory. The resulting solution form motivates us to define the textitquantum Esscher Transform, which subsumes the classical Esscher Transform as a special case. Envisioning potential applications of the quantum Esscher Transform, we also discuss its implementation on fault-tolerant quantum computers. Our algorithm is based on the modern techniques of block-encoding and quantum singular value transformation (QSVT). We show that given block-encoded inputs, our algorithm outputs a subnormalized block-encoding of the quantum Esscher transform within accuracy ε in tilde O(kappa d łog^2 1/epsilon) queries to the inputs, where κ is the condition number of the input density operator and d is the number of constraints.

Given a linear system of equations Ax = b, quantum linear system solvers (QLSSs) approximately prepare a quantum state |x⟩ for which the amplitudes are proportional to the solution vector x. Asymptotically optimal QLSSs have query complexity O(κlog(1/ε)), where κ is the condition number of A, and ε is the approximation error. However, runtime guarantees for existing optimal and near-optimal QLSSs do not have favorable constant factors, in part because they rely on complex or difficult-to-analyze techniques like variable-time amplitude amplification and adiabatic path-following. Here, we give a conceptually simple, near-optimal QLSS that does not use these techniques. If the solution norm ∥x∥∥A∥/∥b∥ is known exactly, our QLSS requires only a single application of kernel reflection (an extension of eigenstate filtering), and has query complexity (1 + O(ε))κ ln(2√2/ε). If the norm is not known, it can be estimated up to a constant factor using O(log log(κ)) applications of kernel projection (a slight generalization of eigenstate filtering), yielding a QLSS with near-optimal total complexity O(κ log log(κ) log log log(κ) + κ log(1/ε)). Preliminary constant-factor analysis suggests that for practical values of κ, ε our QLSS provides rigorous guarantees that are at least an order of magnitude better than previous guarantees.

We propose a family of explicit geometrically local circuits realizing any abelian non-chiral topological phase as an actively error-corrected fault-tolerant memory. These circuits are constructed from measuring 1-form symmetries in discrete fixed-point path integrals, which we express through cellular cohomology and higher-order cup products. The specific path integral we use is the abelian Dijkgraaf-Witten state sum on a 3-dimensional cellulation, which is a spacetime representation of the twisted quantum double model. The resulting circuits are based on a syndrome extraction circuit of the (qudit) stabilizer toric code, into which we insert non-Clifford phase gates that implement the ``twist''. The overhead compared to the toric code is moderate, in contrast to known constructions for twisted abelian phases. The simplest non-trivial example is a fault-tolerant circuit for the double-semion phase, defined on the same set of qubits as the stabilizer toric code, with 12 controlled-S gates in addition to the 8 controlled-X gates and 2 single-qubit measurements of the toric code per spacetime unit cell. We also show that other architectures for the (qudit) toric code phase, like measurement-based topological quantum computation or Floquet codes, can be enriched with phase gates to implement twisted quantum doubles instead of their untwisted versions. As a further result, we prove fault tolerance under arbitrary local (including non-Pauli) noise for a very general class of topological circuits that we call 1-form symmetric fixed-point circuits. This notion unifies the circuits in this paper as well as the stabilizer toric code, subsystem toric code, measurement-based topological quantum computation, or the (CSS) honeycomb Floquet code. We also demonstrate how our method can be adapted to construct fault-tolerant circuits for specific non-Abelian phases.

We demonstrate quantum advantage with several basic assumptions, specifically based on only the existence of OWFs. We introduce inefficient-verifier proofs of quantumness (IV-PoQ), and construct it from classical bit commitments. IV-PoQ is an interactive protocol between a verifier and a quantum prover consisting of two phases. In the first phase, the verifier is probabilistic polynomial-time, and it interacts with the prover. In the second phase, the verifier becomes inefficient, and makes its decision based on the transcript of the first phase. If the prover is honest, the inefficient verifier accepts with high probability, but any classical malicious prover only has a small probability of being accepted by the inefficient verifier. Our construction demonstrates the following results: (1)If one-way functions exist, then IV-PoQ exist. (2)If distributional collision-resistant hash functions exist (which exist if hard-on-average problems in SZK exist), then constant-round IV-PoQ exist. We also demonstrate quantum advantage based on worst-case-hard assumptions. We define auxiliary-input IV-PoQ (AI-IV-PoQ) that only require that for any malicious prover, there exist infinitely many auxiliary inputs under which the prover cannot cheat. We construct AI-IV-PoQ from an auxiliary-input version of commitments in a similar way, showing that (1)If auxiliary-input one-way functions exist (which exist if CZK⊈BPP), then AI-IV-PoQ exist. (2)If auxiliary-input collision-resistant hash functions exist (which is equivalent to PWPP⊈FBPP) or SZK⊈BPP, then constant-round AI-IV-PoQ exist.

We develop quantum information processing primitives for the planar rotor, the state space of a particle on a circle. The n-rotor Clifford group, U(1)n(n+1)/2 ⋊ GLn(Z), is represented by continuous U(1) gates generated by polynomials quadratic in angular momenta, as well as discrete GLn(Z) gates generated by momentum sign-flip and sum gates. Understandings in rotor Clifford group allow us to establish connections between homological rotor error-correcting codes and oscillator quantum codes, including Gottesman-Kitaev-Preskill codes and rotation-symmetric bosonic codes. Inspired by homological rotor codes, we provide a systematic construction of multi-mode rotation-symmetric bosonic codes by analoging Fock states to rotor states with non-negative angular momentum. This new family of multi-mode bosonic codes protect against dephasing and changes in occupation numbers, which we call homological number-phase codes. Their encoding and decoding circuits are readily derived from the corresponding rotor Clifford operations. In particular, we show how to non-destructively measure the oscillator phase using conditional occupation-number addition and post-selection. We also outline several rotor and oscillator varieties of the GKP-stabilizer codes. References: arXiv:2311.07679, homological rotor error-correcting codes (arXiv:2303.13723), GKP-stabilizer codes (arXiv:1903.12615)

We give an oracle separation between QMA and QCMA for quantum algorithms that have bounded adaptivity in their oracle queries; that is, the number of rounds of oracle calls is small, though each round may involve polynomially many queries in parallel. Our oracle construction is a simplified version of the construction used recently by Li, Liu, Pelecanos, and Yamakawa (2023), who showed an oracle separation between QMA and QCMA when the quantum algorithms are only allowed to access the oracle classically. To prove our results, we introduce a property of relations called slipperiness, which may be useful for getting a fully general classical oracle separation between QMA and QCMA.

We consider the preparation of matrix product states (MPS) on quantum devices via quantum circuits of local gates. We first prove that faithfully preparing translation-invariant normal (i.e., short-range correlated) MPS of N sites requires a circuit depth T = Ω(log N). We then introduce an algorithm based on the renormalization-group transformation to prepare normal MPS with an error ε in depth T = O[log(N/ε)]. The algorithm has thus the fastest scaling possible for this class of states. We also show that measurement and feedback leads to an exponential speedup of the algorithm to T = O[log log(N/ε)]. Measurements also allow one to prepare arbitrary translation-invariant MPS, including long-range non-normal ones, in the same depth. Finally, the algorithm naturally extends to inhomogeneous MPS.

We make progress in understanding the relationship between learning models with access to entangled, separable and statistical measurements in the quantum statistical query (QSQ) model. We show the following results. Entangled versus separable measurements: The goal is to learn an unknown f from the concept class C containing functions from 0,1^n to [k] given copies of a uniform superposition over |x,f(x)>. We show that, if T copies suffice to learn f using entangled measurements, O(nT^2) copies suffice to learn f using only separable measurements. Entangled versus statistical measurements: The goal is to learn a function f in C given access to separable measurements or statistical measurements. We exhibit a concept class C based of degree-2 functions with exponential separation between QSQ learning and quantum learning with entangled measurements (even in the presence of noise). This proves the ""quantum analogue"" of the seminal result of Blum et al. that separates classical SQ learning from classical PAC learning with classification noise. QSQ lower bounds for learning states: We introduce a quantum statistical query dimension (QSD), and use it to give lower bounds on the QSQ complexity of learning. We prove superpolynomial QSQ lower bounds for testing purity of quantum states, shadow tomography, learning coset states for the Abelian hidden subgroup problem, degree-2 functions, planted biclique states, and learning output states of Clifford circuits of depth polylog(n). We also show that an extension of QSD characterizes the complexity of general search problems. Further applications: We give an unconditional separation between weak and strong error mitigation and prove lower bounds for learning distributions in the QSQ model. Prior works by Quek et al., Hinsche et al., and Nietner et al. proved analogous results assuming diagonal measurements and our work removes this assumption.

The recent MIP*=RE theorem of Ji, Natarajan, Vidick, Wright, and Yuen shows that the complexity class MIP* of multiprover proof systems with entangled provers contains all recursively enumerable languages. Prior work of Grilo, Slofstra, and Yuen [FOCS '19] further shows (via a technique called simulatable codes) that every language in MIP* has a perfect zero knowledge (PZK) MIP* protocol. The MIP*=RE theorem uses two-prover one-round proof systems, and hence such systems are complete for MIP*. However, the construction in Grilo, Slofstra, and Yuen uses six provers, and there is no obvious way to get perfect zero knowledge with two provers via simulatable codes. This leads to a natural question: are there two-prover PZK-MIP* protocols for all of MIP*? In this paper, we show that every language in MIP* has a two-prover one-round PZK-MIP* protocol, answering the question in the affirmative. For the proof, we use a new method based on a key consequence of the MIP*=RE theorem, which is that every MIP* protocol can be turned into a family of boolean constraint system (BCS) nonlocal games. This makes it possible to work with MIP* protocols as boolean constraint systems, and in particular allows us to use a variant of a construction due to Dwork, Feige, Kilian, Naor, and Safra [Crypto '92] which gives a classical MIP protocol for 3SAT with perfect zero knowledge. To show quantum soundness of this classical construction, we develop a toolkit for analyzing quantum soundness of reductions between BCS games, which we expect to be useful more broadly. This toolkit also applies to commuting operator strategies, and our argument shows that every language with a commuting operator BCS protocol has a two prover PZK commuting operator protocol.

Quantum error mitigation is a key approach for extracting target state properties on state-of-the-art noisy machines and early fault-tolerant devices. Using the ideas from flag fault tolerance and virtual state purification, we develop the virtual channel purification (VCP) protocol, which consumes similar qubit and gate resources as virtual state purification but offers up to exponentially stronger error suppression with increased system size and more noisy operation copies. Furthermore, VCP removes most of the assumptions required in virtual state purification. Essentially, VCP is the first quantum error mitigation protocol that does not require specific knowledge about the noise models, the target quantum state, and the target problem while still offering rigorous performance guarantees for practical noise regimes. Further connections are made between VCP and quantum error correction to produce one of the first protocols that combine quantum error correction and quantum error mitigation beyond concatenation. We can remove all noise in the channel while paying only the same sampling cost as low-order purification, reaching beyond the standard bias-variance trade-off in quantum error mitigation. Our protocol can also be adapted to key tasks in quantum networks like channel capacity activation and entanglement distribution.

Quantum state purification is the functionality that, given multiple copies of an unknown state, outputs a state with increased purity. This is an essential building block for the near- and middle-term quantum ecosystems before the availability of full fault tolerance, where one may want to obtain purified quantum states instead of expectation values. We propose an effective state purification gadget with a moderate quantum overhead by projecting multiple noisy quantum inputs to their symmetry subspace defined by a set of projectors forming a subgroup of the symmetry group. This provides a state purification performance scaling inverse-linearly to the number of state copies given a fixed stochastic error rate, which drastically improves the implementation overhead in previous works. Our method may find its application in designing robust verification protocols for quantum outputs before the availability of fully fault-tolerant computing.

We prove that any two-party correlation in the commuting operator model can be approximated using a tracial embeddable strategy, a class of strategy defined on a finite tracial von Neumann algebra, which we define in this paper. Using this characterization, we show that any approximately synchronous correlation can be approximated to the average of a collection of synchronous correlations in the commuting operator model. This generalizes the result from Vidick [JMP 2022], which only applies to finite-dimensional quantum correlations. Furthermore, we extend the state-dependent norm variant of the Gowers-Hatami theorem to finite von Neumann algebras. Combined with the aforementioned characterization, this enables us to lift many known results about robust self-testing for non-local games to the commuting operator model, including a sample efficient EPR testing for the commuting operator strategies. We believe that, in addition to the contribution from this paper, this class of strategies can be helpful for further understanding non-local games in the infinite-dimensional setting.

We present a computational problem with the following properties: (i) Every instance can be solved with near-certainty by a constant-depth quantum circuit using only nearest-neighbor gates in 3D even when its implementation is corrupted by noise. (ii) Any constant-depth classical circuit composed of unbounded fan-in AND, OR, as well as NOT gates, i.e., an AC0-circuit, of size smaller than a certain subexponential, fails to solve a uniformly random instance with probability greater than a certain constant. Such an advantage against unbounded fan-in classical circuits was previously only known in the noise-free case or without locality constraints. We overcome these limitations, proposing a quantum advantage demonstration amenable to experimental realizations. Subexponential circuit-complexity lower bounds have traditionally been referred to as exponential. We use the term colossal since our fault-tolerant 3D architecture resembles a certain Roman monument.

We study the complexity of a classic problem in computational topology, the homology problem: given a description of some space X and an integer k, decide if X contains a k-dimensional hole. The setting and statement of the homology problem are completely classical, yet we find that the complexity is characterized by quantum complexity classes. Our result can be seen as an aspect of a connection between homology and supersymmetric quantum mechanics [Wit82]. We consider clique complexes, motivated by the practical application of topological data analysis (TDA). The clique complex of a graph is the simplicial complex formed by declaring every k+1-clique in the graph to be a k-simplex. Our main result is that deciding whether the clique complex of a weighted graph has a hole or not, given a suitable promise on the gap, is QMA1-hard and contained in QMA. Our main innovation is a technique to lower bound the eigenvalues of the combinatorial Laplacian operator. For this, we invoke a tool from algebraic topology known as spectral sequences. In particular, we exploit a connection between spectral sequences and Hodge theory [For94]. Spectral sequences will play a role analogous to perturbation theory for combinatorial Laplacians. In addition, we develop the simplicial surgery technique used in prior work [CK22]. Our result provides some suggestion that the quantum TDA algorithm [LGZ16] cannot be dequantized. More broadly, we hope that our results will open up new possibilities for quantum advantage in topological data analysis.

Motivated by realistic hardware considerations of the pre-fault-tolerant era, we comprehensively study the impact of uncorrected noise on quantum circuits. We first show that any noise `truncates' most quantum circuits to effectively logarithmic depth, in the task of computing Pauli expectation values. We then prove that quantum circuits under any non-unital noise exhibit lack of barren plateaus for cost functions composed of local observables. But, by leveraging the effective shallowness, we also design a classical algorithm to estimate Pauli expectation values within inverse-polynomial additive error with high probability over the ensemble. Its runtime is independent of circuit depth and it operates in polynomial time in the number of qubits for one-dimensional architectures and quasi-polynomial time for higher-dimensional ones. Taken together, our results showcase that, unless we carefully engineer the circuits to take advantage of the noise, it is unlikely that noisy quantum circuits are preferable over shallow quantum circuits for algorithms that output Pauli expectation value estimates, like many variational quantum machine learning proposals. Moreover, we anticipate that our work could provide valuable insights into the fundamental open question about the complexity of sampling from (possibly non-unital) noisy random circuits.

We show that any pseudoentangled state ensemble with a gap of t bits of entropy requires Ω(t) non-Clifford gates to prepare. This bound is tight up to polylogarithmic factors if linear-time quantum-secure pseudorandom functions exist. Our result follows from a polynomial-time algorithm to estimate the entanglement entropy of a quantum state across any cut of qubits. When run on an n-qubit state that is stabilized by at least 2^n−t Pauli operators, our algorithm produces an estimate that is within an additive factor of t/2 bits of the true entanglement entropy.

Despite the fundamental role the Quantum Satisfiability (QSAT) problem has played in quantum complexity theory, a central question remains open: At which local dimension does the complexity of QSAT transition from ""easy"" to ""hard""? Here, we study QSAT with each constraint acting on a k-dimensional and l-dimensional qudit pair, denoted (k,l)-QSAT. Our first main result shows that, surprisingly, QSAT on qubits can remain QMA_1-hard, in that (2,5)-QSAT is QMA_1-complete. (QMA_1 is a quantum analogue of MA with perfect completeness.) In contrast, (2,2)-QSAT (i.e. Quantum 2-SAT on qubits) is well-known to be poly-time solvable [Bravyi, 2006]. Our second main result proves that (3,d)-QSAT on the 1D line with d = O(1) is also QMA_1-hard. Finally, we initiate the study of (2,d)-QSAT on the 1D line by giving a frustration-free 1D Hamiltonian with a unique, entangled ground state. As implied by our title, our first result uses a direct embedding: We combine a novel clock construction with the 2D circuit-to-Hamiltonian construction of [Gosset and Nagaj, 2013]. Of note is a new simplified and analytic proof for the latter (as opposed to a partially numeric proof in [GN13]). This exploits Unitary Labelled Graphs [Bausch, Cubitt, Ozols, 2017] together with a new ""Nullspace Connection Lemma"", allowing us to break low energy analyses into small patches of projectors, and to improve the soundness analysis of [GN13] from Omega(1/T^6) to Omega(1/T^2), for T the number of gates. Our second result goes via black-box reduction: Given an arbitrary 1D Hamiltonian H on d'-dimensional qudits, we show how to embed it into an effective 1D (3,d)-QSAT instance, for d = O(1). Our approach may be viewed as a weaker notion of ""simulation"" (à la [Bravyi, Hastings 2017], [Cubitt, Montanaro, Piddock 2018]). As far as we are aware, this gives the first ""black-box simulation""-based QMA_1-hardness result.

Nonlocal games are a foundational tool for understanding entanglement and constructing quantum protocols in settings with multiple spatially separated quantum devices. In this work, we continue the study initiated by Kalai et al. (STOC '23) of compiled nonlocal games, played between a classical verifier and a single cryptographically limited quantum device. Our main result is that the compiler proposed by Kalai et al. is sound for any two-player XOR game. A celebrated theorem of Tsirelson shows that for XOR games, the quantum value is exactly given by a semidefinite program, and we obtain our result by showing that the SDP upper bound holds for the compiled game up to a negligible error arising from the compilation. This answers a question raised by Natarajan and Zhang (FOCS '23), who showed soundness for the specific case of the CHSH game. Using our techniques, we obtain several additional results, including (1) tight bounds on the compiled value of parallel-repeated XOR games, (2) operator self-testing statements for any compiled XOR game, and (3) a ``nice" sum-of-squares certificate for any XOR game, from which operator rigidity is manifest.

Checking whether two quantum circuits are approximately equivalent is a common task in quantum computing. We consider a closely related identity check problem: given a quantum circuit U, one has to estimate the diamond-norm distance between U and the identity channel. We present a classical algorithm approximating the distance to the identity within a factor alpha = D+1 for shallow geometrically local D-dimensional circuits provided that the circuit is sufficiently close to the identity. The runtime of the algorithm scales linearly with the number of qubits for any constant circuit depth and spatial dimension. We also show that the operator-norm distance to the identity || U - I || can be efficiently approximated within a factor alpha = 5 for shallow 1D circuits and, under a certain technical condition, within a factor alpha = 2D + 3 for shallow D-dimensional circuits. A numerical implementation of the identity check algorithm is reported for 1D Trotter circuits with up to 100 qubits.

The theory of Total Function NP (TFNP) and its subclasses says that, even if one is promised an efficiently verifiable proof exists for a problem, finding this proof can be intractable. Despite being a classical complexity class, however, TFNP has made a surprise appearance in the study of Quantum Satisfiability (QSAT): If a QSAT instance has a System of Distinct Representatives (SDR), then it has a product-state solution [Laumann, Läuchli, Moessner, Scardicchio, and Sondhi 2010]. Efficiently finding this product-state solution, however, has remained elusive. In this work, we introduce a new framework based on Weighted SDRs (WSDR), which among other results, allows us to: (1) significantly simplify and extend the results of [LLMSS 2010] to qudit systems, (2) establish a connection to the Bézout number for multihomogeneous polynomial systems, and (3) apply the parameterized algorithm of [Aldi, de Beaudrap, Gharibian, Saeedi 2021] to solve new instances of QSAT efficiently on qudits. The second of these, in particular, allows us to define the first ""quantum-inspired"" subclass of TFNP, for which we show QSAT with SDR is complete. Thus, we obtain the first evidence that QSAT with SDR is, in fact, intractable.

Quantum nondeterministic distributed computing was recently introduced as dQMA (distributed quantum Merlin-Arthur) protocols by Fraigniaud, Le Gall, Nishimura and Paz (ITCS 2021). In dQMA protocols, with the help of quantum proofs and local communication, nodes on a network verify a global property of the network. Fraigniaud et al. showed that, when the network size is small, there exists an exponential separation in proof size between distributed classical and quantum verification protocols, for the equality problem, where the verifiers check if all the data owned by a subset of them are identical. In this paper, we further investigate and characterize the power of the dQMA protocols for various decision problems. First, we give a more efficient dQMA protocol for the equality problem with a simpler analysis. This is done by adding a symmetrization step on each node and exploiting properties of the permutation test, which is a generalization of the SWAP test. We also show a quantum advantage for the equality problem on path networks still persists even when the network size is large, by considering ``relay points'' between extreme nodes. Second, we show that even in a general network, there exist efficient dQMA protocols for the ranking verification problem, the Hamming distance problem, and more problems that derive from efficient quantum one-way communication protocols. Third, in a line network, we construct an efficient dQMA protocol for a problem that has an efficient two-party QMA communication protocol. Finally, we obtain the first lower bounds on the proof and communication cost of dQMA protocols. To prove a lower bound on the equality problem, we show any dQMA protocol with an entangled proof between nodes can be simulated with a dQMA protocol with a separable proof between nodes by using a QMA communication-complete problem introduced by Raz and Shpilka (CCC 2004).

Quantum data access and quantum processing can make certain classically intractable learning tasks feasible. However, quantum capabilities will only be available to a select few in the near future. Thus, reliable schemes that allow classical clients to delegate learning to untrusted quantum servers are required to facilitate widespread access to quantum learning advantages. Building on a recently introduced framework of interactive proof systems for classical machine learning by Goldwasser et al. (ITCS 2021), we develop a framework for classical verification of quantum learning. We exhibit learning problems that a classical learner cannot efficiently solve on their own, but that they can efficiently and reliably solve when interacting with an untrusted quantum prover. Concretely, we consider the problems of agnostic learning parities and Fourier-sparse functions with respect to distributions with uniform input marginal. We propose a new quantum data access model that we call "mixture-of-superpositions" quantum examples, based on which we give efficient quantum learning algorithms for these tasks. Moreover, we prove that agnostic quantum parity and Fourier-sparse learning can be efficiently verified by a classical verifier with only random example or statistical query access. Finally, we showcase two general scenarios in learning and verification in which quantum mixture-of-superpositions examples do not lead to sample complexity improvements over classical data. Our results demonstrate that the potential power of quantum data for learning tasks, while not unlimited, can be utilized by classical agents through interaction with untrusted quantum entities.

Decoders that provide an estimate of the probability of a logical failure conditioned on the error syndrome (``soft-output decoders'') can reduce the overhead cost of fault-tolerant quantum memory and computation. In this work, we construct efficient soft-output decoders for the surface code derived from the Minimum-Weight Perfect Matching and Union-Find decoders. We show that soft-output decoding can improve the performance of a ``hierarchical code,'' a concatenated scheme in which the inner code is the surface code, and the outer code is a high-rate quantum low-density parity-check code. Alternatively, the soft-output decoding can improve the reliability of fault-tolerant circuit sampling by flagging those runs that should be discarded because the probability of a logical error is intolerably large.

Pseudorandom quantum states (PRSs) and pseudorandom unitaries (PRUs) possess the dual nature of being efficiently constructible while appearing completely random to any efficient quantum algorithm. In this study, we establish fundamental bounds on pseudorandomness. We show that PRSs and PRUs exist only when the probability that an error occurs is negligible, ruling out their generation on noisy intermediate-scale and early fault-tolerant quantum computers. Further, we show that PRUs need imaginarity while PRS do not have this restriction. This implies that quantum randomness requires in general a complex-valued formalism of quantum mechanics, while for random quantum states real numbers suffice. Additionally, we derive lower bounds on the coherence of PRSs and PRUs, ruling out the existence of sparse PRUs and PRSs. We also show that the notions of PRS, PRUs and pseudorandom scramblers (PRSSs) are distinct in terms of resource requirements. We introduce the concept of pseudoresources, where states which contain a low amount of a given resource masquerade as high-resource states. We define pseudocoherence, pseudopurity and pseudoimaginarity, and identify three distinct types of pseudoresources in terms of their masquerading capabilities. Our work also establishes rigorous bounds on the efficiency of property testing, demonstrating the exponential complexity in distinguishing real quantum states from imaginary ones, in contrast to the efficient measurability of unitary imaginarity. Lastly, we show that the transformation from a complex to a real model of quantum computation is inefficient, in contrast to the reverse process, which is efficient. Our results establish fundamental limits on property testing and provide valuable insights into quantum pseudorandomness.

We consider random quantum circuits (RQC) on arbitrary connected graphs whose edges determine the allowed 2-qudit interactions. Prior work has established that such n-qudit circuits with local dimension q on 1D, complete, and D-dimensional graphs form approximate unitary designs, that is, they generate unitaries from distributions close to the Haar measure on the unitary group U(q^n) after polynomially many gates. Here, we extend those results by proving that RQCs comprised of O(poly(n,k)) gates on a wide class of graphs form approximate unitary k-designs. We prove that RQCs on graphs with spanning trees of bounded degree and height form k-designs after O(|E|n rm poly(k)) gates, where |E| is the number of edges in the graph. Furthermore, we identify larger classes of graphs for which RQCs generate approximate designs in polynomial circuit size. For k łeq 4, we show that RQCs on graphs of certain maximum degrees form designs after O(|E|n) gates, providing explicit constants. We determine our circuit size bounds from the spectral gaps of local Hamiltonians. To that end, we extend the finite-size (or Knabe) method for bounding gaps of frustration-free Hamiltonians on regular graphs to arbitrary connected graphs. We further introduce a new method based on the Detectability Lemma for determining the spectral gaps of Hamiltonians on arbitrary graphs. Our methods have wider applicability as the first method provides a succinct alternative proof of [Commun. Math. Phys. 291, 257 (2009)] and the second method proves that RQCs on any connected architecture form approximate designs in quasi-polynomial circuit size.

Can an adversary compromise the security of our system by obtaining information on sensitive data such as cryptographic keys through side-channels? Even worse, can an adversary hack into our computer and simply steal them? This question is almost as old as the Internet and significant effort has been spent on designing mechanisms to prevent and detect such attacks. Once quantum computers arrive, will the situation remain the same or can we hope to live in a better world? We first consider ubiquitous side-channel attacks, which aim to leak side information on secret system components, studied in the leakage-resilient cryptography literature. Classical leakage-resilient cryptography must necessarily impose restrictions on the type of leakage one aims to protect against. As a notable example, the most well-studied leakage model is that of bounded leakage, where it is assumed that an adversary learns at most L bits of leakage on secret components, for some leakage bound L. Although this leakage bound is necessary, many real-world side-channel attacks cannot be captured by bounded leakage. In this work, we design cryptographic schemes that provide guarantees against arbitrary side channel attacks: - Using techniques from unclonable quantum cryptography, we design several basic leakage- resilient primitives, such as public- and private-key encryption, (weak) pseudorandom functions, digital signatures and quantum money schemes which remain secure under (polynomially) unbounded classical leakage. In particular, this leakage can be much longer than the (quantum) secret being leaked upon. In our view, leakage is the result of observations of quantities such as power consumption and hence is most naturally viewed as classical information. Notably, the leakage-resilience of our schemes holds even in the stronger “LOCC leakage” model where the adversary can obtain adaptive leakage for unbounded number of rounds. - What if the adversary simply breaks into our system to steal our secret keys, rather than mounting only a side-channel attack?What if the adversary can even tamper with the data arbitrarily, for example to cover its tracks? We initiate the study of intrusion detection in the quantum setting, where one would like to detect if security has been compromised even in the face of an arbitrary intruder attack which can leak and tamper with classical as well as quantum data. We design cryptographic schemes supporting intrusion-detection for a host of primitives such as public- and private-key encryption, digital signature, functional encryption, program obfuscation and software protection. Our schemes are based on techniques from cryptography with secure key leasing and certified deletion.

Quantum optical systems are typically affected by two types of noise: photon loss and dephasing. Despite extensive research on each noise process individually, a comprehensive understanding of their combined effect is still lacking. A crucial problem lies in determining the values of loss and dephasing for which the resulting loss-dephasing channel is anti-degradable, implying the absence of codes capable of correcting its effect or, alternatively, capable of enabling quantum communication. A conjecture in [Quantum 6, 821 (2022)] suggested that the bosonic loss-dephasing channel is not anti-degradable if the loss is below 50%. In this paper we refute this conjecture, specifically proving that for any value of the loss, if the dephasing is above a critical value, then the bosonic loss-dephasing channel is anti-degradable. While our result identifies a large parameter region where quantum communication is not possible, we also prove that if two-way classical communication is available, then quantum communication — and thus quantum key distribution — is always achievable, even for high values of loss and dephasing.

Magic states, pivotal for universal quantum computation via classically simulable Clifford gates, often undergo decomposition into resourceless stabilizer states, facilitating simulation through classical means. This approach yields three operationally significant metrics: stabilizer rank, fidelity, and extent. We extend these simulation methods to encompass matchgate circuits (MGCs), and define equivalent metrics for this setting. We begin with an investigation into the algebraic constraints defining Gaussian states, marking the first explicit characterisation of these states. The explicit description of Gaussian states is pivotal to our methods for tackling all the simulation tasks. Central to our inquiry is the concept of Gaussian rank – a pivotal metric defining the minimum terms required for decomposing a quantum state into Gaussian constituents. This metric holds paramount significance in determining the runtime of rank-based simulations for MGCs featuring magic state inputs. The absence of low-rank decompositions presents a computational hurdle, thereby prompting a deeper examination of fermionic magic states. We find that the Gaussian rank of 2 instances of our canonical magic state is 4 under symmetry-restricted decompositions. Additionally, our numerical analysis suggests the absence of low-rank decompositions for 2 or 3 copies of this magic state. Further, we explore the Gaussian extent, a convex metric offering an upper bound on the rank. We prove the Gaussian extent's multiplicative behaviour on 4-qubit systems, along with initial strides towards proving its sub-multiplicative nature in general settings. One important result in that direction we present is an upper bound on the Gaussian fidelity of generic states.

We give an almost complete characterization of the hardness of c-coloring χ-chromatic graphs with distributed algorithms, for a wide range of models of distributed computing. In particular, we show that these problems do not admit any distributed quantum advantage. To do that: beginenumerate ıtem We give a new distributed algorithm that finds a c-coloring in χ-chromatic graphs in tildemathcalO(n^frac1alpha) rounds, with alpha = biglłfloorfracc-1chi - 1bigrrfloor. ıtem We prove that any distributed algorithm for this problem requires Ømega(n^frac1alpha) rounds. endenumerate Our upper bound holds in the classical, deterministic mathsfLOCAL model, while the near-matching lower bound holds in the emphnon-signaling model. This model, introduced by Arfaoui and Fraigniaud in 2014, captures all models of distributed graph algorithms that obey physical causality; this includes not only classical deterministic mathsfLOCAL and randomized mathsfLOCAL but also mathsfquantum-mathsfLOCAL, even with a pre-shared quantum state. We also show that similar arguments can be used to prove that, e.g., 3-coloring 2-dimensional grids or c-coloring trees remain hard problems even for the non-signaling model, and in particular do not admit any quantum advantage. Our lower-bound arguments are purely graph-theoretic at heart; no background on quantum information theory is needed to establish the proofs.

Given a quantum channel and a state which satisfy a fixed point equation approx- imately (say, up to an error ε), can one find a new channel and a state, which are respectively close to the original ones, such that they satisfy an exact fixed point equa- tion? It is interesting to ask this question for different choices of constraints on the structures of the original channel and state, and requiring that these are also satisfied by the new channel and state. We affirmatively answer the above question, under fairly general assumptions on these structures, through a compactness argument. Ad- ditionally, for channels and states satisfying certain specific structures, we find explicit upper bounds on the distances between the pairs of channels (and states) in question. When these distances decay quickly (in a particular, desirable manner) as ε → 0, we say that the original approximate fixed point equation is rapidly fixable. We establish rapid fixability, not only for general quantum channels, but also when the original and new channels are both required to be unitary, mixed unitary or unital. In contrast, for the case of bipartite quantum systems with channels acting trivially on one subsys- tem, we prove that approximate fixed point equations are not rapidly fixable. In this case, the distance to the closest channel (and state) which satisfy an exact fixed point equation can depend on the dimension of the quantum system in an undesirable way. We apply our results on approximate fixed point equations to the question of robust- ness of quantum Markov chains (QMC) and establish the following: For any tripartite quantum state, there exists a dimension-dependent upper bound on its distance to the set of QMCs, which decays to zero as the conditional mutual information of the state vanishes.

Problems based on the structure of graphs – for example finding cliques, independent sets, or colourings – are of fundamental importance in classical complexity. It is well motivated to consider similar problems about quantum graphs, which are an operator system generalisation of graphs. Defining well-formulated decision problems for quantum graphs faces several technical challenges, and consequently the connections between quantum graphs and complexity have been underexplored. In this work, we introduce and study the clique problem for quantum graphs. Our approach utilizes a well-known connection between quantum graphs and quantum channels. The inputs for our problems are presented as quantum channels induced by circuits, which implicitly determine a corresponding quantum graph. We also use this approach to reimagine the clique and independent set problems for classical graphs, by taking the inputs to be circuits of deterministic or noisy channels which implicitly determine confusability graphs. We show that, by varying the collection of channels in the language, these give rise to complete problems for the classes NP, MA, QMA, and QMA(2). In this way, we exhibit a classical complexity problem whose natural quantisation is QMA(2), rather than QMA, which is commonly assumed. To prove the results in the quantum case, we make use of methods inspired by self-testing. To illustrate the utility of our techniques, we include a new proof of the reduction of QMA(k) to QMA(2) via cliques for quantum graphs. We also study the complexity of a version of the independent set problem for quantum graphs, and provide preliminary evidence that it may be in general weaker in complexity, contrasting to the classical case where the clique and independent set problems are equivalent.

We introduce the parallel reversible pebbling game on directed graphs for constructing parallel quantum circuits that are efficient with respect to amortized space-time complexity (equivalently named cumulative complexity (CC)), a stronger metric than the conventional space-time complexity used for parallel algorithms. Our main result is a mapping from irreversible algorithms for computing a function, to quantum algorithms for computing the function in superposition, with just a sub-polynomial overhead in cumulative complexity. Thus, to construct a CC-efficient quantum oracle for a function, it suffices to solve the simpler problem of designing a CC-efficient classical algorithm for the function. This transformation also allows us to leverage the vast body of work on classical pebbling games for developing parallel quantum circuits with low amortized space-time complexity, given the data-dependency graph of the problem.

The inherent irreversibility of quantum dynamics for open systems poses a significant barrier to the inversion of unknown quantum processes. To tackle this challenge, we propose the framework of virtual combs that exploit the unknown process iteratively with additional classical post-processing to simulate the process inverse. Our research establishes a path to achieving the exact inverse of unknown channels with certain conditions, accompanied by a no-go theorem that underscores the intrinsic limitations imposed by quantum mechanics on such tasks. Notably, we demonstrate that an n-slot virtual comb can exactly reverse a depolarizing channel with one unknown noise parameter out of n+1 potential candidates, and a 1-slot virtual comb can exactly reverse an arbitrary pair of quantum channels. We further explore the approximate inverse of an unknown channel within a given channel set. For any unknown depolarizing channels within a specified noise region, we unveil a worst-case error decay of O(n^−1) of reversing the channel via virtual combs. Moreover, we show that virtual combs with constant slots can be applied to universally reverse unitary operations and investigate the trade-off between the slot number and the sampling overhead.

In this work, we prove uniform continuity bounds for entropic quantities related to the sandwiched Rényi divergences such as the sandwiched Rényi conditional entropy. We follow three different approaches: The first one is the axiomatic approach, which exploits the sub-/ superadditivity and joint concavity/ convexity of the exponential of the divergence. In our second approach, termed the "operator space approach", we express the entropic measures as norms and utilize their properties for establishing the bounds. These norms draw inspiration from interpolation space norms. We not only demonstrate the norm properties solely relying on matrix analysis tools but also extend their applicability to a context that holds relevance in resource theories. By this, we extend the strategies of Marwah and Dupuis as well as Beigi and Goodarzi employed in the sandwiched Rényi conditional entropy context. Finally, we merge the approaches into a mixed approach that has some advantageous properties and then discuss in which regimes each bound performs best. Our results improve over the previous best continuity bounds or sometimes even give the first continuity bounds available. In a separate contribution, we use the ALAAF method, developed in a previous article by some of the authors, to study the stability of approximate quantum Markov chains.

We show that spin chains in thermal equilibrium have a correlation structure in which individual regions are strongly correlated at most with their near vicinity. We quantify this with alternative notions of the conditional mutual information defined through the so-called Belavkin-Staszewski relative entropy. We prove that these measures decay super-exponentially, under the assumption that the spin chain Hamiltonian is translation-invariant. Using a recovery map associated with these measures, we sequentially construct tensor network approximations in terms of marginals of small (sub-logarithmic) size. As a main application, we show that classical representations of the states can be learned efficiently from local measurements with a polynomial sample complexity. We also prove an approximate factorization condition for the purity of the entire Gibbs state, which implies that it can be efficiently estimated to a small multiplicative error from a small number of local measurements. As a technical step of independent interest, we show an upper bound to the decay of the Belavkin-Staszewski relative entropy upon the application of a conditional expectation.

We study the one-clean-qubit model of quantum communication where one qubit is in a pure state and all other qubits are maximally mixed. We demonstrate a partial function that has a quantum protocol of cost O(log N) in this model, however, every interactive randomized protocol has cost Ømega(sqrtN), settling a conjecture of Klauck and Lim. In contrast, all prior quantum versus classical communication separations required at least Ømega(log N) clean qubits. The function demonstrating our separation also has an efficient protocol in the quantum-simultaneous-with-entanglement model of cost O(log N). We thus recover the state-of-the-art separations between quantum and classical communication complexity. Our proof is based on a recent hypercontractivity inequality introduced by Ellis, Kindler, Lifshitz, and Minzer, in conjunction with tools from the representation theory of compact Lie groups.

Secret sharing allows a user to split a secret into many shares so that the secret can be recovered if, and only if, an authorized set of shares is collected. Although secret sharing typically does not require any computational hardness assumptions, its security does require that an adversary cannot collect an authorized set of shares. Over long periods of time where an adversary can benefit from multiple data breaches, this may become an unrealistic assumption. We initiate the systematic study of secret sharing with certified deletion in order to achieve security even against an adversary that eventually collects an authorized set of shares. In secret sharing with certified deletion, a (classical) secret s is split into quantum shares that can be destroyed in a manner verifiable by the dealer. We put forth two natural definitions of security. No-signaling security roughly requires that if multiple non-communicating adversaries delete sufficiently many shares, then their combined view contains negligible information about s, even if the total set of corrupted parties forms an authorized set. Adaptive security requires privacy of s against an adversary that can continuously and adaptively corrupt new shares and delete previously-corrupted shares, as long as the total set of corrupted shares minus deleted shares remains unauthorized. Next, we show that these security definitions are achievable: we show how to construct (i) a secret sharing scheme with no-signaling certified deletion for any monotone access structure, and (ii) a threshold secret sharing scheme with adaptive certified deletion. Our first construction uses Bartusek and Khurana's (CRYPTO 2023) 2-out-of-2 secret sharing scheme with certified deletion as a building block, while our second construction is built from scratch and requires several new technical ideas. For example, we significantly generalize the ``XOR extractor'' of Agarwal, Bartusek, Khurana, and Kumar (EUROCRYPT 2023) in order to obtain better seedless extraction from certain quantum sources of entropy, and show how polynomial interpolation can double as a high-rate randomness extractor in our context of threshold sharing with certified deletion.

Locality is a fundamental feature of many physical time evolutions. Assumptions on locality and related structural properties also underlie recently proposed procedures for learning an unknown Hamiltonian from access to the induced time evolution. However, no protocols to rigorously test whether an unknown Hamiltonian is in fact local were known. We investigate Hamiltonian locality testing as a property testing problem, where the task is to determine whether an unknown Hamiltonian H is k-local or epsilon-far from all k-local Hamiltonians, given access to the time evolution along H. First, we emphasize the importance of the chosen distance measure: With respect to the operator norm, a worst-case distance measure, incoherent quantum locality testers require at least order 2^n many time evolution queries and an expected total evolution time of order 2^n/epsilon, and even coherent testers need at least order 2^(n/2) many queries and order 2^(n/2)/epsilon total evolution time. In contrast, when distances are measured according to the normalized Frobenius norm, corresponding to an average-case distance, we give a sample-, time-, and computationally efficient incoherent Hamiltonian locality testing algorithm based on randomized measurements. In fact, our procedure can be used to simultaneously test a wide class of Hamiltonian properties beyond locality. Finally, we prove that learning a general Hamiltonian remains exponentially hard with this average-case distance, thereby establishing an exponential separation between Hamiltonian testing and learning. Our work initiates the study of property testing for quantum Hamiltonians, demonstrating that a broad class of Hamiltonian properties is efficiently testable even with limited quantum capabilities, and positioning Hamiltonian testing as an independent area of research alongside Hamiltonian learning.

We extend the theory of locally checkable labeling problems (LCLs) from the classical LOCAL model to a number of other models that have been studied recently, including the quantum-LOCAL model, finitely-dependent processes, non-signaling model, dynamic-LOCAL model, and online-LOCAL model [e.g. STOC 2024, ICALP 2023]. First, we demonstrate the advantage that finitely-dependent processes have over the classical LOCAL model. We show that all LCL problems solvable with locality O(log⋆n) in the LOCAL model admit a finitely-dependent distribution (with constant locality). In particular, this gives a finitely-dependent coloring for regular trees, answering an open question by Holroyd [2023]. This also introduces a new formal barrier for understanding the distributed quantum advantage: it is not possible to exclude quantum advantage for any LCL in the Θ(log⋆n) complexity class by using non-signaling arguments. Second, we put limits on the capabilities of all of these models. To this end, we introduce a model called randomized online-LOCAL, which is strong enough to simulate e.g. SLOCAL and dynamic-LOCAL, and we show that it is also strong enough to simulate any non-signaling distribution and hence any quantum-LOCAL algorithm. We prove the following result for rooted trees: if we can solve an LCL problem with locality o(loglogn) in the randomized online-LOCAL model, we can solve it with locality O(log⋆n) in the classical deterministic LOCAL model. Put together, these results show that in rooted trees the set of LCLs that can be solved with locality O(log⋆n) is the same across all these models: classical deterministic and randomized LOCAL, quantum-LOCAL, non-signaling model, dynamic-LOCAL, and deterministic and randomized online-LOCAL.

The entropy accumulation theorem, and its subsequent generalized version, is a powerful tool in the security analysis of many device-dependent and device-independent cryptography protocols. However, it has the drawback that the finite-size bounds it yields are not necessarily optimal, and furthermore it relies on the construction of an affine min-tradeoff function, which can often be challenging to construct optimally in practice. In this work, we address both of these challenges simultaneously by deriving a new entropy accumulation bound. Our bound yields significantly better finite-size performance, and can be computed as an intuitively interpretable convex optimization, without any specification of affine min-tradeoff functions. Furthermore, it can be applied directly at the level of Renyi entropies if desired, yielding fully-Renyi security proofs. Our proof techniques are based on elaborating on a connection between entropy accumulation and the frameworks of quantum probability estimation or f-weighted Rényi entropies, and in the process we obtain some new results with respect to those frameworks as well.

A basic question in the PAC model of learning is whether proper learning is harder than improper learning. In the classical case, there are examples of concept classes with VC dimension d that have sample complexity Ω(d/ϵ log(1/ϵ)) for proper learning with error ϵ, while the complexity for improper learning is O(d/ϵ). One such example arises from the Coupon Collector problem. Motivated by the efficiency of proper versus improper learning with quantum samples, Arunachalam, Belovs, Childs, Kothari, Rosmanis, and de Wolf (TQC 2020) studied an analogue, the Quantum Coupon Collector problem. Curiously, they discovered that for learning size k subsets of [n] the problem has sample complexity Θ(k log mink,n−k+1), in contrast with the complexity of Θ(k log k) for Coupon Collector. This effectively negates the possibility of a separation between the two modes of learning via the quantum problem, and Arunachalam et al. posed the possibility of such a separation as an open question. In this work, we first present an algorithm for the Quantum Coupon Collector problem with sample complexity that matches the sharper lower bound of (1−o_k(1))k ln mink,n−k+1 shown recently by Bab Hadiashar, Nayak, and Sinha (IEEE TIT 2024), for the entire range of the parameter k. Next, we devise a variant of the problem, the Quantum Padded Coupon Collector. We prove that its sample complexity matches that of the classical Coupon Collector problem for both modes of learning, thereby exhibiting the same asymptotic separation between proper and improper quantum learning as mentioned above. The techniques we develop in the process can be directly applied to any form of padded quantum data. We hope that padding can more generally lift other forms of classical learning behaviour to the quantum setting.

We revisit the problem of characterising the complexity of Quantum PAC learning, as introduced by Bshouty and Jackson [1]. Several quantum advantages have been demonstrated in this setting, however, none are generic: they apply to particular concept classes and typically only work when the distribution that generates the data is known. In the general case, it was recently shown by Arunachalam and de Wolf [2] that quantum PAC learners can only achieve constant factor advantages over classical PAC learners. We show that with a natural extension of the definition of quantum PAC learning used by Arunachalam and de Wolf, we can achieve a generic advantage in quantum learning. To be precise, for any concept class C of VC dimension d, we show there is an (ϵ,δ)-quantum PAC learner with sample complexity O(1/√ϵ[d+log(1/δ)]log^9(1/ϵ)). Up to polylogarithmic factors, this is a square root improvement over the classical learning sample complexity. We show the tightness of our result by proving an Ω(d/√ϵ) lower bound that matches our upper bound up to polylogarithmic factors. [1] SIAM J. Comput. 1998, 28, 1136-1153 [2] JMLR, 19 (2018) 1-36

Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it – one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. We also work out the constant factors associated with a novel implementation of a high-order Trotter approach to simulating a grid representation of these systems. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoco or P450.

A set of classical or quantum states is equivalent to another one if there exists a pair of classical or quantum channels mapping either set to the other one. For dichotomies (pairs of states), this is closely connected to (classical or quantum) Rényi divergences (RD) and the data-processing inequality: If a RD remains unchanged when a channel is applied to the dichotomy, then there is a recovery channel mapping the image back to the initial dichotomy. Here, we prove for classical dichotomies that equality of the RDs alone is already sufficient for the existence of a channel in any of the two directions and discuss some applications. In the quantum case, all families of quantum RDs are seen to be insufficient because they cannot detect anti-unitary transformations. Thus, including anti-unitaries, we pose the problem of finding a sufficient family. It is shown that the Petz and maximal quantum RD are still insufficient in this more general sense and we provide evidence for sufficiency of the minimal quantum RD. As a side result of our techniques, we obtain an infinite list of inequalities fulfilled by the classical, the Petz quantum, and the maximal quantum RDs. These inequalities are not true for the minimal quantum RDs. Our results further imply that any sufficient set of conditions for state transitions in the resource theory of athermality must be able to detect time-reversal.

Analysis on the Boolean hypercube −1,1^n, particularly Fourier analysis, has played a crucial role in many areas of mathematics and computer science, including learning algorithms. In view of the success and importance of quantum algorithms, it is natural to transfer classical learning results to the quantum realm. In this contribution, we extend the recent progress on learning low-degree functions and quantum operators to the setting of general qudit systems, as well as develop novel Fourier-analytic tools for studying (generalized) Pauli decompositions of Hermitian operators. These tools also allow us to deduce new results in quantum Boolean analysis and approximate theory, such as the junta-type theorem and dimension-free discrete Remez-type inequalities.

Quantum query complexity has several nice properties with respect to composition. First, bounded-error quantum query algorithms can be composed without incurring log factors through error reduction (emphexactness). Second, through careful accounting (emphthriftiness), the total query complexity is smaller if subroutines are mostly run on cheaper inputs – a property that is much less obvious in quantum algorithms than in their classical counterparts. While these properties were previously seen through the model of span programs (alternatively, the dual adversary bound), a recent work by two of the authors (Belovs, Yolcu 2023) showed how to achieve these benefits without converting to span programs, by defining emphquantum Las Vegas query complexity. Independently, recent works, including by one of the authors (Jeffery 2022), have worked towards bringing thriftiness to the more practically significant setting of quantum emphtime complexity. In this work, we show how to achieve both exactness and thriftiness in the setting of time complexity. We generalize the quantum subroutine composition results of Jeffery 2022 so that, in particular, no error reduction is needed. We give a time complexity version of the well-known result in quantum query complexity, Q(fcirc g)=ØO(Q(f)cdot Q(g)), without log factors. We achieve this by employing a novel approach to the design of quantum algorithms based on what we call emphtransducers, and which we think is of large independent interest. While a span program is a completely different computational model, a transducer is a direct generalisation of a quantum algorithm, which allows for much greater transparency and control. Transducers naturally characterize general state conversion, rather than only decision problems; provide a very simple treatment of other quantum primitives such as quantum walks; and lend themselves well to time complexity analysis.

In this work, we continue the search for quantum locally testable codes (qLTCs) of new parameters by presenting three constructions that can make new qLTCs from old. The first analyses the soundness of a quantum code under Hastings' weight reduction construction for qLDPC codes to give a weight reduction procedure for qLTCs. Secondly, we describe a novel `soundness amplification' procedure for qLTCs which can increase the soundness of any qLTC to a constant while preserving its distance and dimension, with an impact only felt on its locality. Finally, we apply the AEL distance amplification construction to the case of qLTCs for the first time which can turn a high-distance qLTC into one with linear distance, at the expense of other parameters. These constructions can be used on as-yet undiscovered qLTCs to obtain new parameters, but we also find a number of present applications. Applying these constructions in various combinations to recent advancements yields near-optimal quantum locally testable codes.

Constant-rate low-density parity-check (LDPC) codes are promising candidates for constructing efficient fault-tolerant quantum memories. However, if physical gates are subject to geometric-locality constraints, it becomes challenging to realize these codes. In this paper, we construct a new family of [[N,K,D]] codes, referred to as hierarchical codes, that encode a number of logical qubits K = Omega(N/łog(N)^2). The N-th element of this code family is obtained by concatenating a constant-rate quantum LDPC code with a surface code; nearest-neighbor gates in two dimensions are sufficient to implement the corresponding syndrome-extraction circuit and achieve a threshold. Below threshold the logical failure rate vanishes superpolynomially as a function of the distance D(N). We present a bilayer architecture for implementing the syndrome-extraction circuit, and estimate the logical failure rate for this architecture. Under conservative assumptions, we find that the hierarchical code outperforms the basic encoding where all logical qubits are encoded in the surface code.

We consider the problem of learning low-degree quantum objects up to ε-error in l_2-distance. We show the following results: (I) unknown n-qubit degree-d (in the Pauli basis) quantum channels and unitaries can be learned using O(1/ε^d) queries (which is independent of n), (II) polynomials p:-1,1^n -> [-1,1] arising from d-query quantum algorithms can be learned from O((1/ε)^d log n) many random examples (x,p(x)) (which implies learnability even for d=O(log n)), and (III) degree-d polynomials p:-1,1^n -> [-1,1] can be learned through O(1/ε^d) queries to a quantum unitary Up that block-encodes p. Our main technical contributions are new Bohnenblust-Hille inequalities for quantum channels and completely bounded polynomials.

We study the quantum computational complexity of the discrete logarithm (DL) problems and integer factorization in the context of ``generic algorithms''—that is, algorithms that do not exploit any properties of the group/ring encoding. We establish the generic models of quantum algorithms for group/ring problems as quantum analogs of their classical counterparts. In these models, we count the number of group/ring operations as complexity measures. Shor's and Regev's algorithms (or their slight modifications) can be described in these models. We show the quantum complexity lower bounds and (almost) matching algorithms of the discrete logarithm and integer factorization in these models. * (The DL problem, fully quantum setting) We prove that any generic quantum DL algorithm must make a logarithmic number of group operations for the group size, showing that Shor's algorithm that makes the logarithmic number of group operations is asymptotically optimal regarding the number of group operations. This holds even considering parallel quantum algorithms. * (The DL problem, hybrid/memory-bounded setting) We observe that some (known) variations of Shor's algorithm can take advantage of classical computations to reduce the number and depth of quantum group operations. We establish a model for generic hybrid quantum-classical algorithms and prove the matching lower bounds. We also study the memory-bounded setting and establish asymptotically tight lower bounds. We extend these results to the multiple-instance DL problem with a matching new algorithm. * (Integer factorization) We give a logarithmic lower bound for the order-finding algorithms, an important step for Shor's algorithm. We also prove a logarithmic lower bound for a certain generic factoring algorithm outputting relatively small integers, which includes a modified version of Regev's algorithm.

Learning tasks play an increasingly prominent role in quantum information and computation. They range from fundamental problems such as state discrimination and metrology over the framework of quantum probably approximately correct (PAC) learning, to the recently proposed shadow variants of state tomography. However, the many directions of quantum learning theory have so far evolved separately. We propose a general mathematical formalism for describing quantum learning by training on classical-quantum data and then testing how well the learned hypothesis generalizes to new data. In this framework, we prove bounds on the expected generalization error of a quantum learner in terms of classical and quantum mutual information quantities measuring how strongly the learner's hypothesis depends on the specific data seen during training. To achieve this, we use tools from quantum optimal transport and quantum concentration inequalities to establish non-commutative versions of decoupling lemmas that underlie recent information-theoretic generalization bounds for classical machine learning. Our framework encompasses and gives intuitively accessible generalization bounds for a variety of quantum learning scenarios such as quantum state discrimination, PAC learning quantum states, quantum parameter estimation, and quantumly PAC learning classical functions. Thereby, our work lays a foundation for a unifying quantum information-theoretic perspective on quantum learning.

Geometric locality is an important theoretical and practical factor for quantum low-density parity-check (qLDPC) codes which affects code performance and ease of physical realization. For device architectures restricted to 2D local gates, naively implementing the high-rate codes suitable for low-overhead fault-tolerant quantum computing incurs prohibitive overhead. In this work, we present an error correction protocol built on a bilayer architecture that aims to reduce operational overheads when restricted to 2D local gates by measuring some generators less frequently than others. We investigate the family of bivariate bicycle qLDPC codes and show that they are well suited for a parallel syndrome measurement scheme using fast routing with local operations and classical communication (LOCC). Through circuit-level simulations, we find that in some parameter regimes bivariate bicycle codes implemented with this protocol have logical error rates comparable to the surface code while using fewer physical qubits.

The solution of large systems of nonlinear differential equations is needed for many applications in science and engineering. In this study, we present three main improvements to existing quantum algorithms based on the Carleman linearisation technique. First, by using a high-precision technique for the solution of the linearised differential equations, we achieve logarithmic dependence of the complexity on the error and near-linear dependence on time. Second, we demonstrate that a rescaling technique can considerably reduce the cost, which would otherwise be exponential in the Carleman order for a system of ODEs, preventing a quantum speedup for PDEs. Third, we provide improved, tighter bounds on the error of Carleman linearisation. We apply our results to a class of discretised reaction-diffusion equations using higher-order finite differences for spatial resolution. We show that providing a stability criterion independent of the discretisation can conflict with the use of the rescaling due to the difference between the max-norm and 2-norm. An efficient solution may still be provided if the number of discretisation points is limited, as is possible when using higher-order discretisations.

Quantum state tomography, aimed at deriving a classical description of an unknown state from measurement data, is a fundamental task in quantum physics. In this work, we analyse the ultimate achievable performance of tomography of continuous-variable systems, such as bosonic and quantum optical systems. We prove that tomography of these systems is extremely inefficient in terms of time resources, much more so than tomography of qudit systems: the minimum number of state copies needed for tomography not only scales exponentially with the number of modes but also exhibits a dramatic scaling with the trace-distance error, even for low-energy states. On a more positive note, we prove that tomography of Gaussian states is efficient. To accomplish this, we answer a fundamental question for the field of continuous-variable quantum information: if we know with a certain error the first and second moments of an unknown Gaussian state, what is the resulting trace-distance error that we make on the state? Lastly, we demonstrate that tomography of non-Gaussian states prepared through Gaussian unitaries and a few local non-Gaussian evolutions is efficient and experimentally feasible.

Quantum Tanner codes constitute a family of quantum low-density parity-check (LDPC) codes with good parameters, i.e., constant encoding rate and relative distance. In this article, we prove that quantum Tanner codes also facilitate single-shot quantum error correction (QEC) of adversarial noise, where one measurement round (consisting of constant-weight parity checks) suffices to perform reliable QEC even in the presence of measurement errors. We establish this result for both the sequential and parallel decoding algorithms introduced by Leverrier and Zemor. Furthermore, we show that in order to suppress errors over multiple repeated rounds of QEC, it suffices to run the parallel decoding algorithm for constant time in each round. Combined with good code parameters, the resulting constant-time overhead of QEC and robustness to (possibly time-correlated) adversarial noise make quantum Tanner codes alluring from the perspective of quantum fault-tolerant protocols.

The essential requirement for fault-tolerant quantum computation (FTQC) is the total protocol design to achieve a fair balance of all the critical factors relevant to its practical realization, such as the space overhead, the threshold, and the modularity. A major obstacle in realizing FTQC with conventional protocols, such as those based on the surface code and the concatenated Steane code, has been the space overhead, i.e., the required number of physical qubits per logical qubit. Protocols based on high-rate quantum low-density parity-check (LDPC) codes gather considerable attention as a way to reduce the space overhead, but problematically, the existing fault-tolerant protocols for such quantum LDPC codes sacrifice the other factors. Here we construct a new fault-tolerant protocol to meet these requirements simultaneously based on more recent progress on the techniques for concatenated codes rather than quantum LDPC codes, achieving a constant space overhead, a high threshold, and flexibility in modular architecture designs. In particular, under a physical error rate of 0.1%, our protocol reduces the space overhead to achieve the logical CNOT error rates 10^-10 and 10^-24 by more than 90% and 97%, respectively, compared to the protocol for the surface code. Furthermore, our protocol achieves the threshold of 2.4% under a conventional circuit-level error model, substantially outperforming that of the surface code. The use of concatenated codes also naturally introduces abstraction layers essential for the modularity of FTQC architectures. These results indicate that the code-concatenation approach opens a way to significantly save qubits in realizing FTQC while fulfilling the other essential requirements for the practical protocol design.

We study quantum neural networks made by parametric one-qubit gates and fixed two-qubit gates in the limit of infinite width, where the generated function is the expectation value of the sum of single-qubit observables over all the qubits. First, we prove that the probability distribution of the function generated by the untrained network with randomly initialized parameters converges in distribution to a Gaussian process whenever each measured qubit is correlated only with few other measured qubits. Then, we analytically characterize the training of the network via gradient descent with square loss on supervised learning problems. We prove that, as long as the network is not affected by barren plateaus, the trained network can perfectly fit the training set and that the probability distribution of the function generated after training still converges in distribution to a Gaussian process. Finally, we consider the statistical noise of the measurement at the output of the network and prove that a polynomial number of measurements is sufficient for all the previous results to hold and that the network can always be trained in polynomial time.

Stabilizer channels are stabilizer circuits that implement logical operations while mapping from an input stabilizer code to an output stabilizer code. They are widely used to implement fault tolerant error correction and logical operations in stabilizer codes such as surface codes and LDPC codes, and more broadly in subsystem, Floquet and space-time codes. We introduce a rigorous and general formalism to analyze the fault tolerance properties of any stabilizer channel under a broad class of noise models. This includes rigorous but easy-to-work-with definitions and algorithms for the fault distance and hook faults for stabilizer channels. The generalized notion of hook faults which we introduce, defined with respect to an arbitrary subset of a circuit’s faults rather than a fixed phenomenological noise model, can be leveraged for fault-tolerant circuit design. Additionally, we establish necessary conditions such that channel composition preserves the fault distance. We apply our framework to design and analyze fault tolerant stabilizer channels for surface codes, revealing novel aspects of fault tolerant circuits.

We establish connections between state tomography, pseudorandomness, quantum state synthesis, and circuit lower bounds. In particular, let C be a family of non-uniform quantum circuits of polynomial size and suppose that there exists an algorithm that, given copies of |ψ⟩, distinguishes whether |ψ⟩ is produced by C or is Haar random, promised one of these is the case. For arbitrary fixed constant c, we show that if the algorithm uses at most O(2^n^c) time and 2^n^0.99 samples then stateBQE⊄stateC. Here stateBQE:=stateBQTIME[2^O(n)] and stateC are state synthesis complexity classes as introduced by Rosenthal and Yuen (ITCS 2022), which capture problems with classical inputs but quantum output. Note that efficient tomography implies a similarly efficient distinguishing algorithm against Haar random states, even for nearly exponential-time algorithms. Because every state produced by a polynomial-size circuit can be learned with 2O(n) samples and time, or O(n^ω(1)) samples and 2^O(n^ω(1)) time, we show that even slightly non-trivial quantum state tomography algorithms would lead to new statements about quantum state synthesis. Finally, a slight modification of our proof shows that distinguishing algorithms for quantum states can imply circuit lower bounds for decision problems as well. This help sheds light on why time-efficient tomography algorithms for non-uniform quantum circuit classes has only had limited and partial progress. Our work parallels results by Arunachalam et al. (FOCS 2021) that revealed a similar connection between quantum learning of Boolean functions and circuit lower bounds for classical circuit classes, but modified for the purposes of state tomography and state synthesis.

We show how to realize a general quantum circuit involving gates between arbitrary pairs of qubits by means of geometrically local quantum operations and efficient classical computation. We prove that circuit-level local stochastic noise modeling an imperfect implementation of our derived schemes is equivalent to local stochastic noise in the original circuit. Our constructions incur a constant-factor increase in the quantum circuit depth and a polynomial overhead in the number of qubits: To execute an arbitrary quantum circuit on n qubits, we give a 3D quantum fault-tolerance architecture involving O(n^3/2 log^3 n) qubits, and a quasi-2D architecture using O(n^2 log^3 n) qubits. Applied to recent fault-tolerance constructions, this gives a fault-tolerance threshold theorem for universal quantum computations with local operations, a polynomial qubit overhead and a quasi-polylogarithmic depth overhead. More generally, our transformation dispenses with the need for considering the locality of operations when designing schemes for fault-tolerant quantum information processing.

Barren plateaus have emerged as a pivotal challenge for variational quantum computing. Our understanding of this phenomenon underwent a transformative shift with the recent introduction of a Lie algebraic theory capable of explaining most sources of barren plateaus. However, this theory requires either initial states or observables that lie in the circuit's Lie algebra. Focusing on parametrized matchgate circuits, in this work we are able to go beyond this assumption and provide an exact formula for the loss function variance that is valid for arbitrary input states and measurements. Our results reveal that new phenomena emerge when the Lie algebra constraint is relaxed. For instance, we find that the variance does not necessarily vanish inversely with the Lie algebra's dimension. Instead, this measure of expressiveness is replaced by a generalized expressiveness quantity: the dimension of the Lie group modules. By characterizing the operators in these modules as products of Majorana operators, we can introduce a precise notion of generalized globality and show that measuring generalized-global operators leads to barren plateaus. Our work also provides operational meaning to the generalized entanglement as we connect it with known fermionic entanglement measures, and show that it satisfies a monogamy relation. Finally, while parameterized matchgate circuits are not efficiently simulable in general, our results suggest that the structure allowing for trainability may also lead to classical simulability.