Bill Fefferman

BosonSampling and Random Circuit Sampling are important both as a theoretical tool for separating quantum and classical computation, and as an experimental means of demonstrating quantum speedups. Prior works have shown that average-case hardness of sampling follows from certain unproven conjectures about the hardness of computing output probabilities, such as the Permanent-of-Gaussians Conjecture (PGC), which states that $e^{-n\log{n}-n-O(\log n)}$ additive-error estimates to the output probability of most random BosonSampling experiments are $\#P$-hard. Prior works have only shown weaker average-case hardness results that do not imply sampling hardness. Proving these conjectures has become a central question in quantum complexity. In this work, we show that $e^{-n\log n-n-O(n^\delta)}$ additive-error estimates to output probabilities of most random BosonSampling experiments are $\#P$-hard for any $\delta>0$, exponentially improving on prior work. In the process, we circumvent all known barrier results for proving PGC. The remaining hurdle to prove PGC is now “merely” to show that the $O(n^\delta)$ in the exponent can be improved to $O(\log n).$ We also obtain an analogous result for Random Circuit Sampling. We then show, for the first time, a hardness of average-case classical sampling result for BosonSampling, under an anticoncentration conjecture. Specifically, we prove the impossibility of multiplicative-error sampling from random BosonSampling experiments with probability $1-2^{-\tilde{\mathstrut O}(N^{1/3})}$ for input size $N$, unless the Polynomial Hierarchy collapses. This exponentially improves upon the state-of-the-art. To do this, we introduce new proof techniques which tolerate exponential loss in the worst-to-average-case reduction. This opens the possibility to show the hardness of average-case sampling without ever proving PGC.

Recently, several quantum benchmarking algorithms have been developed to characterize noisy quantum gates on today's quantum devices. A well-known issue in benchmarking is that not everything about quantum noise is learnable due to the existence of gauge freedom, leaving open the question of what information about noise is learnable and what is not, which has been unclear even for a single CNOT gate. Here we give a precise characterization of the learnability of Pauli noise channels attached to Clifford gates, showing that learnable information corresponds to the cycle space of the pattern transfer graph of the gate set, while unlearnable information corresponds to the cut space. This implies the optimality of cycle benchmarking, in the sense that it can learn all learnable information about Pauli noise. We experimentally demonstrate noise characterization of IBM's CNOT gate up to 2 unlearnable degrees of freedom, for which we obtain bounds using physical constraints. In addition, we give an attempt to characterize the unlearnable information by assuming perfect initial state preparation. However, based on the experimental data, we conclude that this assumption is inaccurate as it yields unphysical estimates, and we obtain a lower bound on state preparation noise.

Abstract A foundational result in the theory of quantum computation, known as the ``principle of safe storage,'' shows that it is always possible to take a quantum circuit and produce an equivalent circuit that makes all measurements at the end of the computation. While this procedure is time efficient, meaning that it does not introduce a large overhead in the number of gates, it uses extra ancillary qubits, and so is not generally space efficient. It is quite natural to ask whether it is possible to eliminate intermediate measurements without increasing the number of ancillary qubits. We give an affirmative answer to this question by exhibiting a procedure to eliminate all intermediate measurements that is simultaneously space efficient and time efficient. In particular, this shows that the definition of a space-bounded quantum complexity class is robust to allowing or forbidding intermediate measurements. A key component of our approach, which may be of independent interest, involves showing that the well-conditioned versions of many standard linear-algebraic problems may be solved by a quantum computer in less space than seems possible by a classical computer.

Abstract Understanding the power of random quantum circuit sampling experiments has emerged as one of the most pressing topics in the near-term quantum era. In this work we make progress toward bridging the major remaining gaps between theory and experiment, incorporating the effects of experimental imperfections into the theoretical hardness arguments. We do this first by proving that computing the output probability of an $m$-gate random quantum circuit to within additive imprecision $2^{-O(m^{1+\epsilon})}$ is #P-hard for any $\epsilon>0$, an exponential improvement over the prior hardness results of Bouland et al. and Movassagh which were resistant to imprecision $2^{-O(m^3)}$. This improvement very nearly reaches the threshold ($2^{-O(m)}/\text{poly}(m)$) sufficient to establish the hardness of sampling for constant-depth random quantum circuits. To prove this result we introduce new error reduction techniques for polynomial interpolation, as well as a new robust Berlekamp-Welch argument over the Reals which may be of independent interest. Second we show that these results are still true in the presence of a constant rate of noise, so long as the noise rate is below the error detection threshold. That is, even though random circuits with a constant noise rate converge rapidly to the maximally mixed state, the (exponentially) small deviations in their output probabilities away from uniformity remain difficult to compute. Interestingly, we then show that our two main results are in tension with one another, and the latter result implies the former result is essentially optimal with respect to additive imprecision error, even with substantial generalizations of our techniques.

Abstract The field of quantum Hamiltonian complexity lies at the intersection of quantum many-body physics and computational complexity theory, with deep implications to both fields. The main object of study is the LocalHamiltonian problem, which is concerned with estimating the ground-state energy of a local Hamiltonian and is complete for the class QMA, a quantum generalization of the class NP. A major challenge in the field is to understand the complexity of the LocalHamiltonian problem in more physically natural parameter regimes. One crucial parameter in understanding the ground space of any Hamiltonian in many-body physics is the spectral gap, which is the difference between the smallest two eigenvalues. Despite its importance in quantum many-body physics, the role played by the spectral gap in the complexity of the LocalHamiltonian is less well-understood. In this work, we make progress on this question by considering the precise regime, in which one estimates the ground-state energy to within inverse exponential precision. Computing ground-state energies precisely is a task that is important for quantum chemistry and quantum many-body physics. In the setting of inverse-exponential precision, there is a surprising result that the complexity of LocalHamiltonian is magnified from QMA to PSPACE, the class of problems solvable in polynomial space. We clarify the reason behind this boost in complexity. Specifically, we show that the full complexity of the high precision case only comes about when the spectral gap is exponentially small. As a consequence of the proof techniques developed to show our results, we uncover important implications for the representability and circuit complexity of ground states of local Hamiltonians, the theory of uniqueness of quantum witnesses, and techniques for the amplification of quantum witnesses in the presence of postselection.