Francesco Anna Mele

Quantum data hiding is the existence of pairs of bipartite quantum states that are (almost) perfectly distinguishable with global measurements, yet close to indistinguishable when only measurements implementable with local operations and classical communication are allowed. Remarkably, data hiding states can also be chosen to be separable, meaning that secrets can be hidden using no entanglement that are almost irretrievable without entanglement --- this is sometimes called `nonlocality without entanglement'. Essentially two families of data hiding states were known prior to this work: Werner states and random states. Hiding Werner states can be made either separable or globally perfectly orthogonal, but not both --- separability comes at the price of orthogonality being only approximate. Random states can hide many more bits, but they are typically entangled and again only approximately orthogonal. In this paper, we present an explicit construction of novel group-symmetric data hiding states that are simultaneously separable, perfectly orthogonal, and even invariant under partial transpose, thus exhibiting the phenomenon of nonlocality without entanglement to the utmost extent. Our analysis leverages novel applications of numerical analysis tools to study convex optimisation problems in quantum information theory, potentially offering technical insights that extend beyond this work.

Gaussian states are widely regarded as the most important class of continuous-variable (CV) quantum states, as they naturally arise in physical systems and play a key role in quantum technologies. This motivates a fundamental question: given copies of an unknown CV state, how can we efficiently test whether it is Gaussian? We address this problem from the perspective of representation theory and quantum learning theory, characterizing the sample complexity of Gaussianity testing as a function of the number of modes. For pure states, we prove that just a constant number of copies is sufficient to decide whether the state is exactly Gaussian. We then extend this to the tolerant setting, showing that a polynomial number of copies suffices to distinguish states that are close to Gaussian from those that are far. In contrast, we establish that testing Gaussianity of general mixed states necessarily requires exponentially many copies, thereby identifying a fundamental limitation in testing CV systems. Our approach relies on rotation-invariant symmetries of Gaussian states together with the recently introduced toolbox of CV trace-distance bounds.

The field of quantum learning theory has advanced rapidly in recent years, at the intersection of quantum information science, statistical learning, and computational complexity. A key task in this area is quantum process tomography, which seeks to learn unitary transformations of quantum states efficiently. Efficient process tomography would be highly valuable: for instance, learning an unknown natural process could enable its efficient implementation and simulation on a quantum computer. However, learning arbitrary unitary operators is generally prohibitively expensive, with several sample- and time-complexity lower bounds showing the task is intractable. Thus, work typically focuses on more structured classes of operators when computational efficiency is desired. Two especially important such classes are bosonic and fermionic Gaussian unitaries. These operators have compact parametrizations, rich algebraic structure, and enough expressiveness to capture many relevant physical processes. As a result, they are ubiquitous in quantum information theory. In this work, we advance the learning theory of bosonic and fermionic unitaries in two ways: (1) We give the first time-efficient algorithm to learn bosonic Gaussian unitaries. The complexity of the algorithm scales polynomially in the number of modes, a total photon number bound (which is critical in defining an energy-constrained distance measure), and a squeezing parameter which captures how much the operator increases the mean energy of a vacuum state. (2) We give a first-of-its-kind algorithm to learn fermionic unitaries prepared with at most t non-Gaussian gates. Our algorithm scales polynomially in the number of modes and exponentially in t, and we argue that this scaling is optimal up to polynomial factors. Both algorithms produce an output whose distance to the input unitary is small in the worst-case (diamond) distance. Our results are organized into two separate manuscripts: one is arXiv:2504.11318 (Mildly-Interacting Fermionic Unitaries are Efficiently Learnable), and the other will be released on arXiv within a month (Efficient Learning of Bosonic Gaussian Unitary Channels).

We study the problem of learning Hamiltonians H that are s-sparse in the Pauli basis, given access to their time-evolution operators. Although Hamiltonian learning has been extensively investigated, two issues recur in much of the existing literature: the absence of lower bounds establishing optimality and the use of mathematically convenient but physically opaque error measures. We address both challenges by introducing two physically motivated notions of distance between Hamiltonians and designing a nearly optimal algorithm with respect to one of these metrics. The first, the time-constrained distance, quantifies distinguishability through dynamical evolution up to a bounded time. The second, the temperature-constrained distance, captures distinguishability through thermal states at bounded inverse temperatures. We show that s-sparse Hamiltonians with bounded operator norm can be learned under both distances using only $O(s log(1/ε))$ experiments and $O(s^2/ε)$ total evolution time. For the time-constrained distance, we further establish lower bounds of $Ω((s/n) log(1/ε) + s)$ experiments and $Ω(√s/ε)$ total evolution time, demonstrating near-optimality in the number of experiments. As an intermediate result, we obtain an algorithm that learns every Pauli coefficient of s-sparse Hamiltonians up to error ε in $O(s log(1/ε))$ experiments and $O(s/ε)$ total evolution time, improving upon several recent results. The source of this improvement is a new isolation technique, inspired by the Valiant-Vazirani theorem (STOC’85), which shows that NP is as easy as detecting unique solutions. This isolation technique allows us to query the time evolution of a single Pauli coefficient of a sparse Hamiltonian—even when the Pauli support of the Hamiltonian is unknown—ultimately enabling us to recover the Pauli support itself.

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.

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.

The two-way capacities of quantum channels determine the ultimate entanglement and secret-key distribution rates achievable by two distant parties that are connected by a noisy transmission line, in absence of quantum repeaters. Since repeaters will likely be expensive to build and maintain, a central open problem of quantum communication is to understand what performances are achievable without them. In this paper, we find a new lower bound on the energy-constrained and unconstrained two-way quantum and secret-key capacities of all phase-insensitive bosonic Gaussian channels, namely thermal attenuator, thermal amplifier, and additive Gaussian noise, which are realistic models for the noise affecting optical fibres or free-space links. Ours is the first nonzero lower bound on the two-way quantum capacity in the parameter range where the (reverse) coherent information becomes negative, and it shows explicitly that entanglement distribution is always possible when the channel is not entanglement breaking. This completely solves a crucial open problem of the field, namely, establishing the maximum excess noise which is tolerable in continuous-variable quantum key distribution. In addition, our construction is fully explicit, i.e.~we devise and optimise a concrete entanglement distribution and distillation protocol that works by combining recurrence and hashing protocols.

The exploration of tomography of bosonic Gaussian states is presumably as old as quantum optics, but only recently, their precise and rigorous study have been moving into the focus of attention, motivated by technological developments. In this work, we present an efficient and experimentally feasible Gaussian state tomography algorithm with provable recovery trace-distance guarantees, whose sample complexity depends only on the number of modes, and---remarkably---is independent of the state's photon number or energy, up to doubly logarithmic factors. Our algorithm yields a doubly-exponential improvement over existing methods, and it employs operations that are readily accessible in experimental settings: the preparation of an auxiliary squeezed vacuum, passive Gaussian unitaries, and homodyne detection. At its core lies an adaptive strategy that systematically reduces the total squeezing of the system, enabling efficient tomography. Quite surprisingly, this proves that estimating a Gaussian state in trace distance is generally more efficient than directly estimating its covariance matrix. Our algorithm is particularly well-suited for applications in quantum metrology and sensing, where highly squeezed---and hence high-energy---states are commonly employed. As a further contribution, we establish improved sample complexity bounds for standard heterodyne tomography, equipping this widely used protocol with rigorous trace-norm guarantees.

Non-Gaussianity is a key resource for achieving quantum advantages in bosonic platforms. Here, we investigate the symplectic rank: a novel non-Gaussianity monotone that satisfies remarkable operational and resource-theoretic properties. Mathematically, the symplectic rank of a pure state is the number of symplectic eigenvalues of the covariance matrix that are strictly larger than the ones of the vacuum. Operationally, it (i) is easy to compute, (ii) emerges as the smallest number of modes onto which all the non-Gaussianity can be compressed via Gaussian unitaries, (iii) lower bounds the non-Gaussian gate complexity of state preparation independently of the gate set, (iv) governs the sample complexity of quantum tomography, and (v) bounds the computational complexity of bosonic circuits. Crucially, the symplectic rank is non-increasing under post-selected Gaussian operations, leading to strictly stronger no-go theorems for Gaussian conversion than those previously known. Remarkably, this allows us to show that the resource theory of non-Gaussianity is irreversible under exact Gaussian operations. Finally, we show that the symplectic rank is a robust non-Gaussian measure, explaining how to witness it in experiments and how to exploit it to meaningfully benchmark different bosonic platforms. In doing so, we derive lower bounds on the trace distance (resp. total variation distance) between arbitrary states (resp. classical probability distributions) in terms of the norm distance between their covariance matrices, which may be of independent interest.

Bosonic quantum communication has extensively been analysed in the asymptotic setting, assuming infinite channel uses and vanishing communication errors. Comparatively fewer detailed analyses are available in the non-asymptotic setting, which addresses a more precise, quantitative evaluation of the optimal communication rate: how many uses of a bosonic Gaussian channel are required to transmit $k$ qubits, distil $k$ Bell pairs, or generate $k$ secret-key bits, within a given error tolerance $\varepsilon$? In this work, we address this question by finding easily computable lower bounds on the non-asymptotic capacities of Gaussian channels, and we provide explicit evaluations for the pure loss channel, for the pure amplifier channel and for a non-Markovian noise that generalizes the pure loss channel, introduced in [IEEE Transactions on Information Theory 70, 8844–8869 (2024]. To derive our results, we develop new tools of independent interest. In particular, we find a stringent bound on the probability $P_{>N}$ that a Gaussian state has more than $N$ photons, demonstrating that $P_{>N}$ decreases exponentially with $N$. Furthermore, we design the first algorithm capable of computing the trace distance between two Gaussian states up to a fixed precision. To address the non-Markovian case, we also prove properties of singular values of Toeplitz matrices, providing an error bound on the convergence rate of the celebrated Avram–Parter’s theorem, which we regard as a new tool of independent interest for the field of quantum information theory and matrix analysis.