01621nas a2200157 4500008004100000245006100041210005700102260001400159520113900173100002001312700002201332700002301354700002401377700002501401856003701426 2024 eng d00aThe Second Moment of Hafnians in Gaussian Boson Sampling0 aSecond Moment of Hafnians in Gaussian Boson Sampling c3/20/20243 a

Gaussian Boson Sampling is a popular method for experimental demonstrations of quantum advantage, but many subtleties remain in fully understanding its theoretical underpinnings. An important component in the theoretical arguments for approximate average-case hardness of sampling is anticoncentration, which is a second-moment property of the output probabilities. In Gaussian Boson Sampling these are given by hafnians of generalized circular orthogonal ensemble matrices. In a companion work [arXiv:2312.08433], we develop a graph-theoretic method to study these moments and use it to identify a transition in anticoncentration. In this work, we find a recursive expression for the second moment using these graph-theoretic techniques. While we have not been able to solve this recursion by hand, we are able to solve it numerically exactly, which we do up to Fock sector 2n=80. We further derive new analytical results about the second moment. These results allow us to pinpoint the transition in anticoncentration and furthermore yield the expected linear cross-entropy benchmarking score for an ideal (error-free) device.

1 aEhrenberg, Adam1 aIosue, Joseph, T.1 aDeshpande, Abhinav1 aHangleiter, Dominik1 aGorshkov, Alexey, V. uhttps://arxiv.org/abs/2403.1387802031nas a2200121 4500008004100000245006200041210006200103260001500165520165100180100001701831700002401848856003701872 2023 eng d00aSecret extraction attacks against obfuscated IQP circuits0 aSecret extraction attacks against obfuscated IQP circuits c12/15/20233 a

Quantum computing devices can now perform sampling tasks which, according to complexity-theoretic and numerical evidence, are beyond the reach of classical computers. This raises the question of how one can efficiently verify that a quantum computer operating in this regime works as intended. In 2008, Shepherd and Bremner proposed a protocol in which a verifier constructs a unitary from the comparatively easy-to-implement family of so-called IQP circuits, and challenges a prover to execute it on a quantum computer. The challenge problem is designed to contain an obfuscated secret, which can be turned into a statistical test that accepts samples from a correct quantum implementation. It was conjectured that extracting the secret from the challenge problem is NP-hard, so that the ability to pass the test constitutes strong evidence that the prover possesses a quantum device and that it works as claimed. Unfortunately, about a decade later, Kahanamoku-Meyer found an efficient classical secret extraction attack. Bremner, Cheng, and Ji very recently followed up by constructing a wide-ranging generalization of the original protocol. Their IQP Stabilizer Scheme has been explicitly designed to circumvent the known weakness. They also suggested that the original construction can be made secure by adjusting the problem parameters. In this work, we develop a number of secret extraction attacks which are effective against both new approaches in a wide range of problem parameters. The important problem of finding an efficient and reliable verification protocol for sampling-based proofs of quantum supremacy thus remains open.

1 aGross, David1 aHangleiter, Dominik uhttps://arxiv.org/abs/2312.1015601741nas a2200181 4500008004100000245006600041210006300107260001300170520118700183100001801370700002301388700002401411700002101435700002001456700002501476700002101501856003701522 2023 eng d00aA sharp phase transition in linear cross-entropy benchmarking0 asharp phase transition in linear crossentropy benchmarking c5/8/20233 a

Demonstrations of quantum computational advantage and benchmarks of quantum processors via quantum random circuit sampling are based on evaluating the linear cross-entropy benchmark (XEB). A key question in the theory of XEB is whether it approximates the fidelity of the quantum state preparation. Previous works have shown that the XEB generically approximates the fidelity in a regime where the noise rate per qudit ε satisfies εN≪1 for a system of N qudits and that this approximation breaks down at large noise rates. Here, we show that the breakdown of XEB as a fidelity proxy occurs as a sharp phase transition at a critical value of εN that depends on the circuit architecture and properties of the two-qubit gates, including in particular their entangling power. We study the phase transition using a mapping of average two-copy quantities to statistical mechanics models in random quantum circuit architectures with full or one-dimensional connectivity. We explain the phase transition behavior in terms of spectral properties of the transfer matrix of the statistical mechanics model and identify two-qubit gate sets that exhibit the largest noise robustness.

1 aWare, Brayden1 aDeshpande, Abhinav1 aHangleiter, Dominik1 aNiroula, Pradeep1 aFefferman, Bill1 aGorshkov, Alexey, V.1 aGullans, Michael uhttps://arxiv.org/abs/2305.0495401923nas a2200241 4500008004100000245007300041210006900114260001400183520110600197653004301303653002701346653002901373653003901402653003101441653005201472100002001524700002801544700001501572700002401587700001601611700001701627856003701644 2022 eng d00aScalably learning quantum many-body Hamiltonians from dynamical data0 aScalably learning quantum manybody Hamiltonians from dynamical d c9/28/20223 a

The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system. In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical data, by bringing together techniques from gradient-based optimization from machine learning with efficient quantum state representations in terms of tensor networks. Our approach is highly practical, experimentally friendly, and intrinsically scalable to allow for system sizes of above 100 spins. In particular, we demonstrate on synthetic data that the algorithm works even if one is restricted to one simple initial state, a small number of single-qubit observables, and time evolution up to relatively short times. For the concrete example of the one-dimensional Heisenberg model our algorithm exhibits an error constant in the system size and scaling as the inverse square root of the size of the data set.

10aFOS: Computer and information sciences10aFOS: Physical sciences10aMachine Learning (cs.LG)10aQuantum Gases (cond-mat.quant-gas)10aQuantum Physics (quant-ph)10aStrongly Correlated Electrons (cond-mat.str-el)1 aWilde, Frederik1 aKshetrimayum, Augustine1 aRoth, Ingo1 aHangleiter, Dominik1 aSweke, Ryan1 aEisert, Jens uhttps://arxiv.org/abs/2209.1432801867nas a2200205 4500008004100000245006500041210006500106260001500171520119000186653003701376653004301413653002701456653003101483100001801514700002301532700002401555700002501579700002001604856003701624 2022 eng d00aSharp complexity phase transitions generated by entanglement0 aSharp complexity phase transitions generated by entanglement c12/20/20223 a

Entanglement is one of the physical properties of quantum systems responsible for the computational hardness of simulating quantum systems. But while the runtime of specific algorithms, notably tensor network algorithms, explicitly depends on the amount of entanglement in the system, it is unknown whether this connection runs deeper and entanglement can also cause inherent, algorithm-independent complexity. In this work, we quantitatively connect the entanglement present in certain quantum systems to the computational complexity of simulating those systems. Moreover, we completely characterize the entanglement and complexity as a function of a system parameter. Specifically, we consider the task of simulating single-qubit measurements of k--regular graph states on n qubits. We show that, as the regularity parameter is increased from 1 to n−1, there is a sharp transition from an easy regime with low entanglement to a hard regime with high entanglement at k=3, and a transition back to easy and low entanglement at k=n−3. As a key technical result, we prove a duality for the simulation complexity of regular graph states between low and high regularity.

10aComputational Complexity (cs.CC)10aFOS: Computer and information sciences10aFOS: Physical sciences10aQuantum Physics (quant-ph)1 aGhosh, Soumik1 aDeshpande, Abhinav1 aHangleiter, Dominik1 aGorshkov, Alexey, V.1 aFefferman, Bill uhttps://arxiv.org/abs/2212.1058202255nas a2200205 4500008004100000245005300041210005000094260001300144520167300157100002001830700002001850700002301870700002101893700001601914700002401930700002501954700001701979700001601996856003702012 2022 eng d00aA single T-gate makes distribution learning hard0 asingle Tgate makes distribution learning hard c7/7/20223 a

The task of learning a probability distribution from samples is ubiquitous across the natural sciences. The output distributions of local quantum circuits form a particularly interesting class of distributions, of key importance both to quantum advantage proposals and a variety of quantum machine learning algorithms. In this work, we provide an extensive characterization of the learnability of the output distributions of local quantum circuits. Our first result yields insight into the relationship between the efficient learnability and the efficient simulatability of these distributions. Specifically, we prove that the density modelling problem associated with Clifford circuits can be efficiently solved, while for depth d=nΩ(1) circuits the injection of a single T-gate into the circuit renders this problem hard. This result shows that efficient simulatability does not imply efficient learnability. Our second set of results provides insight into the potential and limitations of quantum generative modelling algorithms. We first show that the generative modelling problem associated with depth d=nΩ(1) local quantum circuits is hard for any learning algorithm, classical or quantum. As a consequence, one cannot use a quantum algorithm to gain a practical advantage for this task. We then show that, for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth d=ω(log(n)) Clifford circuits is hard. This result places limitations on the applicability of near-term hybrid quantum-classical generative modelling algorithms.

1 aHinsche, Marcel1 aIoannou, Marios1 aNietner, Alexander1 aHaferkamp, Jonas1 aQuek, Yihui1 aHangleiter, Dominik1 aSeifert, Jean-Pierre1 aEisert, Jens1 aSweke, Ryan uhttps://arxiv.org/abs/2207.03140