01833nas a2200145 4500008004100000245008700041210006900128260001400197520137500211100001601586700001501602700001801617700001501635856003701650 2024 eng d00aExpanding Hardware-Efficiently Manipulable Hilbert Space via Hamiltonian Embedding0 aExpanding HardwareEfficiently Manipulable Hilbert Space via Hami c1/16/20243 a

Many promising quantum applications depend on the efficient quantum simulation of an exponentially large sparse Hamiltonian, a task known as sparse Hamiltonian simulation, which is fundamentally important in quantum computation. Although several theoretically appealing quantum algorithms have been proposed for this task, they typically require a black-box query model of the sparse Hamiltonian, rendering them impractical for near-term implementation on quantum devices.
In this paper, we propose a technique named Hamiltonian embedding. This technique simulates a desired sparse Hamiltonian by embedding it into the evolution of a larger and more structured quantum system, allowing for more efficient simulation through hardware-efficient operations. We conduct a systematic study of this new technique and demonstrate significant savings in computational resources for implementing prominent quantum applications. As a result, we can now experimentally realize quantum walks on complicated graphs (e.g., binary trees, glued-tree graphs), quantum spatial search, and the simulation of real-space Schrödinger equations on current trapped-ion and neutral-atom platforms. Given the fundamental role of Hamiltonian evolution in the design of quantum algorithms, our technique markedly expands the horizon of implementable quantum advantages in the NISQ era.

1 aLeng, Jiaqi1 aLi, Joseph1 aPeng, Yuxiang1 aWu, Xiaodi uhttps://arxiv.org/abs/2401.0855001254nas a2200157 4500008004100000245003300041210002900074260001400103520086100117100001300978700001400991700002201005700001701027700001501044856003701059 2024 eng d00aThe Quantum Abstract Machine0 aQuantum Abstract Machine c2/21/20243 a

This paper develops a model of quantum behavior that is intended to support the abstract yet accurate design and functional verification of quantum communication protocols. The work is motivated by the need for conceptual tools for the development of quantum-communication systems that are usable by non-specialists in quantum physics while also correctly capturing at a useful abstraction the underlying quantum phenomena. Our approach involves defining a quantum abstract machine (QAM) whose operations correspond to well-known quantum circuits; these operations, however, are given direct abstract semantics in a style similar to that of Berry's and Boudol's Chemical Abstract Machine. This paper defines the QAM's semantics and shows via examples how it may be used to model and reason about existing quantum communication protocols.

1 aLi, Liyi1 aChang, Le1 aCleaveland, Rance1 aZhu, Mingwei1 aWu, Xiaodi uhttps://arxiv.org/abs/2402.1346901807nas a2200145 4500008004100000245009300041210006900134260001400203520133200217100001601549700002701565700001701592700001501609856003701624 2023 eng d00aAnalyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels0 aAnalyzing Convergence in Quantum Neural Networks Deviations from c3/26/20233 a

A quantum neural network (QNN) is a parameterized mapping efficiently implementable on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. It can be used for supervised learning when combined with classical gradient-based optimizers. Despite the existing empirical and theoretical investigations, the convergence of QNN training is not fully understood. Inspired by the success of the neural tangent kernels (NTKs) in probing into the dynamics of classical neural networks, a recent line of works proposes to study over-parameterized QNNs by examining a quantum version of tangent kernels. In this work, we study the dynamics of QNNs and show that contrary to popular belief it is qualitatively different from that of any kernel regression: due to the unitarity of quantum operations, there is a non-negligible deviation from the tangent kernel regression derived at the random initialization. As a result of the deviation, we prove the at-most sublinear convergence for QNNs with Pauli measurements, which is beyond the explanatory power of any kernel regression dynamics. We then present the actual dynamics of QNNs in the limit of over-parameterization. The new dynamics capture the change of convergence rate during training and implies that the range of measurements is crucial to the fast QNN convergence.

1 aYou, Xuchen1 aChakrabarti, Shouvanik1 aChen, Boyang1 aWu, Xiaodi uhttps://arxiv.org/abs/2303.1484402618nas a2200229 4500008004100000245007500041210006900116260001500185520193200200100001902132700002002151700002602171700001702197700001802214700001802232700002302250700002202273700001502295700001802310700002302328856003702351 2023 eng d00aMicrowave signal processing using an analog quantum reservoir computer0 aMicrowave signal processing using an analog quantum reservoir co c12/26/20233 a

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training is efficient in the number of required runs of the quantum processor and takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on superconducting circuits to classify microwave signals that are analog -- continuous in time. However, while theoretical proposals of analog QRC exist, to date QRC has been implemented using circuit-model quantum systems -- imposing a discretization of the incoming signal in time, with each time point input by executing a gate operation. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our quantum system was operated without artificially discretizing the input data, directly taking in microwave signals. Our work does not attempt to address the question of whether QRCs could provide a quantum computational advantage in classifying pre-recorded classical signals. However, beyond illustrating that sophisticated tasks can be performed with a modest-size quantum system and inexpensive training, our work opens up the possibility of achieving a different kind of advantage than a purely computational advantage: superconducting circuits can act as extremely sensitive detectors of microwave photons; our work demonstrates processing of ultra-low-power microwave signals in our superconducting circuit, and by combining sensitive detection with QRC processing within the same system, one could achieve a quantum sensing-computational advantage, i.e., an advantage in the overall analysis of microwave signals comprising just a few photons.

1 aSenanian, Alen1 aPrabhu, Sridhar1 aKremenetski, Vladimir1 aRoy, Saswata1 aCao, Yingkang1 aKline, Jeremy1 aOnodera, Tatsuhiro1 aWright, Logan, G.1 aWu, Xiaodi1 aFatemi, Valla1 aMcMahon, Peter, L. uhttps://arxiv.org/abs/2312.1616601569nas a2200169 4500008004100000245008700041210006900128260001400197520105800211100001301269700001701282700002201299700001201321700001401333700001501347856003701362 2023 eng d00aQafny: Quantum Program Verification Through Type-guided Classical Separation Logic0 aQafny Quantum Program Verification Through Typeguided Classical  c7/12/20233 a

Formal verification has been proven instrumental to ensure that quantum programs implement their specifications but often requires a significant investment of time and labor. To address this challenge, we present Qafny, an automated proof system designed for verifying quantum programs. At its core, Qafny uses a type-guided quantum proof system that translates quantum operations to classical array operations. By modeling these operations as proof rules within a classical separation logic framework, Qafny provides automated support for the reasoning process that would otherwise be tedious and time-consuming. We prove the soundness and completeness of our proof system and implement a prototype compiler that transforms Qafny programs both into the Dafny programming language and into executable quantum circuits. Using Qafny, we demonstrate how to efficiently verify prominent quantum algorithms, including quantum-walk algorithms, Grover's search algorithm, and Shor's factoring algorithm, with significantly reduced human efforts.

1 aLi, Liyi1 aZhu, Mingwei1 aCleaveland, Rance1 aLee, Yi1 aChang, Le1 aWu, Xiaodi uhttps://arxiv.org/abs/2211.0641101581nas a2200181 4500008004100000245006200041210006200103260001100165490000600176520106100182100002701243700002301270700001901293700001701312700001801329700001501347856003701362 2023 eng d00aQuantum algorithm for estimating volumes of convex bodies0 aQuantum algorithm for estimating volumes of convex bodies c4/20230 v43 a

Estimating the volume of a convex body is a central problem in convex geometry and can be viewed as a continuous version of counting. We present a quantum algorithm that estimates the volume of an n-dimensional convex body within multiplicative error ε using O~(n3.5+n2.5/ε) queries to a membership oracle and O~(n5.5+n4.5/ε) additional arithmetic operations. For comparison, the best known classical algorithm uses O~(n4+n3/ε2) queries and O~(n6+n5/ε2) additional arithmetic operations. To the best of our knowledge, this is the first quantum speedup for volume estimation. Our algorithm is based on a refined framework for speeding up simulated annealing algorithms that might be of independent interest. This framework applies in the setting of "Chebyshev cooling", where the solution is expressed as a telescoping product of ratios, each having bounded variance. We develop several novel techniques when implementing our framework, including a theory of continuous-space quantum walks with rigorous bounds on discretization error.

1 aChakrabarti, Shouvanik1 aChilds, Andrew, M.1 aHung, Shih-Han1 aLi, Tongyang1 aWang, Chunhao1 aWu, Xiaodi uhttps://arxiv.org/abs/1908.0390301354nas a2200157 4500008004100000245006100041210005900102260001400161520088700175100002301062700001601085700002301101700002001124700001501144856003701159 2023 eng d00aA quantum central path algorithm for linear optimization0 aquantum central path algorithm for linear optimization c11/7/20233 a

We propose a novel quantum algorithm for solving linear optimization problems by quantum-mechanical simulation of the central path. While interior point methods follow the central path with an iterative algorithm that works with successive linearizations of the perturbed KKT conditions, we perform a single simulation working directly with the nonlinear complementarity equations. Combining our approach with iterative refinement techniques, we obtain an exact solution to a linear optimization problem involving m constraints and n variables using at most O((m+n)nnz(A)κ(M)L⋅polylog(m,n,κ(M))) elementary gates and O(nnz(A)L) classical arithmetic operations, where nnz(A) is the total number of non-zero elements found in the constraint matrix, L denotes binary input length of the problem data, and κ(M) is a condition number that depends only on the problem data.

1 aAugustino, Brandon1 aLeng, Jiaqi1 aNannicini, Giacomo1 aTerlaky, Tamás1 aWu, Xiaodi uhttps://arxiv.org/abs/2311.0397701891nas a2200145 4500008004100000245003200041210003200073260001300105520152500118100001601643700001901659700001501678700001501693856003701708 2023 eng d00aQuantum Hamiltonian Descent0 aQuantum Hamiltonian Descent c3/2/20233 a

Gradient descent is a fundamental algorithm in both theory and practice for continuous optimization. Identifying its quantum counterpart would be appealing to both theoretical and practical quantum applications. A conventional approach to quantum speedups in optimization relies on the quantum acceleration of intermediate steps of classical algorithms, while keeping the overall algorithmic trajectory and solution quality unchanged. We propose Quantum Hamiltonian Descent (QHD), which is derived from the path integral of dynamical systems referring to the continuous-time limit of classical gradient descent algorithms, as a truly quantum counterpart of classical gradient methods where the contribution from classically-prohibited trajectories can significantly boost QHD's performance for non-convex optimization. Moreover, QHD is described as a Hamiltonian evolution efficiently simulatable on both digital and analog quantum computers. By embedding the dynamics of QHD into the evolution of the so-called Quantum Ising Machine (including D-Wave and others), we empirically observe that the D-Wave-implemented QHD outperforms a selection of state-of-the-art gradient-based classical solvers and the standard quantum adiabatic algorithm, based on the time-to-solution metric, on non-convex constrained quadratic programming instances up to 75 dimensions. Finally, we propose a "three-phase picture" to explain the behavior of QHD, especially its difference from the quantum adiabatic algorithm.

1 aLeng, Jiaqi1 aHickman, Ethan1 aLi, Joseph1 aWu, Xiaodi uhttps://arxiv.org/abs/2303.0147105435nas a2201621 4500008004100000245010800041210006900149260001500218520094500233100001801178700002301196700001601219700002801235700002001263700002001283700001601303700002001319700002101339700002401360700001901384700001901403700002601422700001801448700002301466700002101489700001601510700001701526700002101543700002301564700002101587700001601608700001701624700003101641700003401672700001801706700001801724700002101742700001801763700002401781700001801805700002001823700003501843700002201878700001601900700002001916700001901936700001701955700001901972700002301991700001802014700002402032700002302056700002302079700001802102700001702120700001902137700002602156700002002182700001902202700001902221700002302240700001802263700002202281700001802303700001902321700002802340700002402368700001902392700002002411700002002431700002702451700001202478700001702490700001502507700002102522700001802543700001902561700003202580700002402612700002202636700003102658700001702689700002302706700002402729700002002753700001902773700001902792700001602811700001702827700001802844700001802862700002002880700001902900700002302919700001902942700001702961700002602978700001603004700002003020700001603040700001803056700002803074700002103102700001803123700002403141700001403165700002303179700002003202700002103222700002003243700001803263700001803281700002103299700002103320700002303341700001803364700001803382700001403400700001903414700001603433700001503449700002003464700002103484700002103505700001703526700002803543700002203571700002303593700002603616700001503642700001703657700002303674700002403697700001803721700001703739700002003756856003703776 2023 eng d00aQuantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions0 aQuantumcentric Supercomputing for Materials Science A Perspectiv c12/14/20233 a

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

1 aAlexeev, Yuri1 aAmsler, Maximilian1 aBaity, Paul1 aBarroca, Marco, Antonio1 aBassini, Sanzio1 aBattelle, Torey1 aCamps, Daan1 aCasanova, David1 aChoi, Young, jai1 aChong, Frederic, T.1 aChung, Charles1 aCodella, Chris1 aCorcoles, Antonio, D.1 aCruise, James1 aDi Meglio, Alberto1 aDubois, Jonathan1 aDuran, Ivan1 aEckl, Thomas1 aEconomou, Sophia1 aEidenbenz, Stephan1 aElmegreen, Bruce1 aFare, Clyde1 aFaro, Ismael1 aFernández, Cristina, Sanz1 aFerreira, Rodrigo, Neumann Ba1 aFuji, Keisuke1 aFuller, Bryce1 aGagliardi, Laura1 aGalli, Giulia1 aGlick, Jennifer, R.1 aGobbi, Isacco1 aGokhale, Pranav1 aGonzalez, Salvador, de la Puen1 aGreiner, Johannes1 aGropp, Bill1 aGrossi, Michele1 aGull, Emmanuel1 aHealy, Burns1 aHuang, Benchen1 aHumble, Travis, S.1 aIto, Nobuyasu1 aIzmaylov, Artur, F.1 aJavadi-Abhari, Ali1 aJennewein, Douglas1 aJha, Shantenu1 aJiang, Liang1 aJones, Barbara1 ade Jong, Wibe, Albert1 aJurcevic, Petar1 aKirby, William1 aKister, Stefan1 aKitagawa, Masahiro1 aKlassen, Joel1 aKlymko, Katherine1 aKoh, Kwangwon1 aKondo, Masaaki1 aKurkcuoglu, Doga, Murat1 aKurowski, Krzysztof1 aLaino, Teodoro1 aLandfield, Ryan1 aLeininger, Matt1 aLeyton-Ortega, Vicente1 aLi, Ang1 aLin, Meifeng1 aLiu, Junyu1 aLorente, Nicolas1 aLuckow, Andre1 aMartiel, Simon1 aMartin-Fernandez, Francisco1 aMartonosi, Margaret1 aMarvinney, Claire1 aMedina, Arcesio, Castaneda1 aMerten, Dirk1 aMezzacapo, Antonio1 aMichielsen, Kristel1 aMitra, Abhishek1 aMittal, Tushar1 aMoon, Kyungsun1 aMoore, Joel1 aMotta, Mario1 aNa, Young-Hye1 aNam, Yunseong1 aNarang, Prineha1 aOhnishi, Yu-ya1 aOttaviani, Daniele1 aOtten, Matthew1 aPakin, Scott1 aPascuzzi, Vincent, R.1 aPenault, Ed1 aPiontek, Tomasz1 aPitera, Jed1 aRall, Patrick1 aRavi, Gokul, Subramania1 aRobertson, Niall1 aRossi, Matteo1 aRydlichowski, Piotr1 aRyu, Hoon1 aSamsonidze, Georgy1 aSato, Mitsuhisa1 aSaurabh, Nishant1 aSharma, Vidushi1 aSharma, Kunal1 aShin, Soyoung1 aSlessman, George1 aSteiner, Mathias1 aSitdikov, Iskandar1 aSuh, In-Saeng1 aSwitzer, Eric1 aTang, Wei1 aThompson, Joel1 aTodo, Synge1 aTran, Minh1 aTrenev, Dimitar1 aTrott, Christian1 aTseng, Huan-Hsin1 aTureci, Esin1 aValinas, David, García1 aVallecorsa, Sofia1 aWever, Christopher1 aWojciechowski, Konrad1 aWu, Xiaodi1 aYoo, Shinjae1 aYoshioka, Nobuyuki1 aYu, Victor, Wen-zhe1 aYunoki, Seiji1 aZhuk, Sergiy1 aZubarev, Dmitry uhttps://arxiv.org/abs/2312.0973301172nas a2200133 4500008004100000245007300041210006900114260001400183520075600197100001600953700001700969700001500986856003701001 2023 eng d00aA quantum-classical performance separation in nonconvex optimization0 aquantumclassical performance separation in nonconvex optimizatio c11/1/20233 a

In this paper, we identify a family of nonconvex continuous optimization instances, each d-dimensional instance with 2d local minima, to demonstrate a quantum-classical performance separation. Specifically, we prove that the recently proposed Quantum Hamiltonian Descent (QHD) algorithm [Leng et al., arXiv:2303.01471] is able to solve any d-dimensional instance from this family using O˜(d3) quantum queries to the function value and O˜(d4) additional 1-qubit and 2-qubit elementary quantum gates. On the other side, a comprehensive empirical study suggests that representative state-of-the-art classical optimization algorithms/solvers (including Gurobi) would require a super-polynomial time to solve such optimization instances.

1 aLeng, Jiaqi1 aZheng, Yufan1 aWu, Xiaodi uhttps://arxiv.org/abs/2311.0081102012nas a2200145 4500008004100000245008500041210006900126260001300195520155500208100001801763700001701781700001601798700001501814856003701829 2023 eng d00aSimuQ: A Domain-Specific Language For Quantum Simulation With Analog Compilation0 aSimuQ A DomainSpecific Language For Quantum Simulation With Anal c3/5/20233 a

Hamiltonian simulation is one of the most promising applications of quantum computing. Recent experimental results suggest that continuous-time analog quantum simulation would be advantageous over gate-based digital quantum simulation in the Noisy Intermediate-Size Quantum (NISQ) machine era. However, programming such analog quantum simulators is much more challenging due to the lack of a unified interface between hardware and software, and the only few known examples are all hardware-specific. In this paper, we design and implement SimuQ, the first domain-specific language for Hamiltonian simulation that supports pulse-level compilation to heterogeneous analog quantum simulators. Specifically, in SimuQ, front-end users will specify the target Hamiltonian evolution with a Hamiltonian modeling language, and the programmability of analog simulators is specified through a new abstraction called the abstract analog instruction set by hardware providers. Through a solver-based compilation, SimuQ will generate the pulse-level instruction schedule on the target analog simulator for the desired Hamiltonian evolution, which has been demonstrated on pulse-controlled superconducting (Qiskit Pulse) and neutral-atom (QuEra Bloqade) quantum systems, as well as on normal circuit-based digital quantum machines. Moreover, we also demonstrate the advantage of analog compilation over digital compilation on IBM machines, the use of SimuQ for resource estimation for hypothetical machines, and a scalability test of SimuQ's compilation.

1 aPeng, Yuxiang1 aYoung, Jacob1 aLiu, Pengyu1 aWu, Xiaodi uhttps://arxiv.org/abs/2303.0277501748nas a2200133 4500008004100000245008100041210006900122260001400191520131400205100001601519700002701535700001501562856003701577 2022 eng d00aA Convergence Theory for Over-parameterized Variational Quantum Eigensolvers0 aConvergence Theory for Overparameterized Variational Quantum Eig c5/25/20223 a

The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Despite a lot of empirical studies and recent progress in theoretical understanding of VQE's optimization landscape, the convergence for optimizing VQE is far less understood. We provide the first rigorous analysis of the convergence of VQEs in the over-parameterization regime. By connecting the training dynamics with the Riemannian Gradient Flow on the unit-sphere, we establish a threshold on the sufficient number of parameters for efficient convergence, which depends polynomially on the system dimension and the spectral ratio, a property of the problem Hamiltonian, and could be resilient to gradient noise to some extent. We further illustrate that this overparameterization threshold could be vastly reduced for specific VQE instances by establishing an ansatz-dependent threshold paralleling our main result. We showcase that our ansatz-dependent threshold could serve as a proxy of the trainability of different VQE ansatzes without performing empirical experiments, which hence leads to a principled way of evaluating ansatz design. Finally, we conclude with a comprehensive empirical study that supports our theoretical findings

1 aYou, Xuchen1 aChakrabarti, Shouvanik1 aWu, Xiaodi uhttps://arxiv.org/abs/2205.1248101692nas a2200133 4500008004100000245007800041210006900119260001500188520126500203100001801468700002001486700001501506856003701521 2021 eng d00aAlgebraic Reasoning of Quantum Programs via Non-Idempotent Kleene Algebra0 aAlgebraic Reasoning of Quantum Programs via NonIdempotent Kleene c10/13/20213 a

We investigate the algebraic reasoning of quantum programs inspired by the success of classical program analysis based on Kleene algebra. One prominent example of such is the famous Kleene Algebra with Tests (KAT), which has furnished both theoretical insights and practical tools. The succinctness of algebraic reasoning would be especially desirable for scalable analysis of quantum programs, given the involvement of exponential-size matrices in most of the existing methods. A few key features of KAT including the idempotent law and the nice properties of classical tests, however, fail to hold in the context of quantum programs due to their unique quantum features, especially in branching. We propose the Non-idempotent Kleena Algebra (NKA) as a natural alternative and identify complete and sound semantic models for NKA as well as their appropriate quantum interpretations. In light of applications of KAT, we are able to demonstrate algebraic proofs in NKA of quantum compiler optimization and the normal form of quantum while-programs. Moreover, we extend NKA with Tests (i.e., NKAT), where tests model quantum predicates following the rules of effect algebra, and illustrate how to encode propositional quantum Hoare logic as NKAT theorems.

1 aPeng, Yuxiang1 aYing, Mingsheng1 aWu, Xiaodi uhttps://arxiv.org/abs/2110.0701801381nas a2200205 4500008004100000245004900041210004700090260001400137520077400151100002000925700001900945700001500964700002400979700001901003700001901022700002301041700001501064700001301079856008301092 2021 eng d00aEasyPQC: Verifying Post-Quantum Cryptography0 aEasyPQC Verifying PostQuantum Cryptography c9/20/20213 a

EasyCrypt is a formal verification tool used extensively for formalizing concrete security proofs of cryptographic constructions. However, the EasyCrypt formal logics consider only classical attackers, which means that post-quantum security proofs cannot be formalized and machine-checked with this tool. In this paper we prove that a natural extension of the EasyCrypt core logics permits capturing a wide class of post-quantum cryptography proofs, settling a question raised by (Unruh, POPL 2019). Leveraging our positive result, we implement EasyPQC, an extension of EasyCrypt for post-quantum security proofs, and use EasyPQC to verify post-quantum security of three classic constructions: PRF-based MAC, Full Domain Hash and GPV08 identity-based encryption.

1 aBarbosa, Manuel1 aBarthe, Gilles1 aFan, Xiong1 aGrégoire, Benjamin1 aHung, Shih-Han1 aKatz, Jonathan1 aStrub, Pierre-Yves1 aWu, Xiaodi1 aZhou, Li uhttps://quics.umd.edu/publications/easypqc-verifying-post-quantum-cryptography02180nas a2200181 4500008004100000245003100041210003100072260001200103520171100115100001901826700001301845700001701858700001701875700001701892700001501909700001901924856005501943 2021 eng d00aExpanding the VOQC Toolkit0 aExpanding the VOQC Toolkit c06/20213 a

voqc [Hietala et al. 2021b] (pronounced “vox”) is a compiler for quantum circuits, in the style of
tools like Qiskit [Aleksandrowicz et al. 2019], tket [Cambridge Quantum Computing Ltd 2019],
Quilc [Rigetti Computing 2019], and Cirq [Developers 2021]. What makes voqc different from these
tools is that it has been formally verified in the Coq proof assistant [Coq Development Team 2019].
voqc source programs are expressed in sqir, a simple quantum intermediate representation, which
has a precise mathematical semantics. We use Gallina, Coq’s programming language, to implement
voqc transformations over sqir programs, and use Coq to prove the source program’s semantics
are preserved. We then extract these Gallina definitions to OCaml, and compile the OCaml code to
a library that can operate on standard-formatted circuits.
voqc, and sqir, were built to be general-purpose. For example, while we originally designed sqir
for use in verified optimizations, we subsequently found sqir could also be suitable for writing, and
proving correct, source programs [Hietala et al. 2021a]. We have continued to develop the voqc
codebase to expand its reach and utility.
In this abstract, we present new extensions to voqc as an illustration of its flexibility. These
include support for calling voqc transformations from Python, added support for new gate sets
and optimizations, and the extension of our notion of correctness to include mapping-preservation,
which allows us to apply optimizations after mapping, reducing the cost introduced by making a
program conform to hardware constraints.

1 aHietala, Kesha1 aLi, Liyi1 aGaur, Akshaj1 aGreen, Aaron1 aRand, Robert1 aWu, Xiaodi1 aHicks, Michael uhttp://rand.cs.uchicago.edu/files/planqc_2021c.pdf01673nas a2200145 4500008004100000245006300041210006300104260001400167300001600181490000800197520125000205100001601455700001501471856004101486 2021 eng d00aExponentially Many Local Minima in Quantum Neural Networks0 aExponentially Many Local Minima in Quantum Neural Networks c10/5/2021 a12144-121550 v1393 a

Quantum Neural Networks (QNNs), or the so-called variational quantum circuits, are important quantum applications both because of their similar promises as classical neural networks and because of the feasibility of their implementation on near-term intermediate-size noisy quantum machines (NISQ). However, the training task of QNNs is challenging and much less understood. We conduct a quantitative investigation on the landscape of loss functions of QNNs and identify a class of simple yet extremely hard QNN instances for training. Specifically, we show for typical under-parameterized QNNs, there exists a dataset that induces a loss function with the number of spurious local minima depending exponentially on the number of parameters. Moreover, we show the optimality of our construction by providing an almost matching upper bound on such dependence. While local minima in classical neural networks are due to non-linear activations, in quantum neural networks local minima appear as a result of the quantum interference phenomenon. Finally, we empirically confirm that our constructions can indeed be hard instances in practice with typical gradient-based optimizers, which demonstrates the practical value of our findings. 

1 aYou, Xuchen1 aWu, Xiaodi uhttps://arxiv.org/pdf/2110.02479.pdf01742nas a2200169 4500008004100000245004400041210004400085260001500129520128400144100001301428700002301441700001901464700001801483700001501501700001901516856003701535 2021 eng d00aVerified Compilation of Quantum Oracles0 aVerified Compilation of Quantum Oracles c12/13/20213 a

Quantum algorithms often apply classical operations, such as arithmetic or predicate checks, over a quantum superposition of classical data; these so-called oracles are often the largest components of a quantum algorithm. To ease the construction of efficient, correct oracle functions, this paper presents VQO, a high-assurance framework implemented with the Coq proof assistant. The core of VQO is OQASM, the oracle quantum assembly language. OQASM operations move qubits among three different bases via the Quantum Fourier Transform and Hadamard operations, thus admitting important optimizations, but without inducing entanglement and the exponential blowup that comes with it. OQASM's design enabled us to prove correct VQO's compilers -- from a simple imperative language called OQIMP to OQASM, and from OQASM to SQIR, a general-purpose quantum assembly language -- and allowed us to efficiently test properties of OQASM programs using the QuickChick property-based testing framework. We have used VQO to implement oracles used in Shor's and Grover's algorithms, as well as several common arithmetic operators. VQO's oracles have performance comparable to those produced by Quipper, a state-of-the-art but unverified quantum programming platform.

1 aLi, Liyi1 aVoichick, Finnegan1 aHietala, Kesha1 aPeng, Yuxiang1 aWu, Xiaodi1 aHicks, Michael uhttps://arxiv.org/abs/2112.0670001145nas a2200169 4500008004100000245004600041210004400087260001500131490000600146520069700152100001900849700001700868700001900885700001500904700001900919856003700938 2021 eng d00aA Verified Optimizer for Quantum Circuits0 aVerified Optimizer for Quantum Circuits c11/12/20200 v53 a

We present VOQC, the first fully verified compiler for quantum circuits, written using the Coq proof assistant. Quantum circuits are expressed as programs in a simple, low-level language called SQIR, which is deeply embedded in Coq. Optimizations and other transformations are expressed as Coq functions, which are proved correct with respect to a semantics of SQIR programs. We evaluate VOQC's verified optimizations on a series of benchmarks, and it performs comparably to industrial-strength compilers. VOQC's optimizations reduce total gate counts on average by 17.7% on a benchmark of 29 circuit programs compared to a 10.7% reduction when using IBM's Qiskit compiler.

1 aHietala, Kesha1 aRand, Robert1 aHung, Shih-Han1 aWu, Xiaodi1 aHicks, Michael uhttps://arxiv.org/abs/1912.0225002263nas a2200145 4500008004100000245006800041210006700109260001400176520182500190100001902015700001202034700001902046700001502065856003702080 2020 eng d00aConstant-round Blind Classical Verification of Quantum Sampling0 aConstantround Blind Classical Verification of Quantum Sampling c12/8/20203 a

In a recent breakthrough, Mahadev constructed a classical verification of quantum computation (CVQC) protocol for a classical client to delegate decision problems in BQP to an untrusted quantum prover under computational assumptions. In this work, we explore further the feasibility of CVQC with the more general sampling problems in BQP and with the desirable blindness property. We contribute affirmative solutions to both as follows. (1) Motivated by the sampling nature of many quantum applications (e.g., quantum algorithms for machine learning and quantum supremacy tasks), we initiate the study of CVQC for quantum sampling problems (denoted by SampBQP). More precisely, in a CVQC protocol for a SampBQP problem, the prover and the verifier are given an input x∈{0,1}n and a quantum circuit C, and the goal of the classical client is to learn a sample from the output z←C(x) up to a small error, from its interaction with an untrusted prover. We demonstrate its feasibility by constructing a four-message CVQC protocol for SampBQP based on the quantum Learning With Error assumption. (2) The blindness of CVQC protocols refers to a property of the protocol where the prover learns nothing, and hence is blind, about the client's input. It is a highly desirable property that has been intensively studied for the delegation of quantum computation. We provide a simple yet powerful generic compiler that transforms any CVQC protocol to a blind one while preserving its completeness and soundness errors as well as the number of rounds. Applying our compiler to (a parallel repetition of) Mahadev's CVQC protocol for BQP and our CVQC protocol for SampBQP yields the first constant-round blind CVQC protocol for BQP and SampBQP respectively, with negligible completeness and soundness errors.

1 aChung, Kai-Min1 aLee, Yi1 aLin, Han-Hsuan1 aWu, Xiaodi uhttps://arxiv.org/abs/2012.0484801867nas a2200145 4500008004100000245007000041210006300111260001300174520141800187100001801605700001901623700002701642700001501669856003701684 2020 eng d00aOn the Principles of Differentiable Quantum Programming Languages0 aPrinciples of Differentiable Quantum Programming Languages c4/2/20203 a

Variational Quantum Circuits (VQCs), or the so-called quantum neural-networks, are predicted to be one of the most important near-term quantum applications, not only because of their similar promises as classical neural-networks, but also because of their feasibility on near-term noisy intermediate-size quantum (NISQ) machines. The need for gradient information in the training procedure of VQC applications has stimulated the development of auto-differentiation techniques for quantum circuits. We propose the first formalization of this technique, not only in the context of quantum circuits but also for imperative quantum programs (e.g., with controls), inspired by the success of differentiable programming languages in classical machine learning. In particular, we overcome a few unique difficulties caused by exotic quantum features (such as quantum no-cloning) and provide a rigorous formulation of differentiation applied to bounded-loop imperative quantum programs, its code-transformation rules, as well as a sound logic to reason about their correctness. Moreover, we have implemented our code transformation in OCaml and demonstrated the resource-efficiency of our scheme both analytically and empirically. We also conduct a case study of training a VQC instance with controls, which shows the advantage of our scheme over existing auto-differentiation for quantum circuits without controls.

1 aZhu, Shaopeng1 aHung, Shih-Han1 aChakrabarti, Shouvanik1 aWu, Xiaodi uhttps://arxiv.org/abs/2004.0112201173nas a2200157 4500008004100000245006400041210006400105260001500169490000600184520070600190100002700896700002300923700001700946700001500963856003700978 2020 eng d00aQuantum algorithms and lower bounds for convex optimization0 aQuantum algorithms and lower bounds for convex optimization c12/18/20190 v43 a

While recent work suggests that quantum computers can speed up the solution of semidefinite programs, little is known about the quantum complexity of more general convex optimization. We present a quantum algorithm that can optimize a convex function over an n-dimensional convex body using O~(n) queries to oracles that evaluate the objective function and determine membership in the convex body. This represents a quadratic improvement over the best-known classical algorithm. We also study limitations on the power of quantum computers for general convex optimization, showing that it requires Ω~(n−−√) evaluation queries and Ω(n−−√) membership queries.

1 aChakrabarti, Shouvanik1 aChilds, Andrew, M.1 aLi, Tongyang1 aWu, Xiaodi uhttps://arxiv.org/abs/1809.0173101588nas a2200157 4500008004100000245006600041210006600107260001400173490000800187520113200195100001701327700001701344700001701361700001501378856003701393 2020 eng d00aSimulating large quantum circuits on a small quantum computer0 aSimulating large quantum circuits on a small quantum computer c12/8/20200 v1253 a

Limited quantum memory is one of the most important constraints for near-term quantum devices. Understanding whether a small quantum computer can simulate a larger quantum system, or execute an algorithm requiring more qubits than available, is both of theoretical and practical importance. In this Letter, we introduce cluster parameters K and d of a quantum circuit. The tensor network of such a circuit can be decomposed into clusters of size at most d with at most K qubits of inter-cluster quantum communication. Our main result is a simulation scheme of any (K,d)-clustered quantum circuit on a d-qubit machine in time roughly 2O(K). An important application of our result is the simulation of clustered quantum systems---such as large molecules---that can be partitioned into multiple significantly smaller clusters with weak interactions among them. Another potential application is quantum optimization: we demonstrate numerically that variational quantum eigensolvers can still perform well when restricted to clustered circuits, thus making it feasible to study large quantum systems on small quantum devices.

1 aPeng, Tianyi1 aHarrow, Aram1 aOzols, Maris1 aWu, Xiaodi uhttps://arxiv.org/abs/1904.0010201601nas a2200145 4500008004100000245007200041210006900113260001500182520114400197100001701341700001801358700002701376700001501403856003701418 2020 eng d00aSublinear classical and quantum algorithms for general matrix games0 aSublinear classical and quantum algorithms for general matrix ga c12/11/20203 a

We investigate sublinear classical and quantum algorithms for matrix games, a fundamental problem in optimization and machine learning, with provable guarantees. Given a matrix A∈Rn×d, sublinear algorithms for the matrix game minx∈Xmaxy∈Yy⊤Ax were previously known only for two special cases: (1) Y being the ℓ1-norm unit ball, and (2) X being either the ℓ1- or the ℓ2-norm unit ball. We give a sublinear classical algorithm that can interpolate smoothly between these two cases: for any fixed q∈(1,2], we solve the matrix game where X is a ℓq-norm unit ball within additive error ε in time O~((n+d)/ε2). We also provide a corresponding sublinear quantum algorithm that solves the same task in time O~((n−−√+d−−√)poly(1/ε)) with a quadratic improvement in both n and d. Both our classical and quantum algorithms are optimal in the dimension parameters n and d up to poly-logarithmic factors. Finally, we propose sublinear classical and quantum algorithms for the approximate Carathéodory problem and the ℓq-margin support vector machines as applications.

1 aLi, Tongyang1 aWang, Chunhao1 aChakrabarti, Shouvanik1 aWu, Xiaodi uhttps://arxiv.org/abs/2012.0651902093nas a2200145 4500008004100000245008200041210006900123260001500192490000800207520163800215100002101853700002101874700001501895856003701910 2019 eng d00aLimitations of semidefinite programs for separable states and entangled games0 aLimitations of semidefinite programs for separable states and en c03/04/20190 v3663 a

Semidefinite programs (SDPs) are a framework for exact or approximate optimization that have widespread application in quantum information theory. We introduce a new method for using reductions to construct integrality gaps for SDPs. These are based on new limitations on the sum-of-squares (SoS) hierarchy in approximating two particularly important sets in quantum information theory, where previously no ω(1)-round integrality gaps were known: the set of separable (i.e. unentangled) states, or equivalently, the 2→4 norm of a matrix, and the set of quantum correlations; i.e. conditional probability distributions achievable with local measurements on a shared entangled state. In both cases no-go theorems were previously known based on computational assumptions such as the Exponential Time Hypothesis (ETH) which asserts that 3-SAT requires exponential time to solve. Our unconditional results achieve the same parameters as all of these previous results (for separable states) or as some of the previous results (for quantum correlations). In some cases we can make use of the framework of Lee-Raghavendra-Steurer (LRS) to establish integrality gaps for any SDP, not only the SoS hierarchy. Our hardness result on separable states also yields a dimension lower bound of approximate disentanglers, answering a question of Watrous and Aaronson et al. These results can be viewed as limitations on the monogamy principle, the PPT test, the ability of Tsirelson-type bounds to restrict quantum correlations, as well as the SDP hierarchies of Doherty-Parrilo-Spedalieri, Navascues-Pironio-Acin and Berta-Fawzi-Scholz.

1 aHarrow, Aram, W.1 aNatarajan, Anand1 aWu, Xiaodi uhttps://arxiv.org/abs/1612.0930602812nas a2200145 4500008004100000245005100041210005100092260001500143300001400158490000700172520241800179100001702597700001502614856003702629 2019 eng d00aQuantum query complexity of entropy estimation0 aQuantum query complexity of entropy estimation c10/16/2017 a2899-29210 v653 a

Estimation of Shannon and R´enyi entropies of unknown discrete distributions is a fundamental problem in statistical property testing and an active research topic in both theoretical computer science and information theory. Tight bounds on the number of samples to estimate these entropies have been established in the classical setting, while little is known about their quantum counterparts. In this paper, we give the first quantum algorithms for estimating α- R´enyi entropies (Shannon entropy being 1-Renyi entropy). In particular, we demonstrate a quadratic quantum speedup for Shannon entropy estimation and a generic quantum speedup for α-R´enyi entropy estimation for all α ≥ 0, including a tight bound for the collision-entropy (2-R´enyi entropy). We also provide quantum upper bounds for extreme cases such as the Hartley entropy (i.e., the logarithm of the support size of a distribution, corresponding to α = 0) and the min-entropy case (i.e., α = +∞), as well as the Kullback-Leibler divergence between two distributions. Moreover, we complement our results with quantum lower bounds on α-R´enyi entropy estimation for all α ≥ 0. Our approach is inspired by the pioneering work of Bravyi, Harrow, and Hassidim (BHH) [13] on quantum algorithms for distributional property testing, however, with many new technical ingredients. For Shannon entropy and 0-R´enyi entropy estimation, we improve the performance of the BHH framework, especially its error dependence, using Montanaro’s approach to estimating the expected output value of a quantum subroutine with bounded variance [41] and giving a fine-tuned error analysis. For general α-R´enyi entropy estimation, we further develop a procedure that recursively approximates α-R´enyi entropy for a sequence of αs, which is in spirit similar to a cooling schedule in simulated annealing. For special cases such as integer α ≥ 2 and α = +∞ (i.e., the min-entropy), we reduce the entropy estimation problem to the α-distinctness and the dlog ne-distinctness problems, respectively. We exploit various techniques to obtain our lower bounds for different ranges of α, including reductions to (variants of) existing lower bounds in quantum query complexity as well as the polynomial method inspired by the celebrated quantum lower bound for the collision problem.

1 aLi, Tongyang1 aWu, Xiaodi uhttps://arxiv.org/abs/1710.0602501810nas a2200169 4500008004100000245005600041210005600097260001500153490000700168520133300175100002701508700001801535700001701553700001801570700001501588856003701603 2019 eng d00aQuantum Wasserstein Generative Adversarial Networks0 aQuantum Wasserstein Generative Adversarial Networks c2019/10/310 v323 a

The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines. Inspired by previous studies on the adversarial training of classical and quantum generative models, we propose the first design of quantum Wasserstein Generative Adversarial Networks (WGANs), which has been shown to improve the robustness and the scalability of the adversarial training of quantum generative models even on noisy quantum hardware. Specifically, we propose a definition of the Wasserstein semimetric between quantum data, which inherits a few key theoretical merits of its classical counterpart. We also demonstrate how to turn the quantum Wasserstein semimetric into a concrete design of quantum WGANs that can be efficiently implemented on quantum machines. Our numerical study, via classical simulation of quantum systems, shows the more robust and scalable numerical performance of our quantum WGANs over other quantum GAN proposals. As a surprising application, our quantum WGAN has been used to generate a 3-qubit quantum circuit of ~50 gates that well approximates a 3-qubit 1-d Hamiltonian simulation circuit that requires over 10k gates using standard techniques.

1 aChakrabarti, Shouvanik1 aHuang, Yiming1 aLi, Tongyang1 aFeizi, Soheil1 aWu, Xiaodi uhttps://arxiv.org/abs/1911.0011101473nas a2200157 4500008004100000245008200041210006900123260001500192300001400207490000700221520099100228100001701219700002701236700001501263856003701278 2019 eng d00aSublinear quantum algorithms for training linear and kernel-based classifiers0 aSublinear quantum algorithms for training linear and kernelbased c04/03/2019 a3815-38240 v973 a

We investigate quantum algorithms for classification, a fundamental problem in machine learning, with provable guarantees. Given n d-dimensional data points, the state-of-the-art (and optimal) classical algorithm for training classifiers with constant margin runs in O~(n+d) time. We design sublinear quantum algorithms for the same task running in O~(n−−√+d−−√) time, a quadratic improvement in both n and d. Moreover, our algorithms use the standard quantization of the classical input and generate the same classical output, suggesting minimal overheads when used as subroutines for end-to-end applications. We also demonstrate a tight lower bound (up to poly-log factors) and discuss the possibility of implementation on near-term quantum machines. As a side result, we also give sublinear quantum algorithms for approximating the equilibria of n-dimensional matrix zero-sum games with optimal complexity Θ~(n−−√). 

1 aLi, Tongyang1 aChakrabarti, Shouvanik1 aWu, Xiaodi uhttps://arxiv.org/abs/1904.0227600982nas a2200157 4500008004100000245006700041210006700108260001500175520050800190100001900698700001700717700001900734700001500753700001900768856003700787 2019 eng d00aVerified Optimization in a Quantum Intermediate Representation0 aVerified Optimization in a Quantum Intermediate Representation c04/12/20193 a

We present sqire, a low-level language for quantum computing and verification. sqire uses a global register of quantum bits, allowing easy compilation to and from existing `quantum assembly' languages and simplifying the verification process. We demonstrate the power of sqire as an intermediate representation of quantum programs by verifying a number of useful optimizations, and we demonstrate sqire's use as a tool for general verification by proving several quantum programs correct.

1 aHietala, Kesha1 aRand, Robert1 aHung, Shih-Han1 aWu, Xiaodi1 aHicks, Michael uhttps://arxiv.org/abs/1904.0631901816nas a2200193 4500008004100000245007600041210006900117260001400186300001500200490000600215520125400221100001901475700001901494700001801513700002001531700001901551700001501570856003701585 2018 eng d00aQuantitative Robustness Analysis of Quantum Programs (Extended Version)0 aQuantitative Robustness Analysis of Quantum Programs Extended Ve c2018/12/1 aArticle 310 v33 a

Quantum computation is a topic of significant recent interest, with practical advances coming from both research and industry. A major challenge in quantum programming is dealing with errors (quantum noise) during execution. Because quantum resources (e.g., qubits) are scarce, classical error correction techniques applied at the level of the architecture are currently cost-prohibitive. But while this reality means that quantum programs are almost certain to have errors, there as yet exists no principled means to reason about erroneous behavior. This paper attempts to fill this gap by developing a semantics for erroneous quantum while-programs, as well as a logic for reasoning about them. This logic permits proving a property we have identified, called ε-robustness, which characterizes possible "distance" between an ideal program and an erroneous one. We have proved the logic sound, and showed its utility on several case studies, notably: (1) analyzing the robustness of noisy versions of the quantum Bernoulli factory (QBF) and quantum walk (QW); (2) demonstrating the (in)effectiveness of different error correction schemes on single-qubit errors; and (3) analyzing the robustness of a fault-tolerant version of QBF.

1 aHung, Shih-Han1 aHietala, Kesha1 aZhu, Shaopeng1 aYing, Mingsheng1 aHicks, Michael1 aWu, Xiaodi uhttps://arxiv.org/abs/1811.0358502409nas a2200157 4500008004100000245009100041210006900132520188600201100003302087700001602120700001702136700002402153700002202177700001502199856003702214 2018 eng d00aQuantum SDP Solvers: Large Speed-ups, Optimality, and Applications to Quantum Learning0 aQuantum SDP Solvers Large Speedups Optimality and Applications t3 a

We give two new quantum algorithms for solving semidefinite programs (SDPs) providing quantum speed-ups. We consider SDP instances with m constraint matrices, each of dimension n, rank r, and sparsity s. The first algorithm assumes an input model where one is given access to entries of the matrices at unit cost. We show that it has run time O~(s2(m−−√ε−10+n−−√ε−12)), where ε is the error. This gives an optimal dependence in terms of m,n and quadratic improvement over previous quantum algorithms when m≈n. The second algorithm assumes a fully quantum input model in which the matrices are given as quantum states. We show that its run time is O~(m−−√+poly(r))⋅poly(logm,logn,B,ε−1), with B an upper bound on the trace-norm of all input matrices. In particular the complexity depends only poly-logarithmically in n and polynomially in r. We apply the second SDP solver to the problem of learning a good description of a quantum state with respect to a set of measurements: Given m measurements and copies of an unknown state ρ, we show we can find in time m−−√⋅poly(logm,logn,r,ε−1) a description of the state as a quantum circuit preparing a density matrix which has the same expectation values as ρ on the m measurements, up to error ε. The density matrix obtained is an approximation to the maximum entropy state consistent with the measurement data considered in Jaynes' principle from statistical mechanics. As in previous work, we obtain our algorithm by "quantizing" classical SDP solvers based on the matrix multiplicative weight method. One of our main technical contributions is a quantum Gibbs state sampler for low-rank Hamiltonians with a poly-logarithmic dependence on its dimension, which could be of independent interest.

1 aBrandão, Fernando, G. S. L.1 aKalev, Amir1 aLi, Tongyang1 aLin, Cedric, Yen-Yu1 aSvore, Krysta, M.1 aWu, Xiaodi uhttps://arxiv.org/abs/1710.0258101895nas a2200157 4500008004100000245004900041210004800090260001500138520145500153100001801608700001901626700001801645700002201663700001501685856003701700 2017 eng d00aComputational Notions of Quantum Min-Entropy0 aComputational Notions of Quantum MinEntropy c2017/09/093 a

We initiate the study of computational entropy in the quantum setting. We investigate to what extent the classical notions of computational entropy generalize to the quantum setting, and whether quantum analogues of classical theorems hold. Our main results are as follows. (1) The classical Leakage Chain Rule for pseudoentropy can be extended to the case that the leakage information is quantum (while the source remains classical). Specifically, if the source has pseudoentropy at least k, then it has pseudoentropy at least k − ℓ conditioned on an ℓ- qubit leakage. (2) As an application of the Leakage Chain Rule, we construct the first quantum leakage-resilient stream-cipher in the bounded-quantum-storage model, assuming the existence of a quantum-secure pseudorandom generator. (3) We show that the general form of the classical Dense Model Theorem (interpreted as the equivalence between two definitions of pseudo-relativemin-entropy) does not extend to quantum states. Along the way, we develop quantum analogues of some classical techniques (e.g., the Leakage Simulation Lemma, which is proven by a Nonuniform Min-Max Theorem or Boosting). On the other hand, we also identify some classical techniques (e.g., Gap Amplification) that do not work in the quantum setting. Moreover, we introduce a variety of notions that combine quantum information and quantum complexity, and this raises several directions for future work.

1 aChen, Yi-Hsiu1 aChung, Kai-Min1 aLai, Ching-Yi1 aVadhan, Salil, P.1 aWu, Xiaodi uhttps://arxiv.org/abs/1704.0730902582nas a2200169 4500008004100000245010100041210006900142260001500211520202200226100003302248700001602281700001702297700002402314700002202338700001502360856003702375 2017 eng d00aExponential Quantum Speed-ups for Semidefinite Programming with Applications to Quantum Learning0 aExponential Quantum Speedups for Semidefinite Programming with A c2017/10/063 a

We give semidefinite program (SDP) quantum solvers with an exponential speed-up over classical ones. Specifically, we consider SDP instances with m constraint matrices of dimension n, each of rank at most r, and assume that the input matrices of the SDP are given as quantum states (after a suitable normalization). Then we show there is a quantum algorithm that solves the SDP feasibility problem with accuracy ǫ by using √ m log m · poly(log n,r, ǫ −1 ) quantum gates. The dependence on n provides an exponential improvement over the work of Brand ˜ao and Svore [6] and the work of van Apeldoorn et al. [23], and demonstrates an exponential quantum speed-up when m and r are small. We apply the SDP solver to the problem of learning a good description of a quantum state with respect to a set of measurements: Given m measurements and a supply of copies of an unknown state ρ, we show we can find in time √ m log m · poly(log n,r, ǫ −1 ) a description of the state as a quantum circuit preparing a density matrix which has the same expectation values as ρ on the m measurements up to error ǫ. The density matrix obtained is an approximation to the maximum entropy state consistent with the measurement data considered in Jaynes’ principle. As in previous work, we obtain our algorithm by “quantizing” classical SDP solvers based on the matrix multiplicative weight update method. One of our main technical contributions is a quantum Gibbs state sampler for low-rank Hamiltonians with a poly-logarithmic dependence on its dimension based on the techniques developed in quantum principal component analysis, which could be of independent interest. Our quantum SDP solver is different from previous ones in the following two aspects: (1) it follows from a zero-sum game approach of Hazan [11] of solving SDPs rather than the primal-dual approach by Arora and Kale [5]; and (2) it does not rely on any sparsity assumption of the input matrices.

1 aBrandão, Fernando, G. S. L.1 aKalev, Amir1 aLi, Tongyang1 aLin, Cedric, Yen-Yu1 aSvore, Krysta, M.1 aWu, Xiaodi uhttps://arxiv.org/abs/1710.0258101278nas a2200133 4500008004100000245005800041210005700099260001500156520087600171100001501047700001701062700001601079856004901095 2017 eng d00aRaz-McKenzie simulation with the inner product gadget0 aRazMcKenzie simulation with the inner product gadget c2017/01/283 a

In this note we show that the Raz-McKenzie simulation algorithm which lifts deterministic query lower bounds to deterministic communication lower bounds can be implemented for functions f composed with the Inner Product gadget 1ip(x, y) = P i xiyi mod 2 of logarithmic size. In other words, given a function f : {0, 1} n → {0, 1} with deterministic query complexity D(f), we show that the deterministic communication complexity of the composed function f ◦ 1 n ip is Θ(D(f) log n), where f ◦ 1 n ip(x, y) = f(1ip(x 1 , y 1 ), . . . , 1ip(x n , y n )) where x = (x 1 , . . . , x n ), y = (y 1 , . . . , y n ) and each x i and y i are O(log n) bit strings. In [RM97] and [GPW15], the simulation algorithm is implemented for functions composed with the Indexing gadget, where the size of the gadget is polynomial in the input length of the outer function f.

1 aWu, Xiaodi1 aYao, Penghui1 aYuen, Henry uhttps://eccc.weizmann.ac.il/report/2017/010/