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.

}, doi = {https://doi.org/10.1007/s00220-019-03382-y}, url = {https://arxiv.org/abs/1612.09306}, author = {Aram W. Harrow and Anand Natarajan and Xiaodi Wu} } @article {2377, title = {Simulating large quantum circuits on a small quantum computer}, year = {2019}, month = {03/29/2019}, abstract = {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.

}, url = {https://arxiv.org/abs/1904.00102}, author = {Tianyi Peng and Aram Harrow and Maris Ozols and Xiaodi Wu} } @article {2376, title = {Sublinear quantum algorithms for training linear and kernel-based classifiers}, year = {2019}, month = {04/03/2019}, abstract = {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\−\−\√).\

}, url = {https://arxiv.org/abs/1904.02276}, author = {Tongyang Li and Shouvanik Chakrabarti and Xiaodi Wu} } @article {2404, title = {Verified Optimization in a Quantum Intermediate Representation}, year = {2019}, month = {04/12/2019}, abstract = {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 {\textquoteleft}quantum assembly\&$\#$39; languages and simplifying the verification process. We demonstrate the power of sqire as an intermediate representation of quantum programs by verifying a number of useful optimizations, and we demonstrate sqire\&$\#$39;s use as a tool for general verification by proving several quantum programs correct.

}, url = {https://arxiv.org/abs/1904.06319}, author = {Kesha Hietala and Robert Rand and Shih-Han Hung and Xiaodi Wu and Michael Hicks} } @article {2306, title = {Quantitative Robustness Analysis of Quantum Programs (Extended Version)}, journal = {Proc. ACM Program. Lang.}, volume = {3}, year = {2018}, month = {2018/12/1}, pages = {Article 31}, abstract = {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.

}, doi = {https://doi.org/10.1145/3290344}, url = {https://arxiv.org/abs/1811.03585}, author = {Shih-Han Hung and Kesha Hietala and Shaopeng Zhu and Mingsheng Ying and Michael Hicks and Xiaodi Wu} } @article {2207, title = {Quantum algorithms and lower bounds for convex optimization}, year = {2018}, abstract = {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.

}, url = {https://arxiv.org/abs/1809.01731}, author = {Shouvanik Chakrabarti and Andrew M. Childs and Tongyang Li and Xiaodi Wu} } @article {2309, title = {Quantum SDP Solvers: Large Speed-ups, Optimality, and Applications to Quantum Learning}, year = {2018}, abstract = {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\&$\#$39; 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.

}, url = {https://arxiv.org/abs/1710.02581}, author = {Fernando G. S. L. Brand{\~a}o and Amir Kalev and Tongyang Li and Cedric Yen-Yu Lin and Krysta M. Svore and Xiaodi Wu} } @article {2056, title = {Computational Notions of Quantum Min-Entropy}, year = {2017}, month = {2017/09/09}, abstract = {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.

}, url = {https://arxiv.org/abs/1704.07309}, author = {Yi-Hsiu Chen and Kai-Min Chung and Ching-Yi Lai and Salil P. Vadhan and Xiaodi Wu} } @article {2106, title = {Exponential Quantum Speed-ups for Semidefinite Programming with Applications to Quantum Learning}, year = {2017}, month = {2017/10/06}, abstract = {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.

}, url = {https://arxiv.org/abs/1710.02581}, author = {Fernando G. S. L. Brand{\~a}o and Amir Kalev and Tongyang Li and Cedric Yen-Yu Lin and Krysta M. Svore and Xiaodi Wu} } @article {2105, title = {Quantum query complexity of entropy estimation}, year = {2017}, month = {2017/10/16}, abstract = {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.

}, url = {https://arxiv.org/abs/1710.06025}, author = {Tongyang Li and Xiaodi Wu} } @article {1934, title = {Raz-McKenzie simulation with the inner product gadget}, journal = {Electronic Colloquium on Computational Complexity (ECCC)}, year = {2017}, month = {2017/01/28}, abstract = {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 {\textopenbullet} 1 n ip is Θ(D(f) log n), where f {\textopenbullet} 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.

}, url = {https://eccc.weizmann.ac.il/report/2017/010/}, author = {Xiaodi Wu and Penghui Yao and Henry Yuen} }