Sample Efficient Identity Testing and Independence Testing of Quantum States

In this paper, we study the quantum identity testing problem, i.e., testing whether two given quantum states are identical, and quantum independence testing problem, i.e., testing whether a given multipartite quantum state is in tensor product form. For the quantum identity testing problem of D(C) system, we provide a deterministic measurement scheme that uses O( d 2 2 ) copies via independent measurements with d being the dimension of the state and being the additive error. For the independence testing problem D(C1 ⊗ C2 ⊗ · · · ⊗ Cm) system, we show that the sample complexity is Θ̃( Π m i=1di 2 ) via collective measurements, and O( Πi=1d 2 i 2 ) via independent measurements. If randomized choice of independent measurements are allowed, the sample complexity is Θ( d 3/2 2 ) for the quantum identity testing problem, and Θ̃( Π m i=1d 3/2 i 2 ) for the quantum independence testing problem. 2012 ACM Subject Classification Theory of computation → Quantum computation theory; Mathematics of computing → Probability and statistics

[1]  Ronitt Rubinfeld,et al.  Self-testing polynomial functions efficiently and over rational domains , 1992, SODA '92.

[2]  P. Oscar Boykin,et al.  A New Proof for the Existence of Mutually Unbiased Bases , 2002, Algorithmica.

[3]  J. Eisert,et al.  Reliable quantum certification of photonic state preparations , 2014, Nature Communications.

[4]  Nengkun Yu Quantum Closeness Testing: A Streaming Algorithm and Applications , 2019, 1904.03218.

[5]  Ronitt Rubinfeld,et al.  Testing that distributions are close , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[6]  Ryan O'Donnell,et al.  Quantum Spectrum Testing , 2015, Communications in Mathematical Physics.

[7]  Ilias Diakonikolas,et al.  Sample-Optimal Density Estimation in Nearly-Linear Time , 2015, SODA.

[8]  Scott Aaronson,et al.  Shadow tomography of quantum states , 2017, Electron. Colloquium Comput. Complex..

[9]  Ronitt Rubinfeld,et al.  Approximating and testing k-histogram distributions in sub-linear time , 2012, PODS '12.

[10]  Luc Devroye,et al.  Combinatorial methods in density estimation , 2001, Springer series in statistics.

[11]  Avinatan Hassidim,et al.  Quantum Algorithms for Testing Properties of Distributions , 2009, IEEE Transactions on Information Theory.

[12]  Ryan O'Donnell,et al.  Efficient quantum tomography , 2015, STOC.

[13]  Constantinos Daskalakis,et al.  Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing , 2016, COLT.

[14]  Daniel M. Kane,et al.  Robust Estimators in High Dimensions without the Computational Intractability , 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

[15]  S. Chaturvedi Mutually unbiased bases , 2002 .

[16]  Yanjun Han,et al.  Minimax Estimation of Functionals of Discrete Distributions , 2014, IEEE Transactions on Information Theory.

[17]  Yi-Kai Liu,et al.  Direct fidelity estimation from few Pauli measurements. , 2011, Physical review letters.

[18]  Ronitt Rubinfeld,et al.  The complexity of approximating entropy , 2002, STOC '02.

[19]  Aaron B. Wagner,et al.  Estimating Quantum Entropy , 2017, IEEE Journal on Selected Areas in Information Theory.

[20]  M. Kus,et al.  Concurrence of mixed multipartite quantum States. , 2004, Physical review letters.

[21]  I. N. Sanov On the probability of large deviations of random variables , 1958 .

[22]  Ronitt Rubinfeld Taming big probability distributions , 2012, XRDS.

[23]  Jaikumar Radhakrishnan,et al.  A lower bound for the bounded round quantum communication complexity of set disjointness , 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..

[24]  Yu Cheng,et al.  High-Dimensional Robust Mean Estimation in Nearly-Linear Time , 2018, SODA.

[25]  Gregory Valiant,et al.  Instance optimal learning of discrete distributions , 2016, STOC.

[26]  Scott Aaronson,et al.  The learnability of quantum states , 2006, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[27]  Ronald de Wolf,et al.  A Survey of Quantum Property Testing , 2013, Theory Comput..

[28]  Ronitt Rubinfeld,et al.  Testing Shape Restrictions of Discrete Distributions , 2015, Theory of Computing Systems.

[29]  Oded Goldreich,et al.  Introduction to Property Testing , 2017 .

[30]  Alon Orlitsky,et al.  Competitive Closeness Testing , 2011, COLT.

[31]  Dave Touchette,et al.  Exponential separation of quantum communication and classical information , 2016, STOC.

[32]  Gregory Valiant,et al.  Estimating the unseen: an n/log(n)-sample estimator for entropy and support size, shown optimal via new CLTs , 2011, STOC '11.

[33]  Isaac L. Chuang,et al.  Quantum Computation and Quantum Information (10th Anniversary edition) , 2011 .

[34]  Ilias Diakonikolas,et al.  Optimal Algorithms for Testing Closeness of Discrete Distributions , 2013, SODA.

[35]  Ashley Montanaro,et al.  Testing Product States, Quantum Merlin-Arthur Games and Tensor Optimization , 2010, JACM.

[36]  Daniel M. Kane,et al.  Near-Optimal Closeness Testing of Discrete Histogram Distributions , 2017, ICALP.

[37]  Guy N. Rothblum,et al.  Gentle measurement of quantum states and differential privacy , 2019, Electron. Colloquium Comput. Complex..

[38]  Ronitt Rubinfeld,et al.  Testing Properties of Collections of Distributions , 2013, Theory Comput..

[39]  Stephen Becker,et al.  Quantum state tomography via compressed sensing. , 2009, Physical review letters.

[40]  Daniel M. Kane,et al.  Testing Conditional Independence of Discrete Distributions , 2017, 2018 Information Theory and Applications Workshop (ITA).

[41]  Dana Ron,et al.  Property testing and its connection to learning and approximation , 1998, JACM.

[42]  Rocco A. Servedio,et al.  Testing k-Modal Distributions: Optimal Algorithms via Reductions , 2011, SODA.

[43]  Daniel M. Kane,et al.  Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions , 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.

[44]  Sebastien Bubeck,et al.  Entanglement is Necessary for Optimal Quantum Property Testing , 2020, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS).

[45]  Ronitt Rubinfeld,et al.  Robust Characterizations of Polynomials with Applications to Program Testing , 1996, SIAM J. Comput..

[46]  Daniel M. Kane,et al.  A New Approach for Testing Properties of Discrete Distributions , 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

[47]  Liam Paninski,et al.  A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data , 2008, IEEE Transactions on Information Theory.

[48]  Gregory Valiant,et al.  An Automatic Inequality Prover and Instance Optimal Identity Testing , 2014, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science.

[49]  Stephen E. Fienberg,et al.  Testing Statistical Hypotheses , 2005 .

[50]  Clément L. Canonne,et al.  A Survey on Distribution Testing: Your Data is Big. But is it Blue? , 2020, Electron. Colloquium Comput. Complex..

[51]  Ryan O'Donnell,et al.  Efficient quantum tomography II , 2015, STOC.

[52]  Vladislav Voroninski Quantum Tomography From Few Full-Rank Observables , 2013, ArXiv.

[53]  Constantinos Daskalakis,et al.  Testing Ising Models , 2016, IEEE Transactions on Information Theory.

[54]  Ronitt Rubinfeld,et al.  Sublinear algorithms for testing monotone and unimodal distributions , 2004, STOC '04.

[55]  Paul Valiant Testing symmetric properties of distributions , 2008, STOC '08.

[56]  Paul Valiant,et al.  Estimating the Unseen , 2013, NIPS.

[57]  Ronitt Rubinfeld,et al.  Testing random variables for independence and identity , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

[58]  Yihong Wu,et al.  Minimax Rates of Entropy Estimation on Large Alphabets via Best Polynomial Approximation , 2014, IEEE Transactions on Information Theory.

[59]  Steven T. Flammia,et al.  Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators , 2012, 1205.2300.

[60]  Ryan O'Donnell,et al.  Lower Bounds for Testing Complete Positivity and Quantum Separability , 2019, LATIN.

[61]  Ryan O'Donnell,et al.  Quantum state certification , 2017, STOC.

[62]  Dana Ron,et al.  On Testing Expansion in Bounded-Degree Graphs , 2000, Studies in Complexity and Cryptography.

[63]  Daniel M. Kane,et al.  Sharp Bounds for Generalized Uniformity Testing , 2017, Electron. Colloquium Comput. Complex..

[64]  Seshadhri Comandur,et al.  Testing Expansion in Bounded Degree Graphs , 2007, Electron. Colloquium Comput. Complex..

[65]  Constantinos Daskalakis,et al.  Optimal Testing for Properties of Distributions , 2015, NIPS.

[66]  Ashwin Nayak,et al.  Online learning of quantum states , 2018, NeurIPS.

[67]  Holger Rauhut,et al.  Low rank matrix recovery from rank one measurements , 2014, ArXiv.

[68]  D. Gross,et al.  Schur–Weyl Duality for the Clifford Group with Applications: Property Testing, a Robust Hudson Theorem, and de Finetti Representations , 2017, Communications in Mathematical Physics.

[69]  Xiaodi Wu,et al.  Sample-Optimal Tomography of Quantum States , 2015, IEEE Transactions on Information Theory.

[70]  Tongyang Li,et al.  Distributional property testing in a quantum world , 2019, ITCS.

[71]  David Poulin,et al.  Practical characterization of quantum devices without tomography. , 2011, Physical review letters.

[72]  Gregory Valiant,et al.  The Power of Linear Estimators , 2011, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science.