Quantum Channel Simulation and the Channel’s Smooth Max-Information

We study the general framework of quantum channel simulation, that is, the ability of a quantum channel to simulate another one using different classes of codes. First, we show that the minimum error of simulation and the one-shot quantum simulation cost under no-signalling assisted codes are given by semidefinite programs. Second, we introduce the channel’s smooth max-information, which can be seen as a one-shot generalization of the mutual information of a quantum channel. We provide an exact operational interpretation of the channel’s smooth max-information as the one-shot quantum simulation cost under no-signalling assisted codes, which significantly simplifies the study of channel simulation and provides insights and bounds for the case under entanglement-assisted codes. Third, we derive the asymptotic equipartition property of the channel’s smooth max-information; i.e., it converges to the quantum mutual information of the channel in the independent and identically distributed asymptotic limit. This implies the quantum reverse Shannon theorem in the presence of no-signalling correlations. Finally, we explore the simulation cost of various quantum channels.

[1]  A. Kitaev Quantum computations: algorithms and error correction , 1997 .

[2]  Masahito Hayashi,et al.  Information Spectrum Approach to Second-Order Coding Rate in Channel Coding , 2008, IEEE Transactions on Information Theory.

[3]  William Matthews,et al.  On the Power of PPT-Preserving and Non-Signalling Codes , 2014, IEEE Transactions on Information Theory.

[4]  Mark M. Wilde,et al.  Strong Converse Exponents for a Quantum Channel Discrimination Problem and Quantum-Feedback-Assisted Communication , 2014, Communications in Mathematical Physics.

[5]  Debbie W. Leung,et al.  Zero-Error Channel Capacity and Simulation Assisted by Non-Local Correlations , 2010, IEEE Transactions on Information Theory.

[6]  Mark M. Wilde,et al.  Multiplicativity of Completely Bounded p-Norms Implies a Strong Converse for Entanglement-Assisted Capacity , 2013, ArXiv.

[7]  Joseph M. Renes,et al.  Noisy Channel Coding via Privacy Amplification and Information Reconciliation , 2010, IEEE Transactions on Information Theory.

[8]  Nilanjana Datta,et al.  One-Shot Entanglement-Assisted Quantum and Classical Communication , 2011, IEEE Transactions on Information Theory.

[9]  Mark M. Wilde,et al.  On the second-order asymptotics for entanglement-assisted communication , 2014, Quantum Inf. Process..

[10]  Nilanjana Datta,et al.  Min- and Max-Relative Entropies and a New Entanglement Monotone , 2008, IEEE Transactions on Information Theory.

[11]  Runyao Duan,et al.  On the quantum no-signalling assisted zero-error classical simulation cost of non-commutative bipartite graphs , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[12]  N. Datta,et al.  Approaches for approximate additivity of the Holevo information of quantum channels , 2017, Physical Review A.

[13]  Xin Wang,et al.  Using and reusing coherence to realize quantum processes , 2018, Quantum.

[14]  Anton van den Hengel,et al.  Semidefinite Programming , 2014, Computer Vision, A Reference Guide.

[15]  Runyao Duan,et al.  Semidefinite Programming Strong Converse Bounds for Classical Capacity , 2016, IEEE Transactions on Information Theory.

[16]  Rahul Jain,et al.  Partially Smoothed Information Measures , 2018, IEEE Transactions on Information Theory.

[17]  Xin Wang,et al.  On Converse Bounds for Classical Communication Over Quantum Channels , 2017, IEEE Transactions on Information Theory.

[18]  Claude E. Shannon,et al.  The zero error capacity of a noisy channel , 1956, IRE Trans. Inf. Theory.

[19]  Dave Touchette,et al.  Smooth Entropy Bounds on One-Shot Quantum State Redistribution , 2014, IEEE Transactions on Information Theory.

[20]  Mark M. Wilde,et al.  Cost of quantum entanglement simplified , 2020, Physical review letters.

[21]  Eric M. Rains A semidefinite program for distillable entanglement , 2001, IEEE Trans. Inf. Theory.

[22]  Nilanjana Datta,et al.  Max- Relative Entropy of Entanglement, alias Log Robustness , 2008, 0807.2536.

[23]  Xin Wang,et al.  Semidefinite Programming Converse Bounds for Quantum Communication , 2017, IEEE Transactions on Information Theory.

[24]  Runyao Duan,et al.  No-Signalling-Assisted Zero-Error Capacity of Quantum Channels and an Information Theoretic Interpretation of the Lovász Number , 2014, IEEE Transactions on Information Theory.

[25]  A. Uhlmann The "transition probability" in the state space of a ∗-algebra , 1976 .

[26]  Gilad Gour,et al.  Comparison of Quantum Channels by Superchannels , 2018, IEEE Transactions on Information Theory.

[27]  Mario Berta,et al.  Entanglement cost of quantum channels , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

[28]  H. Vincent Poor,et al.  Channel Coding Rate in the Finite Blocklength Regime , 2010, IEEE Transactions on Information Theory.

[29]  William Matthews,et al.  Finite Blocklength Converse Bounds for Quantum Channels , 2012, IEEE Transactions on Information Theory.

[30]  Xin Wang,et al.  Exact entanglement cost of quantum states and channels under PPT-preserving operations , 2018, ArXiv.

[31]  R. Werner,et al.  Tema con variazioni: quantum channel capacity , 2003, quant-ph/0311037.

[32]  R. Renner,et al.  The Quantum Reverse Shannon Theorem Based on One-Shot Information Theory , 2009, 0912.3805.

[33]  Andreas J. Winter,et al.  Tight Uniform Continuity Bounds for Quantum Entropies: Conditional Entropy, Relative Entropy Distance and Energy Constraints , 2015, ArXiv.

[34]  G. D’Ariano,et al.  Transforming quantum operations: Quantum supermaps , 2008, 0804.0180.

[35]  William Matthews,et al.  A Linear Program for the Finite Block Length Converse of Polyanskiy–Poor–Verdú Via Nonsignaling Codes , 2011, IEEE Transactions on Information Theory.

[36]  Joseph M. Renes,et al.  One-Shot Lossy Quantum Data Compression , 2013, IEEE Transactions on Information Theory.

[37]  Ashish V. Thapliyal,et al.  Entanglement-Assisted Classical Capacity of Noisy Quantum Channels , 1999, Physical Review Letters.

[38]  Rahul Jain,et al.  New One Shot Quantum Protocols With Application to Communication Complexity , 2016, IEEE Transactions on Information Theory.

[39]  A. Winter,et al.  Resource theory of coherence: Beyond states , 2017, 1704.03710.

[40]  John Watrous,et al.  Semidefinite Programs for Completely Bounded Norms , 2009, Theory Comput..

[41]  F. Hiai,et al.  The proper formula for relative entropy and its asymptotics in quantum probability , 1991 .

[42]  M. Tomamichel A framework for non-asymptotic quantum information theory , 2012, 1203.2142.

[43]  Renato Renner,et al.  Smooth Max-Information as One-Shot Generalization for Mutual Information , 2013, IEEE Transactions on Information Theory.

[44]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[45]  Christoph Hirche,et al.  Amortized channel divergence for asymptotic quantum channel discrimination , 2018, Letters in Mathematical Physics.

[46]  Matthias Christandl,et al.  Postselection technique for quantum channels with applications to quantum cryptography. , 2008, Physical review letters.

[47]  Mark M. Wilde,et al.  Entropy of a Quantum Channel: Definition, Properties, and Application , 2018, 2020 IEEE International Symposium on Information Theory (ISIT).

[48]  Renato Renner,et al.  Security of quantum key distribution , 2005, Ausgezeichnete Informatikdissertationen.

[49]  Peter W. Shor,et al.  Entanglement-assisted capacity of a quantum channel and the reverse Shannon theorem , 2001, IEEE Trans. Inf. Theory.

[50]  Nilanjana Datta,et al.  Distilling entanglement from arbitrary resources , 2010, 1006.1896.

[51]  Andreas J. Winter,et al.  The Quantum Reverse Shannon Theorem and Resource Tradeoffs for Simulating Quantum Channels , 2009, IEEE Transactions on Information Theory.

[52]  Runyao Duan,et al.  Non-Asymptotic Entanglement Distillation , 2017, IEEE Transactions on Information Theory.