Discrete Quantum Markov Chains

A framework for finite-dimensional quantum Markov chains on Hilbert spaces is introduced. Quantum Markov chains generalize both classical Markov chains with possibly hidden states and existing models of quantum walks on finite graphs. Quantum Markov chains are based on Markov operations that may be applied to quantum systems and include quantum measurements, for example. It is proved that quantum Markov chains are asymptotically stationary and hence possess ergodic and entropic properties. With a quantum Markov chain one may associate a quantum Markov process, which is a stochastic process in the classical sense. Generalized Markov chains allow a representation with respect to a generalized Markov source model with definite (but possibly hidden) states relative to which observables give rise to classical stochastic processes. It is demonstrated that this model allows for observables to violate Bell’s inequality.

[1]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[2]  Alexander Schönhuth,et al.  Asymptotic Mean Stationarity of Sources With Finite Evolution Dimension , 2007, IEEE Transactions on Information Theory.

[3]  N. Bohr II - Can Quantum-Mechanical Description of Physical Reality be Considered Complete? , 1935 .

[4]  Michael Kuperberg,et al.  Markov Models , 2019, Earthquake Statistical Analysis through Multi-state Modeling.

[5]  Shun-ichi Amari,et al.  Identifiability of hidden Markov information sources and their minimum degrees of freedom , 1992, IEEE Trans. Inf. Theory.

[6]  Andrei Khrennikov,et al.  Interpretations of Probability , 1999 .

[7]  Gunter Ludwig,et al.  CHAPTER IV – The Physical Interpretation of Quantum Mechanics , 1968 .

[8]  Andris Ambainis,et al.  Quantum walks on graphs , 2000, STOC '01.

[9]  Robert M. Gray,et al.  Probability, Random Processes, And Ergodic Properties , 1987 .

[10]  I. Chuang,et al.  Quantum Computation and Quantum Information: Introduction to the Tenth Anniversary Edition , 2010 .

[11]  Julia Kempe,et al.  Quantum random walks: An introductory overview , 2003, quant-ph/0303081.

[12]  G. Roger,et al.  Experimental Test of Bell's Inequalities Using Time- Varying Analyzers , 1982 .

[13]  N. Bohr,et al.  II - Can Quantum-Mechanical Description of Physical Reality be Considered Complete? , 1935 .

[14]  Herbert Jaeger,et al.  Observable Operator Models for Discrete Stochastic Time Series , 2000, Neural Computation.

[15]  D. Blackwell,et al.  On the Identifiability Problem for Functions of Finite Markov Chains , 1957 .

[16]  S. Eddy Hidden Markov models. , 1996, Current opinion in structural biology.

[17]  Dit-Yan Yeung,et al.  Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making , 2001, Sequence Learning.

[18]  Robert K. Brayton,et al.  Stability of dynamical systems: A constructive approach , 1979 .

[19]  J. Bell On the Problem of Hidden Variables in Quantum Mechanics , 1966 .

[20]  O. Barndorff-Nielsen,et al.  On quantum statistical inference , 2003, quant-ph/0307189.

[21]  A. Kolmogoroff Grundbegriffe der Wahrscheinlichkeitsrechnung , 1933 .

[22]  A. Zeilinger,et al.  Quantum implications : essays in honour of David Bohm , 1988 .

[23]  R. Gray Entropy and Information Theory , 1990, Springer New York.

[24]  Peter W. Shor,et al.  Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1995, SIAM Rev..

[25]  P. Dirac,et al.  The physical interpretation of quantum mechanics , 1942 .

[26]  Peter W. Shor Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1999 .

[27]  Mark Jerrum,et al.  Approximate Counting, Uniform Generation and Rapidly Mixing Markov Chains , 1987, International Workshop on Graph-Theoretic Concepts in Computer Science.

[28]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.