Optimizing Quantum Models of Classical Channels: The Reverse Holevo Problem
| Autor: | John R. Mahoney, Cina Aghamohammadi, Samuel P. Loomis, James P. Crutchfield |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Quantum information Computer science Computer Science - Information Theory Transmission rate Fluids & Plasmas FOS: Physical sciences Machine Learning (stat.ML) Quantum channel Quantum entanglement Mathematical Sciences quant-ph Statistics - Machine Learning Quantum state cs.IT Classical channel Statistical physics math.IT cond-mat.stat-mech Quantum Condensed Matter - Statistical Mechanics Mathematical Physics Computer Science::Information Theory Quantum Physics Statistical Mechanics (cond-mat.stat-mech) Information Theory (cs.IT) Statistical and Nonlinear Physics Complexity stat.ML Distribution (mathematics) Physical Sciences Quantum Physics (quant-ph) Communication channel |
| Zdroj: | Journal of Statistical Physics, vol 181, iss 5 |
| Popis: | Given a classical channel---a stochastic map from inputs to outputs---the input can often be transformed to an intermediate variable that is informationally smaller than the input. The new channel accurately simulates the original but at a smaller transmission rate. Here, we examine this procedure when the intermediate variable is a quantum state. We determine when and how well quantum simulations of classical channels may improve upon the minimal rates of classical simulation. This inverts Holevo's original question of quantifying the capacity of quantum channels with classical resources. We also show that this problem is equivalent to another, involving the local generation of a distribution from common entanglement. Comment: 13 pages, 6 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/qfact.htm; substantially updated from v1 |
| Databáze: | OpenAIRE |
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