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Quantum State Reconstruction From Incomplete Data

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arxiv quant-ph/9805020 v1 pith:HGU4EFX2 submitted 1998-05-07 quant-ph

classification quant-ph
keywords quantumdataidenticallypreparedstatessystemsestimationfinite
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Knowing and guessing, these are two essential epistemological pillars in the theory of quantum-mechanical measurement. As formulated quantum mechanics is a statistical theory. In general, a priori unknown states can be completely determined only when measurements on infinite ensembles of identically prepared quantum systems are performed. But how one can estimate (guess) quantum state when just incomplete data are available (known)? What is the most reliable estimation based on a given measured data? What is the optimal measurement providing only a finite number of identically prepared quantum objects are available? These are some of the questions we address. We present several schemes for a reconstruction of states of quantum systems from measured data: (1) We show how the maximum entropy (MaxEnt) principle can be efficiently used for an estimation of quantum states on incomplete observation levels. (2) We show how Bayesian inference can be used for reconstruction of quantum states when only a finite number of identically prepared systems are measured. (3) We describe the optimal generalized measurement of a finite number of identically prepared quantum systems which results in the estimation of a quantum state with the highest fidelity.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Maximum channel entropy principle and microcanonical channels

    quant-ph 2025-08 unverdicted novelty 7.0 of 10

    A maximum-entropy principle for quantum channels yields thermal channels with exponential form, analogous to thermal states.

  2. Thermalization with partial information

    quant-ph 2025-08 unverdicted novelty 6.0 of 10

    A maximum channel entropy principle, backed by a microcanonical-style derivation, identifies the canonical noisy channel that models thermalization under partial information.

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