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Markov processes in Hilbert space and continuous spontaneous localization of systems of identical particles

Ghirardi, Gian Carlo, Pearle, Philip, Rimini, Alberto·1990·Physical Review A

Abstract

Stochastic differential equations describing the Markovian evolution of state vectors in the quantum Hilbert space are studied as possible expressions of a universal dynamical principle. The general features of the considered class of equations as well as their dynamical consequences are investigated in detail. The stochastic evolution is proved to induce continuous dynamical reduction of the state vector onto mutually orthogonal subspaces. A specific choice, expressed in terms of creation and annihilation operators, of the operators defining the Markov process is then proved to be appropriate to describe continuous spontaneous localization of systems of identical particles. The dynamics obtained in such a way leaves practically unaffected the standard quantum evolution of microscopic systems and induces a very rapid suppression of coherence among macroscopically distinguishable states. The classical behavior of macroscopic objects as well as the reduction of the wave packet in a quantum measurement process can be consistently derived from the postulated universal dynamical principle.

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Metadata

Authors
Ghirardi, G. C., Pearle, P., & Rimini, A.
Venue
Physical Review A
Published
1990-07-01
Volume
42
Issue
1
Pages
78-89
ISSN
  • 0556-2791 (ISSN-L)
  • 1050-2947
  • 1094-1622
OpenAlex
W2058509714
Cited by
1,136
References
2
FWCI
9.50
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Markov processes in Hilbert space and continuous spontaneous localization of systems of identical particles. https://app.cochat.ai/papers/10.1103/physreva.42.78
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Ghirardi, G. C., Pearle, P., & Rimini, A. (1990). Markov processes in Hilbert space and continuous spontaneous localization of systems of identical particles. Physical Review A. https://doi.org/10.1103/physreva.42.78

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.