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Quantum Measurement and Control in Quantum Machine Learning

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 4161

Special Issue Editors


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Guest Editor
Centre for Engineered Quantum Systems, University of Queensland, St Lucia, QLD 4072, Australia
Interests: quantum optics; quantum information; mesoscopic transport; nonlinear dynamics; ion-traps; atomic Bose—Einstein condensation; optomechanics; superconducting quantum circuits https://equs.org/users/dr-sally-shrapnel

E-Mail Website
Guest Editor
Centre for Engineered Quantum Systems, University of Queensland, St Lucia, QLD 4072, Australia
Interests: machine learning; causal relations; quantum foundations

Special Issue Information

Dear Colleagues,

Machine learning and optimum stochastic control share similar objectives: to modify the dynamics of a complex stochastic dynamical system using measurement mediated feedback, to minimize the cost function of the output. Quantum control is now a mature subject and includes, in addition to the analogue of classical measurement-based control, a number of uniquely quantum protocols based on coherent control. Noise will be significant and occur even at a temperature of zero. This is a feature that distinguishes classical from quantum machine learning. In this Special Issue, we request papers addressing the role of measurement/coherent quantum control for quantum machine learning. Topics will include:

  • Noisy intermediate scale quantum (NISQ) learning machines using quantum control for training.
  • Quantum thermodynamics (decoherence, entropy, free energy, irreversibility) in quantum machine learning.
  • Generalizations of the classical fluctuation theorems (e.g. Jaryznski equality) for open quantum systems subject to measurement, and their role in machine learning.
  • Coherent control schemes for quantum machine learning.
  • The role of quantum information in quantum machine learning (entanglement, decoherence, error correction, etc.)
  • Coherent Ising machines and similar models.
  • Quantum machine learning as large scale dissipative many-body systems.

Design protocols for dissipative quantum machine learning (e.g. SLH, QHDL).

Prof. Dr. Gerard Milburn
Dr. Sally Shrapnel
Guest Editors

Manuscript Submission Information

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Keywords

  • quantum control
  • machine learning
  • NISQ
  • decoherence
  • stochastic
  • fluctuations
  • Coherent Ising machines
  • SLH

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Published Papers (1 paper)

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Research

26 pages, 1468 KiB  
Article
Entanglement and Photon Anti-Bunching in Coupled Non-Degenerate Parametric Oscillators
by Yoshitaka Inui and Yoshihisa Yamamoto
Entropy 2021, 23(5), 624; https://doi.org/10.3390/e23050624 - 17 May 2021
Cited by 6 | Viewed by 3490
Abstract
We analytically and numerically show that the Hillery-Zubairy’s entanglement criterion is satisfied both below and above the threshold of coupled non-degenerate optical parametric oscillators (NOPOs) with strong nonlinear gain saturation and dissipative linear coupling. We investigated two cases: for large pump mode dissipation, [...] Read more.
We analytically and numerically show that the Hillery-Zubairy’s entanglement criterion is satisfied both below and above the threshold of coupled non-degenerate optical parametric oscillators (NOPOs) with strong nonlinear gain saturation and dissipative linear coupling. We investigated two cases: for large pump mode dissipation, below-threshold entanglement is possible only when the parametric interaction has an enough detuning among the signal, idler, and pump photon modes. On the other hand, for a large dissipative coupling, below-threshold entanglement is possible even when there is no detuning in the parametric interaction. In both cases, a non-Gaussian state entanglement criterion is satisfied even at the threshold. Recent progress in nano-photonic devices might make it possible to experimentally demonstrate this phase transition in a coherent XY machine with quantum correlations. Full article
(This article belongs to the Special Issue Quantum Measurement and Control in Quantum Machine Learning)
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