mlsanityData to 7 Oct 2026

Quantum Neural Estimation of Entropies

arXiv 2307.01171 ↗First posted 3 Jul 2023quant-ph cond-mat.stat-mech cs.ITcited by 6

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Abstract (arXiv)

Entropy measures quantify the amount of information and correlation present in a quantum system. In practice, when the quantum state is unknown and only copies thereof are available, one must resort to the estimation of such entropy measures. Here we propose a variational quantum algorithm for estimating the von Neumann and R'enyi entropies, as well as the measured relative entropy and measured R'enyi relative entropy. Our approach first parameterizes a variational formula for the measure of interest by a quantum circuit and a classical neural network, and then optimizes the resulting objective over parameter space. Numerical simulations of our quantum algorithm are provided, using a noiseless quantum simulator. The algorithm provides accurate estimates of the various entropy measures for the examples tested, which renders it as a promising approach for usage in downstream tasks.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 30 of its 107 references that are in the map. First posted on arXiv 3 Jul 2023.

Builds on

30 of 107 references.

References · 30

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18 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 2.

6 papers.

  1. 2024Quantum Boltzmann machine learning of ground-state energiescited by 11
  2. 2023Multimodal deep representation learning for quantum cross-platform verificationcited by 9
  3. 2025Performance Guarantees for Quantum Neural Estimation of Entropiescited by 3
  4. 2025Estimating quantum relative entropies on quantum computerscited by 2
  5. 2026The Average Relative Entropy and Transpilation Depth determines the noise robustness in Variational Quantum Classifierscited by 0
  6. 2026Entropy Estimation in Multi-Qutrit Systems via Variational and Classical Neural Networkscited by 0