Challenges for Reinforcement Learning in Quantum Circuit Design

Philipp Altmann, Jonas Stein, Michael Kölle, Adelina Bärligea, Thomas Gabor +3 more
12/18/2023
quant-phcs.LG

No official code found. Best implementation: philippaltmann/QCD (12 stars, MIT, last commit 2024-07-29).

Abstract

Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. This work considers the latter, leveraging reinforcement learning (RL) to improve quantum circuit design (QCD), which we formalize by a set of generic objectives. Furthermore, we propose qcd-gym, a concrete framework formalized as a Markov decision process, to enable learning policies capable of controlling a universal set of continuously parameterized quantum gates. Finally, we provide benchmark comparisons to assess the shortcomings and strengths of current state-of-the-art RL algorithms.

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Code Implementations(1)

philippaltmann/QCDCommunity100%
MIT

Quantum Circuit Designer: A gymnasium-based set of environments for benchmarking reinforcement learning for quantum circuit design.

122Shell, PythonAug 8, 20232 years agoMIT
benchmarkcircuit-designgymnasiumquantum-computingreinforcement-learning

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Cite this paper

@article{altmann2023challenges,
  title  = {Challenges for Reinforcement Learning in Quantum Circuit Design},
  author = {Philipp Altmann and Jonas Stein and Michael Kölle and Adelina Bärligea and Thomas Gabor and Thomy Phan and Sebastian Feld and Claudia Linnhoff-Popien},
  year   = {2023},
  eprint = {2312.11337},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.11337v3}
}

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