Quantum control of solid state qubits via Reinforcement Learning

Abstract

The use of reinforcement learning (RL) in the design of quantum control strategies for pulse level control of NISQ era quantum devices is attracting growing interest. Model-free RL, not requiring preexisting knowledge of the physical system, acts as a black-box optimization tool well suited for noisy devices. In this work, we provide a simulation-based analysis of the performance of model-free RL for state preparation and qubit gate design in a challenging setup: indirect control of NV nuclear spins in a spin-bath. In particular, the spin-bath is modeled via the generalized cluster correlation expansion accounting for magnetic impurities and intra-bath interactions. We analyze the performance of the RL agents by performing simulations with minimally observable environments and noisy, sparse rewards as introduced in (Phys. Rev. X 12, 011059), and compare them to other state-of-the-art black-box optimization tools.

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BibTeX RIS

BibTeX

@misc{herb2026quantum,
  title = {{Quantum control of solid state qubits via Reinforcement Learning}},
  author = {Dennis Herb and Mirko Rossini and Sabine Wölk and Joachim Ankerhold and Dominik Maile and Alessio Belenchia},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.21987},
  eprint = {2610.21987},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.21987},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Quantum control of solid state qubits via Reinforcement Learning
AU  - Dennis Herb
AU  - Mirko Rossini
AU  - Sabine Wölk
AU  - Joachim Ankerhold
AU  - Dominik Maile
AU  - Alessio Belenchia
PY  - 2026
DA  - 2026/10/15
PB  - arXiv
DO  - 10.48550/arxiv.2610.21987
UR  - https://arxiv.org/abs/2610.21987
AB  - The use of reinforcement learning (RL) in the design of quantum control strategies for pulse level control of NISQ era quantum devices is attracting growing interest. Model-free RL, not requiring preexisting knowledge of the physical system, acts as a black-box optimization tool well suited for noisy devices. In this work, we provide a simulation-based analysis of the performance of model-free RL for state preparation and qubit gate design in a challenging setup: indirect control of NV nuclear spins in a spin-bath. In particular, the spin-bath is modeled via the generalized cluster correlation expansion accounting for magnetic impurities and intra-bath interactions. We analyze the performance of the RL agents by performing simulations with minimally observable environments and noisy, sparse rewards as introduced in (Phys. Rev. X 12, 011059), and compare them to other state-of-the-art black-box optimization tools.
ER  -

Abstract

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