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.

arXiv:2610.21987 [quant-ph]

(or arXiv:2610.21987v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.21987 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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