Adaptive Reinforcement Learning for Robust Open Quantum System Control: A Multi-Task Framework with Temporal Optimization
arXiv:2605.26925v1 Announce Type: cross Abstract: We present a Multi-task Soft Actor-Critic (SAC) Reinforcement Learning framework designed for open-system quantum control across diverse Hamiltonians, which learns optimal pulse sequences while simultaneously discovering problem-specific evolution time T and number of control pulse segments N. Experimental results across 51 Hamiltonian variations demonstrate that the multi-task SAC model is able to generate control pulses that can drive a system, under environment noise, from its initial state to its target state with high fidelities, establish
The convergence of advanced reinforcement learning techniques and the pressing need for robust control in nascent quantum computing systems is driving innovation in this field.
Achieving precise and adaptive control over open quantum systems is critical for scaling quantum computers and realizing their potential across various applications.
This research outlines a framework that not only optimizes control pulse sequences but also dynamically determines optimal evolution time and number of pulse segments, making quantum control more adaptive and robust against environmental noise.
- · Quantum computing researchers
- · Quantum hardware developers
- · AI researchers in control systems
- · Quantum software developers
- · Traditional fixed-parameter quantum control methods
Improved fidelity and stability in experimental quantum systems, accelerating the development of quantum computers.
Reduced error rates in quantum computations could unlock new applications in materials science and drug discovery sooner than expected.
Enhanced quantum control methods may lead to the development of novel quantum sensors and communication technologies operating in noisy environments.
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Read at arXiv cs.LG