Quantum Gate Control with State Representation for Deep Reinforcement Learning

The extreme sensitivity of contemporary hardware to noise, manufacturing variability, and imperfect quantum logic gates are key factors that limit hardware to reliably perform quantum computation at scale. The use of reinforcement learning assisted control techniques for system dynamics optimization in quantum gate control can significantly improve hardware performance and downstream computing power. However, con-trol field noise and Hamiltonian parameter uncertainty can lead to significant deviations from the target quantum gate. As a result, quantum gates lack the generalization ability to the quantum environment, and existing reinforcement learning assisted for quantum state preparation is not suitable for quantum gate control. In this paper, we design QcontrolSR, a state representation for reinforcement learning method, enabling quantum gate control with enhanced generalization ability. It requires no knowledge of a specific Hamiltonian model of the system, or its underlying unknown process. We introduce the state representation of quantum observations and show through demonstrations that QcontrolSR-optimized quantum gate control is insensitive to driving noise of the typical strength in practice. We show that $R_{x}(\pi/2)$ gate implemented using QcontrolSR can maintain fidelity greater than 0.95 and 0.99 on dynamical quantum stochastic system. The regions with fidelity greater than 0.99 and 0.999 are 2 and 3 times higher than the model-optimized Quantum gate control, respectively.