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High-fidelity quantum gate design is important for various quantumtechnologies, such as quantum computation and quantum communication. Numerouscontrol policies for quantum gate design have been proposed given a dynamicalmodel of the quantum system of interest. However, a quantum system is oftenhighly sensitive to noise, and obtaining its accurate modeling can be difficultfor many practical applications. Thus, the control policy based on a quantumsystem model may be unpractical for quantum gate design. Also, quantummeasurements collapse quantum states, which makes it challenging to obtaininformation through measurements during the control process. In this paper, wepropose a novel training framework using deep reinforcement learning formodel-free quantum control. The proposed framework relies only on themeasurement at the end of the control process and offers the ability to findthe optimal control policy without access to quantum systems during thelearning process. The effectiveness of the proposed technique is numericallydemonstrated for model-free quantum gate design and quantum gate calibrationusing off-policy reinforcement learning algorithms.

Model-free Quantum Gate Design and Calibration using Deep Reinforcement Learning