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Differentiable quantum architecture search (DQAS) is a gradient-basedframework to design quantum circuits automatically in the NISQ era. It wasmotivated by such as low fidelity of quantum hardware, low flexibility ofcircuit architecture, high circuit design cost, barren plateau (BP) problem,and periodicity of weights. People used it to address error mitigation, unitarydecomposition, and quantum approximation optimization problems based on fixeddatasets. Quantum reinforcement learning (QRL) is a part of quantum machinelearning and often has various data. QRL usually uses a manually designedcircuit. However, the pre-defined circuit needs more flexibility for differenttasks, and the circuit design based on various datasets could becomeintractable in the case of a large circuit. The problem of whether DQAS can beapplied to quantum deep Q-learning with various datasets is still open. Themain target of this work is to discover the capability of DQAS to solve quantumdeep Q-learning problems. We apply a gradient-based framework DQAS onreinforcement learning tasks and evaluate it in two different environments -cart pole and frozen lake. It contains input- and output weights, progressivesearch, and other new features. The experiments conclude that DQAS can designquantum circuits automatically and efficiently. The evaluation results showsignificant outperformance compared to the manually designed circuit.Furthermore, the performance of the automatically created circuit depends onwhether the super-circuit learned well during the training process. This workis the first to show that gradient-based quantum architecture search isapplicable to QRL tasks.

Differentiable Quantum Architecture Search for Quantum Reinforcement Learning