Differentiable quantum architecture search (DQAS) is a gradient-based
framework to design quantum circuits automatically in the NISQ era. It was
motivated by such as low fidelity of quantum hardware, low flexibility of
circuit architecture, high circuit design cost, barren plateau (BP) problem,
and periodicity of weights. People used it to address error mitigation, unitary
decomposition, and quantum approximation optimization problems based on fixed
datasets. Quantum reinforcement learning (QRL) is a part of quantum machine
learning and often has various data. QRL usually uses a manually designed
circuit. However, the pre-defined circuit needs more flexibility for different
tasks, and the circuit design based on various datasets could become
intractable in the case of a large circuit. The problem of whether DQAS can be
applied to quantum deep Q-learning with various datasets is still open. The
main target of this work is to discover the capability of DQAS to solve quantum
deep Q-learning problems. We apply a gradient-based framework DQAS on
reinforcement learning tasks and evaluate it in two different environments -
cart pole and frozen lake. It contains input- and output weights, progressive
search, and other new features. The experiments conclude that DQAS can design
quantum circuits automatically and efficiently. The evaluation results show
significant outperformance compared to the manually designed circuit.
Furthermore, the performance of the automatically created circuit depends on
whether the super-circuit learned well during the training process. This work
is the first to show that gradient-based quantum architecture search is
applicable to QRL tasks.

基于梯度的量子架构搜索在 NISQ 时代提供了自动设计量子电路的框架，通过解决误差缓解、量子电路的灵活性、电路设计成本、BP 问题以及权重周期性等问题，该研究展示了梯度量子架构搜索在解决量子深度强化学习任务中的能力。

量子强化学习的可微分量子架构搜索

Differentiable Quantum Architecture Search for Quantum Reinforcement  Learning

Quantum architecture search (QAS) is the process of automating architecture
engineering of quantum circuits. It has been desired to construct a powerful
and general QAS platform which can significantly accelerate current efforts to
identify quantum advantages of error-prone and depth-limited quantum circuits
in the NISQ era. Hereby, we propose a general framework of differentiable
quantum architecture search (DQAS), which enables automated designs of quantum
circuits in an end-to-end differentiable fashion. We present several examples
of circuit design problems to demonstrate the power of DQAS. For instance,
unitary operations are decomposed into quantum gates, noisy circuits are
re-designed to improve accuracy, and circuit layouts for quantum approximation
optimization algorithm are automatically discovered and upgraded for
combinatorial optimization problems. These results not only manifest the vast
potential of DQAS being an essential tool for the NISQ application
developments, but also present an interesting research topic from the
theoretical perspective as it draws inspirations from the newly emerging
interdisciplinary paradigms of differentiable programming, probabilistic
programming, and quantum programming.

本文提出了一种不同 iable 的量子体系结构搜索框架，可自动化设计量子电路，并演示了该框架的优越性和潜力。