Variational quantum algorithms such as the Quantum Approximation Optimization
Algorithm (QAOA) in recent years have gained popularity as they provide the
hope of using NISQ devices to tackle hard combinatorial optimization problems.
It is, however, known that at low depth, certain locality constraints of QAOA
limit its performance. To go beyond these limitations, a non-local variant of
QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of
approximate solutions. The RQAOA has been studied comparatively less than QAOA,
and it is less understood, for instance, for what family of instances it may
fail to provide high quality solutions. However, as we are tackling
$\mathsf{NP}$-hard problems (specifically, the Ising spin model), it is
expected that RQAOA does fail, raising the question of designing even better
quantum algorithms for combinatorial optimization. In this spirit, we identify
and analyze cases where RQAOA fails and, based on this, propose a reinforcement
learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show
that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly
better on these identified instances where RQAOA underperforms, and is
similarly performing on instances where RQAOA is near-optimal. Our work
exemplifies the potentially beneficial synergy between reinforcement learning
and quantum (inspired) optimization in the design of new, even better
heuristics for hard problems.

设计出更好的用于组合优化的量子算法，结合强化学习增强递归 QAOA 表现，与传统 QAOA 相比，表现更好。

强化学习辅助的递归 QAOA

Reinforcement Learning Assisted Recursive QAOA

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。