The quantum alternating operator ansatz (QAOA) is a prominent example of variational quantum algorithms. We propose a generalized QAOA called CD-QAOA, which is inspired by the counterdiabatic driving procedure, designed for quantum many-body systems and optimized using a reinforcement learning (RL) approach. The resulting hybrid control algorithm proves versatile in preparing the ground state of quantum-chaotic many-body spin chains by minimizing the energy. We show that using terms occurring in the adiabatic gauge potential as generators of additional control unitaries, it is possible to achieve fast high-fidelity many-body control away from the adiabatic regime. While each unitary retains the conventional QAOA-intrinsic continuous control degree of freedom such as the time duration, we consider the order of the multiple available unitaries appearing in the control sequence as an additional discrete optimization problem. Endowing the policy gradient algorithm with an autoregressive deep learning architecture to capture causality, we train the RL agent to construct optimal sequences of unitaries. The algorithm has no access to the quantum state, and we find that the protocol learned on small systems may generalize to larger systems. By scanning a range of protocol durations, we present numerical evidence for a finite quantum speed limit in the nonintegrable mixed-field spin-$1/2$ Ising and Lipkin-Meshkov-Glick models, and for the suitability to prepare ground states of the spin-1 Heisenberg chain in the long-range and topologically ordered parameter regimes. This work paves the way to incorporate recent success from deep learning for the purpose of quantum many-body control.

中文翻译：

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受逆绝热驾驶启发的多体基态准备的强化学习

量子交替算子 ansatz (QAOA) 是变分量子算法的一个突出例子。我们提出了一种称为 CD-QAOA 的广义 QAOA，它受到反绝热驱动程序的启发，专为量子多体系统设计，并使用强化学习 (RL) 方法进行了优化。由此产生的混合控制算法在通过最小化能量来准备量子混沌多体自旋链的基态方面具有通用性。我们表明，使用绝热规范势中出现的项作为额外控制幺正的发生器，可以实现远离绝热状态的快速高保真多体控制。虽然每个酉都保留了传统的 QAOA 内在连续控制自由度，例如持续时间，我们将出现在控制序列中的多个可用幺正的顺序视为一个额外的离散优化问题。赋予策略梯度算法一个自回归深度学习架构来捕捉因果关系，我们训练 RL 代理构建最优的幺正序列。该算法无法访问量子状态，我们发现在小型系统上学习的协议可能会推广到更大的系统。通过扫描一系列协议持续时间，我们提供了不可积混合场自旋中有限量子速度限制的数值证据。该算法无法访问量子状态，我们发现在小型系统上学习的协议可能会推广到更大的系统。通过扫描一系列协议持续时间，我们提供了不可积混合场自旋中有限量子速度限制的数值证据。该算法无法访问量子状态，我们发现在小型系统上学习的协议可能会推广到更大的系统。通过扫描一系列协议持续时间，我们提供了不可积混合场自旋中有限量子速度限制的数值证据。$1/2$Ising 和 Lipkin-Meshkov-Glick 模型，以及在长程和拓扑有序参数范围内准备自旋 1 海森堡链的基态的适用性。这项工作为将深度学习的最新成功应用于量子多体控制铺平了道路。