Using partial knowledge of a quantum state to control multiqubit entanglement
is a largely unexplored paradigm in the emerging field of quantum interactive
dynamics with the potential to address outstanding challenges in quantum state
preparation and compression, quantum control, and quantum complexity. We
present a deep reinforcement learning (RL) approach to constructing short
disentangling circuits for arbitrary 4-, 5-, and 6-qubit states using an
actor-critic algorithm. With access to only two-qubit reduced density matrices,
our agent decides which pairs of qubits to apply two-qubit gates on; requiring
only local information makes it directly applicable on modern NISQ devices.
Utilizing a permutation-equivariant transformer architecture, the agent can
autonomously identify qubit permutations within the state, and adjusts the
disentangling protocol accordingly. Once trained, it provides circuits from
different initial states without further optimization. We demonstrate the
agent's ability to identify and exploit the entanglement structure of
multiqubit states. For 4-, 5-, and 6-qubit Haar-random states, the agent learns
to construct disentangling circuits that exhibit strong correlations both
between consecutive gates and among the qubits involved. Through extensive
benchmarking, we show the efficacy of the RL approach to find disentangling
protocols with minimal gate resources. We explore the resilience of our trained
agents to noise, highlighting their potential for real-world quantum computing
applications. Analyzing optimal disentangling protocols, we report a general
circuit to prepare an arbitrary 4-qubit state using at most 5 two-qubit (10
CNOT) gates.

利用偏知识控制多量子位纠缠是量子交互动力学领域中的一个未经探索的范式，本论文采用一种深度强化学习方法构建任意 4、5 和 6 量子位状态的简化量子解分路，证明了该方法能够识别和利用多量子位态的纠缠结构，为真实量子计算应用提供了潜力。