AI 中文总结
针对NISQ时代开放量子系统模拟难题，提出资源高效算法，推导混合酉伴随通道，引入自适应变分量子轨迹压缩框架，训练参数化量子电路，经数值模拟验证算法准确性与资源效率，为开放量子系统模拟提供实用途径。
AI 中文摘要
在嘈杂的中尺度量子（NISQ）时代，开放量子系统的量子模拟受到耗散动力学的非酉性质和近期量子处理器上可用量子资源有限的阻碍。本文提出一种资源高效算法，用于在NISQ设备上模拟林德布拉德动力学。对于具有泡利耗散的开放量子系统，首先推导近似目标耗散动力学的紧凑稳定混合酉伴随通道，通过轨迹采样实现无辅助量子比特。为进一步减少实现采样轨迹所需的电路深度，引入自适应变分量子轨迹压缩框架。通过训练深度自适应参数化量子电路近似重复的特罗特化哈密顿模拟算符，以替换采样轨迹中出现的重复酉段，且训练无需辅助量子比特。对耗散量子XY模型的数值模拟证明了算法的准确性和资源效率，为在近期量子硬件上无辅助量子比特且减少深度地模拟开放量子系统提供了实用途径。
英文摘要
Quantum simulation of open quantum systems in the noisy intermediate-scale quantum (NISQ) era is hindered by the non-unitary nature of dissipative dynamics and the limited quantum resources available on near-term quantum processors. In this work, we propose a resource-efficient algorithm for simulating Lindbladian dynamics on NISQ devices. For open quantum systems with Pauli dissipations, we first derive a compact and stable mixed-unitary adjoint channel that approximates the target dissipative dynamics and enables ancilla-free implementation through trajectory sampling. To further reduce the circuit depth required for implementing the sampled trajectories, we introduce an adaptive variational quantum trajectory compression framework. In this framework, a depth-adaptive parameterized quantum circuit is trained to approximate repeated Trotterized Hamiltonian simulation operators, which are then used to replace repeated unitary segments appearing in the sampled trajectories. Importantly, the training procedure can also be performed without auxiliary qubits. Numerical simulations of the dissipative quantum $XY$ model demonstrate the accuracy and resource efficiency of the proposed algorithm. Our results provide a practical route toward ancilla-free and depth-reduced simulation of open quantum systems on near-term quantum hardware.
Comments14 pages, 9 figures
