标题： 基于局部时变变分原理的量子电路模拟 显示英文标题

标题： Quantum circuit simulation with a local time-dependent variational principle

摘要： 经典量子电路模拟对于评估潜在的量子优势和设备基准测试至关重要，但需要复杂的方法来避免资源的指数增长。 特别是张量网络方法，如矩阵乘积态（MPS）结合时间演化块削减（TEBD）算法，目前主导了大规模电路模拟。 这些方法在纠缠受限时能高效扩展，但随着纠缠增加，其键维迅速增长，并通过成本高昂的SWAP插入处理远距离门。 受多体物理中时间依赖变分原理（TDVP）成功的启发，我们将量子电路重新解释为一系列离散时间演化，使用门生成器通过局部TDVP公式构建基于MPS的电路模拟。 这种方法通过（1）自然地适应远距离门和（2）最优地表示MPS流形上的状态，解决了TEBD的关键限制。 通过更全局地扩散纠缠，该方法抑制了局部键增长并减少了内存和运行时间成本。 我们在五个49量子比特电路上对这种方法进行了基准测试：三个哈密顿量电路（一维开放和周期性海森堡，二维7x7伊辛）和两个算法电路（量子近似优化，硬件高效假设）。 在所有情况下，我们的方法相比标准工具显著减少了资源消耗，确立了电路模拟的新最先进水平，并推动了量子计算、凝聚态物理等领域的进展。 显示英文摘要

摘要： Classical simulations of quantum circuits are vital for assessing potential quantum advantage and benchmarking devices, yet they require sophisticated methods to avoid the exponential growth of resources. Tensor network approaches, in particular matrix product states (MPS) combined with the time-evolving block decimation (TEBD) algorithm, currently dominate large-scale circuit simulations. These methods scale efficiently when entanglement is limited but suffer rapid bond dimension growth with increasing entanglement and handle long-range gates via costly SWAP insertions. Motivated by the success of the time-dependent variational principle (TDVP) in many-body physics, we reinterpret quantum circuits as a series of discrete time evolutions, using gate generators to construct an MPS-based circuit simulation via a local TDVP formulation. This addresses TEBD's key limitations by (1) naturally accommodating long-range gates and (2) optimally representing states on the MPS manifold. By diffusing entanglement more globally, the method suppresses local bond growth and reduces memory and runtime costs. We benchmark the approach on five 49-qubit circuits: three Hamiltonian circuits (1D open and periodic Heisenberg, 2D 7x7 Ising) and two algorithmic ones (quantum approximate optimization, hardware-efficient ansatz). Across all cases, our method yields substantial resource reductions over standard tools, establishing a new state-of-the-art for circuit simulation and enabling advances across quantum computing, condensed matter, and beyond.