AI 中文总结
该研究提出基于信念传播的解纠缠器量子电路合成方法，可制备多类张量网络态，保真度高，为量子应用提供新途径。
AI 中文摘要
我们开发了一种用于制备一类张量网络态的量子电路合成方法。该方案适用于可通过信念传播（BP）处理的态，BP是一种张量网络规范方案，近期已可实现大规模经典模拟。该问题被简化为独立的、严格局部的经典变分优化：每个最近邻两量子比特“解纠缠器”门最小化边定义的熵。解纠缠器将态驱动至乘积态，其厄米共轭可制备目标态。每个解纠缠层的深度最多为z+1（z为每个位点的最大最近邻数），优化过程不存在贫瘠高原，且键维度保持有界。作为演示，仅用3-5个解纠缠层，我们制备了编码17维正态分布的102量子比特树张量网络，以及64至127量子比特重六边形晶格上的横场伊辛模型基态，保真度达0.9-0.999量级。该方法通过将经典张量网络态转移至硬件，为量子应用开辟了新的可能性。
英文摘要
We develop a quantum circuit synthesis method for preparing a class of tensor network states. The scheme applies to states tractable with belief propagation (BP), a tensor network gauging scheme which recently allowed for classical simulations at large scales. The problem is reduced to independent, strictly local, classical variational optimizations: each nearest-neighbor two-qubit "disentangler" gate minimizes the entropy defined on an edge. Disentanglers drive the state to a product state and their Hermitian conjugate prepares the target. Each disentangling layer has depth at most $z+1$ (with $z$ the maximal number of nearest neighbors per site), the optimization has no barren plateaus, and the bond dimension stays bounded. As a demonstration, with only $3$-$5$ disentangling layers we prepare a $102$-qubit tree tensor network encoding a $17$-dimensional normal distribution and the transverse-field Ising model ground states on a $64$- to $127$-qubit heavy-hex lattice with fidelities of order $0.9-0.999$. The method opens new possibilities for quantum applications by transferring classical tensor network states onto hardware.
Comments5+2+5 pages, 4+1+2 figures. Comments welcome!
