发表机构
Phasecraft Ltd.（Phasecraft有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于双括号框架的概率虚时演化算法PDBQITE，通过单辅助比特和受控演化实现指数级电路深度缩减，并在分子基态制备和MaxCut问题上优于现有算法。
AI 中文摘要
虚时演化（ITE）是一种广泛用于制备目标哈密顿量基态的技术。基于最近提出的双括号量子虚时演化（DB-QITE）框架，我们引入了一种概率变体（PDBQITE），该变体实现了电路深度的指数级缩减。我们的算法仅需要一个辅助量子比特和一个量子电路，该电路每次迭代包含一次受控实时演化和一次电路中间测量。其代价是总成功概率随迭代次数呈指数衰减，为此我们推导了一个与系统大小无关的解析下界。我们的数值模拟表明，每一步的成功概率仍接近1，使得执行多次迭代变得现实可行，从而相较于先前的构造提供了显著改进。我们证明，PDBQITE在分子哈密顿量的基态制备方面优于近期的概率算法PITE，并且对于MaxCut问题，在随机正则图上，混合QAOA-PDBQITE方案在相同电路深度下比QAOA实现了更高的近似比。
英文摘要
Imaginary-time evolution (ITE) is a widely used technique for preparing the ground state of a target Hamiltonian. Building on the recently proposed framework of Double-Bracket quantum ITE (DB-QITE), we introduce a probabilistic variant (PDBQITE) that achieves an exponential reduction in circuit depth. Our algorithm requires only a single ancilla qubit and a quantum circuit that consists of one controlled real-time evolution and one mid-circuit measurement per iteration. The trade-off is an exponential decay of the total success probability with the number of iterations, for which we derive an analytical lower bound that is independent of system size. Our numerical simulations show that the per-step success probability remains close to unity, making it realistic to execute many iterations, thus providing a substantial improvement over prior constructions. We show that PDBQITE outperforms the near-term probabilistic algorithm PITE for ground-state preparation of molecular Hamiltonians, and that a hybrid QAOA-PDBQITE scheme achieves higher approximation ratios than QAOA at the same circuit depth on random regular graphs for the MaxCut problem.
Comments31 pages, 14 figures
