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arXiv 2603.08025quant-phphysics.chem-ph

基于顺序Givens旋转的经典驱动混合量子算法以降低测量成本

Classically Driven Hybrid Quantum Algorithms with Sequential Givens Rotations for Reduced Measurement Cost

Benjamin Mokhtar, Noboru Inoue, Takashi Tsuchimochi

AI总结:

本文提出了一种经典驱动的混合量子算法,通过顺序Givens旋转降低测量成本,适用于电子结构模拟。

摘要:

用于电子结构模拟的量子算法正在积极开发,然而许多混合量子-经典方法受到与大分子哈密顿量相关的测量开销的限制。本文介绍了一种以对角化驱动的框架,通过顺序Givens旋转逐步将电子哈密顿量驱动至Slater决定性基的(块)对角形式。与Schrodinger-picture方法通过变分优化波函数不同,我们的方法采用Heisenberg-picture观点:哈密顿量被迭代变换,旋转角度通过低维有效块的经典方法确定,从而将量子工作量减少到每个迭代中少量固定矩阵元测量。候选生成器通过近似Baker-Campbell-Hausdorff更新与截断和累积量近似来估计,以控制哈密顿量增长,同时辅以随机选择以避免停滞。我们进一步引入了角度合并程序,通过合并重复的小角度旋转来减少电路深度。我们在此框架上对N₂和强相关氢系统进行了基准测试,评估了收敛行为、残差结构诊断、测量精度权衡、电路成本和有限采样下的鲁棒性。

英文摘要:

Quantum algorithms for electronic-structure simulations are actively being developed, yet many hybrid quantum-classical approaches are bottlenecked by the measurement overhead associated with large molecular Hamiltonians. Here we introduce a diagonalization-driven framework that progressively drives the electronic Hamiltonian toward a (block-)diagonal form in the Slater-determinant basis using sequential Givens rotations. In contrast to Schrödinger-picture methods that variationally optimize a wave function, our approach adopts a Heisenberg-picture viewpoint: the Hamiltonian is iteratively transformed, and rotation angles are determined classically from low-dimensional effective blocks, reducing the quantum workload to a small, fixed set of matrix-element measurements per iteration. Candidate generators are estimated via approximate Baker-Campbell-Hausdorff updates with truncation and cumulant-based approximations that control Hamiltonian growth, complemented by stochastic selection to avoid stagnation. We further introduce an angle-merging procedure that reduces circuit depth by consolidating repeated small-angle rotations. We benchmark the framework on N$_2$ and strongly correlated hydrogen systems, assessing convergence behavior, residual-structure diagnostics, measurement-accuracy trade-offs, circuit costs, and robustness under finite sampling.

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