使用量子计算机观察到比经典稀疏基态求解器更高的精度
Observation of Improved Accuracy over Classical Sparse Ground-State Solvers using a Quantum Computer
AI总结：
研究基态能量问题中量子算法与经典算法的优劣，构建特定局部哈密顿量问题，对比经典SCI方法，实验表明基于样本的Krylov量子对角化在IBM Heron R3处理器上能超越SCI，解决了相关性能比较问题。
摘要：
证明量子算法在基态能量问题上优于经典算法是量子计算中一个悬而未决的重要问题。我们通过实验证明，一种量子算法可以超越经典的选定配置相互作用（SCI）方法，这是计算化学和凝聚态物理中使用的关键技术家族。我们构建了一类具有稀疏基态的局部哈密顿量问题，并表明SCI无法找到一个49量子比特实例的基态。然后我们表明，在IBM Heron R3处理器上运行的基于样本的Krylov量子对角化成功完成了相同任务。虽然使用针对我们哈密顿量构造设计的迭代求解器经典地可解决该问题，但这项工作解决了基于样本的量子对角化算法是否能超越标准SCI启发式方法的问题。
英文摘要：
Demonstrating quantum advantage over classical algorithms for ground state energy problems is an outstanding open problem in quantum computation. We experimentally demonstrate that a quantum algorithm can outperform classical selected configuration interaction (SCI) methods, a key family of techniques used in computational chemistry and condensed matter physics. We construct a class of local Hamiltonian problems with sparse ground states, and show that SCI fails to find the ground state of a 49-qubit instance. We then show that sample-based Krylov quantum diagonalization, run on an IBM Heron R3 processor, succeeds at the same task. While the problem is solvable classically using iterative solvers designed to target our Hamiltonian construction, this work resolves the question of whether a sample-based quantum diagonalization algorithm can outperform standard SCI heuristics.
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