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arXiv quant-ph/0206059quant-ph
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随机可满足性问题的绝热量子计算

Adiabatic Quantum Computing for Random Satisfiability Problems

Tad Hogg

摘要:

将绝热量子计算的离散形式与其他搜索方法(经典和量子)进行了比较,用于随机可满足性(SAT)问题。随着步骤的数量增长,只有作为变量数量的立方,绝热方法给出的解决方案的概率接近1的问题大小可行的评估,通过模拟在当前的计算机上。然而,对于这些大小,大多数实例的最小能量间隙相当大,因此对于小问题所看到的良好性能缩放可能无法反映成本由微小间隙主导的渐近行为。此外,由此产生的搜索成本比其他方法高得多。与传统的GSAT启发式方法相比,与绝热极限不匹配的量子算法的变体平均成本更低,增长更慢。

英文摘要:

The discrete formulation of adiabatic quantum computing is compared with other search methods, classical and quantum, for random satisfiability (SAT) problems. With the number of steps growing only as the cube of the number of variables, the adiabatic method gives solution probabilities close to 1 for problem sizes feasible to evaluate via simulation on current computers. However, for these sizes the minimum energy gaps of most instances are fairly large, so the good performance scaling seen for small problems may not reflect asymptotic behavior where costs are dominated by tiny gaps. Moreover, the resulting search costs are much higher than for other methods. Variants of the quantum algorithm that do not match the adiabatic limit give lower costs, on average, and slower growth than the conventional GSAT heuristic method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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