量子退火
Unfolding as Quantum Annealing
摘要：
高能物理充满了困难的计算问题，这是量子计算可以用来加速计算的领域之一。我们提出了一种基于似然的正则化展开在量子计算机上的实现。反问题是重铸的二次无约束二进制优化（QUBO），它具有相同的形式的伊辛哈密顿量，因此它是可编程的量子退火。我们使用一个捕捉问题本质的模型测试了该方法，并将结果与欧洲核子研究中心大型强子对撞机（LHC）精密测量中常用的基线方法进行了比较。展开后的分布与原始分布非常一致。我们还展示了该方法可以扩展到包括滋扰参数的影响，代表系统的不确定性影响测量的来源。
英文摘要：
High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations. We present an implementation of likelihood-based regularized unfolding on a quantum computer. The inverse problem is recast in terms of quadratic unconstrained binary optimization (QUBO), which has the same form of the Ising hamiltonian and hence it is solvable on a programmable quantum annealer. We tested the method using a model that captures the essence of the problem, and compared the results with a baseline method commonly used in precision measurements at the Large Hadron Collider (LHC) at CERN. The unfolded distribution is in very good agreement with the original one. We also show how the method can be extended to include the effect of nuisance parameters representing sources of systematic uncertainties affecting the measurement.
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。
