Noise-resilient quantum algorithms and software for quantum computers

量子计算机在解决与广泛的工业应用相关的问题方面具有巨大的潜力，其发展构成了当前量子和数字战略的关键部分。然而，量子计算机本质上是概率性的，因此它们的计算具有相关的不确定性和误差条。重要的是，为了实现其潜力并被工业用户采用，需要对这些误差线进行良好的估计，以便对量子计算机的结果提供所需的信任。由于量子计算机将允许模拟传统计算机无法访问的系统，问题是通过传统计算机上的模拟来验证这些错误是不可行的。因此，该项目的目标是根据量子器件的可测量误差参数来量化不确定性。量子计量学可以确定给定设备的每个噪声源的大小，但该项目的关键贡献是通过可测量性分析和不确定性传播，将不同噪声项对最终结果不确定性的贡献联系起来。为此，将开发用于量子算法验证的不确定性量化的贝叶斯分层模型，并将其应用于化学模拟，凝聚态物理和机器学习领域的量子计算软件，这些领域预计量子计算机将在近期和中期产生最大的影响。

Quantum computers have immense potential to solve problems of relevance for wide ranges of industrial applications, and their development forms a key part of current Quantum and Digital Strategies. However, quantum computers are inherently probabilistic, and therefore their calculations have associated uncertainty and error bars. Importantly, to achieve their potential and be adopted by users in industry, good estimates of these error bars are needed in order to provide the required trust in the results of quantum computers. Since quantum computers will allow to simulate systems not accessible on conventional computers, the problem is that it is infeasible to verify these errors by simulations on conventional computers. The objective of this project therefore is to quantify the uncertainty based on the measurable error parameters of the quantum device. Quantum metrology can determine the magnitude of each noise source for a given device, but the key contribution of this project is to link, via metrological analysis and uncertainty propagation, the contributions of different noise terms to the uncertainty of the final result. To this aim Bayesian hierarchical models for uncertainty quantification will be developed for quantum algorithm verification, and applied to quantum computing software in the fields of chemistry simulations, condensed matter physics, and machine learning, areas where quantum computers are expected to bring the largest impact in the near and medium term.