Quantum Software for Simulation of molecular systems on NISQ devices

近中期量子（NISQ）设备描述了今天或未来几年内可用的量子设备。量子硬件还没有完全容错和纠错，这意味着在当前设备上运行的任何算法都会受到噪声的影响。该项目旨在开发和测试NISQ设备上分子系统模拟的算法。可以模拟的方案的一个例子是哈伯德模型。在整个项目中，将重点关注验证，目标是开发尽可能精确的量子算法。包括在博士工作中的是一个项目的延续，该项目开发了一个混合量子-经典机器学习方案，用于在存在噪声的情况下识别量子态。这是一种通过经典机器学习算法使量子设备的输出最小化的方案。这项工作是以前研究量子设备受到噪声影响的情况的延伸。这项工作将在博士学位后期用于开发用于分子模拟的混合机器学习算法。

Near-term Intermediate Scale Quantum (NISQ) devices describe quantum devices which are available today, or within the next few years. Quantum hardware is not yet fully fault tolerant and error corrected, meaning that any algorithms running on current devices are subject to noise. This project aims to develop and test algorithms for simulation of molecular systems on NISQ devices. An example of a scheme which can be simulated is the Hubbard model. There will be a strong focus upon validation throughout this project, where the goal is to develop the most accurate quantum algorithms possible.Included in the PhD work is a continuation of a project developing a hybrid quantum - classical machine learning scheme for quantum state discrimination in the presence of noise. This is a scheme where the output of a quantum device is minimised by a classical machine learning algorithm. The work is an extension of previous work examining the case where the quantum device is subject to noise. This work will then be used later in the PhD to develop hybrid machine learning algorithms for molecular simulation.