EAGER: QSA: Approximating the Ground States of Non-Stoquastic Hamiltonians Using the Variational Quantum Eigensolver

EAGER：QSA：使用变分量子本征求解器逼近非随机哈密顿量的基态

• 批准号: 2037755
• 负责人: Tameem Albash
• 金额: $ 19.94万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037755&HistoricalAwards=false
• 关键词: EAGER; QSA; Approximating; Ground; States

Quantum algorithms utilize the unique properties of quantum physics to perform computational tasks, and for certain tasks they can do so more efficiently than algorithms restricted to the laws of classical physics. Quantum computers that can implement such algorithms are now publicly available, but these devices remain limited in the size and length of computations they can perform, keeping quantum algorithms with proven quantum advantages out of reach. Hybrid algorithms that use both quantum and classical hardware have been proposed as one approach to address this challenge, and this project aims to study the viability of hybrid approaches in delivering a quantum advantage by performing a systematic computational cost comparison with state-of-the-art classical algorithms. If advantages are possible using near-term quantum computers, it would dramatically enhance our ability to understand and predict complex systems across the physical sciences. The project highlights the multi-disciplinary nature of quantum computing and will train students to have a diverse toolbox to tackle emerging challenges in the field. This approach is at the heart of the project's efforts to develop a new curriculum to prepare a 'quantum-ready' workforce to address the call of the National Quantum Initiative Act of 2018.The task of approximating the ground state of many-body non-stoquastic Hamiltonians, a class of quantum Hamiltonians that describes many relevant model systems such as fermionic and sign-problematic Hamiltonians, manifests itself in a range of disciplines, from high energy physics to quantum chemistry. Current classical approaches for tackling this problem are computationally prohibitive at relevant system sizes, and overcoming or mitigating this computational bottleneck would enable new simulations of important model systems with far-reaching impacts across the physical sciences. To what degree present quantum hardware can achieve this remains an open question. This project addresses this possibility by performing a side-by-side comparison of the computational cost of hybrid quantum-classical variational algorithms and state-of-the-art classical algorithms using well-defined problem classes of non-stoquastic Hamiltonians of varying difficulty. A key objective of this assessment is to understand the differences and similarities between the optimization landscapes of the hybrid and purely-classical approaches, which may provide insight into the conditions under which the hybrid approach can achieve an advantage. The research combines lessons from spin glass theory, Hamiltonian complexity, numerical simulations, and rigorous benchmarking experience in order to make an assessment of the viability of achieving a quantum advantage on near-term quantum hardware.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

量子算法利用量子物理学的唯一特性来执行计算任务，对于某些任务，它们比仅限于古典物理定律的算法更有效地执行了这样做。 现在可以公开可用的可以实现此类算法的量子计算机，但是这些设备在可以执行的计算大小和时间长度上仍然有限，将量子算法保持在远距离范围内。已经提出，同时使用量子和经典硬件的混合算法是应对这一挑战的一种方法，该项目旨在研究混合方法的可行性，以通过对系统的计算成本进行比较来提供量子优势艺术古典算法。 如果使用近期量子计算机可以使用优势，它将极大地增强我们理解和预测物理科学中复杂系统的能力。该项目强调了量子计算的多学科性质，并将培训学生拥有多样化的工具箱来应对该领域的新挑战。这种方法是该项目努力开发新课程的核心一类量子汉密尔顿人的stoquastic hamiltonians描述了许多相关模型系统，例如费米子和标志性的哈密顿人，在从高能量物理学到量子化学的一系列学科中表现出来。解决此问题的当前经典方法是在相关系统尺寸上的计算高度，并且克服或缓解这种计算瓶颈将启用对重要模型系统的新模拟，并在整个物理科学中产生影响很大的影响。现在的量子硬件可以在多大程度上实现这一目标仍然是一个悬而未决的问题。该项目通过使用定义明确的问题类别的问题类别的问题类别的杂交量子量子变异算法和最先进的经典算法进行并排比较来解决这种可能性。该评估的关键目的是了解混合和纯粹经典方法的优化景观之间的差异和相似性，这可能会洞悉混合方法可以获得优势的条件。该研究结合了旋转玻璃理论，哈密顿式复杂性，数值模拟和严格的基准测试经验的经验教训，以评估在近期量子硬件上实现量子优势的可行性，这反映了NSF的法规任务，并已被视为已被视为值得通过基金会的智力优点和更广泛的影响审查标准来通过评估来支持。

项目成果

Diabatic quantum annealing for the frustrated ring model

受挫环模型的非绝热量子退火

• DOI: 10.1088/2058-9565/acfbaa
• 发表时间: 2023
• 期刊: Quantum Science and Technology
• 影响因子: 6.7
• 作者: Côté, Jeremy;Sauvage, Frédéric;Larocca, Martín;Jonsson, Matías;Cincio, Lukasz;Albash, Tameem
• 通讯作者: Albash, Tameem

Quantum-inspired tempering for ground state approximation using artificial neural networks

使用人工神经网络进行基态近似的量子启发回火

• DOI: 10.21468/scipostphys.14.5.121
• 发表时间: 2023
• 期刊: SciPost Physics
• 影响因子: 5.5
• 作者: Albash, Tameem;Smith, Conor;Campbell, Quinn;Baczewski, Andrew D.
• 通讯作者: Baczewski, Andrew D.