EAGER: QAC-QSA: A HYBRID QUANTUM-CLASSICAL PATH-INTEGRAL METHOD FOR CHEMICAL DYNAMICS

EAGER：QAC-QSA：化学动力学混合量子经典路径积分方法

• 批准号: 2038005
• 负责人: Nandini Ananth
• 金额: $ 30万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038005&HistoricalAwards=false
• 关键词: EAGER; QAC; QSA; HYBRID; QUANTUM

Nandini Ananth and Peter McMahon of Cornell University are supported by an EAGER award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop new methods for using quantum computers to solve dynamics in quantum-simulation problems. Computer simulations that fully solve the fundamental quantum mechanical equations that describe the way electrons, atoms, and molecules interact and move in time hold the key to predicting and characterizing chemical reactions. It has long been appreciated that this task requires quantum computers which, unlike classical computers, can, in principle, solve the most complex of quantum mechanical equations. With the advent of near-term (first generation) quantum computers, substantial effort is underway to develop new algorithms to solve chemical problems on these devices. While there has been some success in methods to characterize the static energies of molecular systems, dynamic algorithms capable of being run on current hardware are lacking. Ananth and McMahon are developing a hybrid algorithm that combines both classical and quantum computers to perform quantum dynamics simulations, where the quantum computer need only run short programs (circuits) that are suitable for execution on near-term machines. This research has immediate broader impacts in the quantum-computing industry in the United States and may open the door to an array of new applications and scientific research. The development and testing of methods from this project will be transferred to industry practice through the open sharing of code and results. Students trained in this research will join the next generation workforce in quantum information systems. These hybrid quantum-classical scheme may enable, for the first time, quantum dynamic simulations on a quantum computer, moving beyond the current algorithms that are focused on primarily static properties like spectra. Conventional wisdom is that dynamics beyond trivially short timescales will require fault-tolerant quantum computers because standard long-time dynamics simulations require the sequential computation of a large number of short-time propagation steps leading to significant error accumulation. Ananth and McMahon are developing a hybrid quantum-classical algorithm for long-time dynamic simulations that overcomes this limitation. Specifically, they are using the path-integral framework to represent a longtime dynamics simulation as a series of independent short-time propagation steps that can be performed using near-term quantum computers. The hybrid nature of the algorithm arises from combining efficient classical Monte Carlo methods to sample important real-time paths in configuration space weighted by quantum transition probabilities. This research may demonstrate the feasibility of condensed phase quantum dynamic simulations and enable finite temperature simulations of quantum systems on near-term quantum computers. This effort may significantly expand the range of chemical problems that can be studied to include reaction rate calculations and characterization of mechanisms and non-linear spectra.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.

康奈尔大学的Nandini Ananth和Peter McMahon获得了化学系化学理论、模型和计算方法项目的EAGER奖，他们开发了使用量子计算机解决量子模拟动力学问题的新方法。计算机模拟完全解决了描述电子、原子和分子相互作用和运动方式的基本量子力学方程，这是预测和表征化学反应的关键。人们早就认识到，这项任务需要量子计算机，与经典计算机不同，量子计算机原则上可以解决最复杂的量子力学方程。随着近期（第一代）量子计算机的出现，人们正在努力开发新的算法来解决这些设备上的化学问题。虽然在描述分子系统静态能量的方法上已经取得了一些成功，但能够在当前硬件上运行的动态算法仍然缺乏。Ananth和McMahon正在开发一种混合算法，该算法结合了经典计算机和量子计算机来执行量子动力学模拟，量子计算机只需要运行适合在近期机器上执行的短程序（电路）。这项研究对美国的量子计算行业产生了直接而广泛的影响，并可能为一系列新的应用和科学研究打开大门。该项目方法的开发和测试将通过代码和结果的开放共享转移到行业实践中。在这项研究中训练的学生将加入量子信息系统的下一代劳动力。这些混合量子-经典方案可能首次使量子计算机上的量子动态模拟成为可能，超越了目前主要关注光谱等静态特性的算法。传统的观点认为，超越非常短的时间尺度的动力学将需要容错量子计算机，因为标准的长时间动力学模拟需要大量短时间传播步骤的顺序计算，从而导致显著的误差积累。Ananth和McMahon正在开发一种用于长时间动态模拟的混合量子经典算法，以克服这一限制。具体来说，他们使用路径积分框架将长期动力学模拟表示为一系列独立的短期传播步骤，这些步骤可以使用近期量子计算机执行。该算法的混合特性源于结合了有效的经典蒙特卡罗方法，在量子跃迁概率加权的构型空间中对重要的实时路径进行采样。本研究可证明凝聚态量子动力学模拟的可行性，并可在近期量子计算机上实现量子系统的有限温度模拟。这一努力可能会显著扩大化学问题的范围，可以研究包括反应速率计算和表征机制和非线性光谱。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。