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EAGER-QAC-QCH: NSF-BSF: Quantum Computation as a Non-Equilibrium Dynamical Many-Body System

EAGER-QAC-QCH：NSF-BSF：量子计算作为非平衡动态多体系统

基本信息

批准号：
2037654
负责人：
Alex Kamenev
金额：
$ 29.11万
依托单位：
University of Minnesota-Twin Cities
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：
2020-09-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037654&HistoricalAwards=false
关键词：
EAGER QAC QCH NSF BSF

项目摘要

Non-technical Summary This award is made on an EAGER proposal invited through the Quantum Algorithm Challenge Dear Colleague Letter. It supports research and education to study new concepts for how quantum mechanical states can be prepared and manipulated to perform computation which includes protocols or algorithms required to use a quantum computer to solve a problem. Quantum computing hardware has significantly advanced over the past few years. Both Google and IBM have recently demonstrated devices with about 50 fully controlled superconducting qubits with high fidelity and long coherence times. The number of qubits may be expected to increase even further, but the number of independent external controls presents a crucial bottleneck in scaling up the modern quantum computing architecture. This means that near-term quantum computers are not going to operate the way classical processors do. The PI aims to develop specific operating protocols, which will allow already existing quantum devices to perform particular optimization tasks, which are exponentially hard for conventional classical algorithms. The latter suffer from being trapped into sub-optimal solutions for extremely long times. Quantum tunneling allows for simultaneous exploration of multiple potentially optimal configurations, and ultimately enables finding the true unique optimum. This project is aimed to investigate theoretical limits for efficiency of these quantum algorithms. Practical demonstration schemes will be conceptualized and possibly implemented on existing prototypical quantum devices. NSF funds will provide training for a graduate student research assistant and (partially) a postdoctoral fellow. Both will be trained in the theoretical apparatus underlying construction of algorithms for quantum computation. The results of the project will be incorporated in graduate classes at the University of Minnesota as well as at regular summer schools, which the PI co-organizes through the Fine Theoretical Physics Institute. Part of the project will be conducted in close cooperation with Prof. Yuval Gefen of the Weizmann Institute, who will be funded separately by BSF. The BSF part will provide training for another postdoctoral fellow. Technical Summary This award is made on an EAGER proposal invited through the Quantum Algorithm Challenge Dear Colleague Letter. It supports research and education to study new concepts for how quantum mechanical states can be prepared and manipulated to perform computation which includes protocols or algorithms required to use a quantum computer to solve a problem. The PI will consider quantum approximate optimization algorithms (QAOA) and quantum image recognition schemes. Both are based on the idea of an information processing engine, which repeatedly performs a particular cycle. The cycle involves coupling and decoupling of the active quantum system, which can be modeled by a Sherrington-Kirkpatrick spin glass encoded with a desired optimization problem, with the information bath. A qubit realization of the Sachdev-Ye-Kitaev (SYK) model will be explored as a model for the information bath. The SYK system, being a holographic dual of a black hole, possesses a finite entropy down to the exponentially small temperature. This allows quantum tunneling among multiple local minima of the spin glass. The quantum measurement operation, performed in the end of each cycle, provides a progressively improving set of candidate optimal spin configurations. A goal of the project is to evaluate the probability of finding the true optimum within such a set. Another goal is to optimize the cycle to determine theoretical bounds for efficiency of the information engine. The NSF-BSF part of the project will deal with a theoretical description in terms of the reduced density matrix of the working substance. We expect to find a Lindblad-like evolution equation. It will allow for an efficient analysis of computation schemes using existing powerful field-theoretical and computational techniques.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.

非技术性总结该奖项是根据通过量子算法挑战亲爱的同事信邀请的EAGER提案颁发的。它支持研究和教育，研究如何准备和操纵量子力学状态以执行计算的新概念，包括使用量子计算机解决问题所需的协议或算法。量子计算硬件在过去几年中取得了显着进步。谷歌和IBM最近都展示了具有大约50个完全受控的超导量子位的设备，这些量子位具有高保真度和长相干时间。量子比特的数量可能会进一步增加，但独立的外部控制的数量是扩大现代量子计算架构的关键瓶颈。这意味着近期的量子计算机不会像经典处理器那样运行。PI旨在开发特定的操作协议，允许现有的量子设备执行特定的优化任务，这对于传统的经典算法来说是指数级困难的。后者长期受困于次优解决方案。量子隧穿允许同时探索多个潜在的最佳配置，并最终能够找到真正唯一的最佳配置。该项目旨在研究这些量子算法效率的理论极限。实际的演示方案将被概念化，并可能在现有的原型量子设备上实现。NSF基金将为一名研究生研究助理和（部分）一名博士后研究员提供培训。两人都将接受量子计算算法构建的理论设备的培训。该项目的结果将被纳入明尼苏达大学的研究生课程以及PI通过精细理论物理研究所共同组织的定期暑期学校。该项目的一部分将与魏茨曼研究所的Yuval Gefen教授密切合作进行，后者将由BSF单独资助。BSF部分将为另一位博士后研究员提供培训。该奖项是根据量子算法挑战赛邀请的EAGER提案颁发的。它支持研究和教育，研究如何准备和操纵量子力学状态以执行计算的新概念，包括使用量子计算机解决问题所需的协议或算法。PI将考虑量子近似优化算法（QAOA）和量子图像识别方案。两者都是基于信息处理引擎的想法，重复执行特定的循环。该循环涉及有源量子系统与信息池的耦合和解耦，该有源量子系统可以由编码有所需优化问题的Sherrington-Kirkpatrick自旋玻璃建模。Sachdev-Ye-Kitaev（SYK）模型的量子比特实现将被探索作为信息池的模型。SYK系统是黑洞的全息对偶系统，在指数小的温度下具有有限的熵。这允许在自旋玻璃的多个局部极小值之间的量子隧穿。在每个周期结束时执行的量子测量操作提供了一组逐步改进的候选最佳自旋配置。该项目的一个目标是评估在这样一个集合中找到真正最优值的概率。另一个目标是优化循环，以确定信息引擎效率的理论界限。该项目的NSF-BSF部分将处理工作物质的约化密度矩阵方面的理论描述。我们期望找到一个类似Lindblad的演化方程。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。