Collaborative Research: EPiQC: Enabling Practical-Scale Quantum Computation

合作研究：EPiQC：实现实用规模的量子计算

• 批准号: 1730449
• 负责人: Frederic Chong
• 金额: $ 494.32万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1730449&HistoricalAwards=false
• 关键词: Collaborative; Research; EPiQC; Enabling; Practical

Quantum computing sits poised at the verge of a revolution. Quantum machines may soon be capable of performing calculations in machine learning, computer security, chemistry, and other fields that are extremely difficult or even impossible for today's computers. Few of these limitless possibilities on the horizon that quantum computing could lead to are better drug discovery, more efficient photovoltaics, new nanoscale materials, and perhaps even more efficient food production. These benefits will be enabled by substantially improving the ability to solve computational problems in quantum chemistry, quantum simulation, and optimization. These dramatic improvements arise because each additional quantum bit doubles the potential computing power of a machine, accumulating exponential gains that could eventually eclipse the world's largest supercomputers. Quantum computing will also drive a new segment of the computing industry, providing new strategies for specific applications that increase computational power even as physical limits slow improvements in classical silicon-chip technology. This multi-institutional project, Enabling Practical-scale Quantum Computing (EPiQC) Expedition, will help bring the great potential of this new paradigm into reality by reducing the current gap between existing theoretical algorithms and practical quantum computing architectures. Over five years, the EPiQC Expedition will collectively develop new algorithms, software, and machine designs tailored to key properties of quantum device technologies with 100 to 1000 quantum bits. This work will facilitate profound new scientific discoveries and also broadly impact the state of high-performance computing. To prepare the U.S. workforce for this revolution in computing, we need to educate citizens to think about computing from a quantum perspective, integrating concepts such as probability and uncertainty into the digital lexicon. The EPiQC Expedition will design teaching curricula and distribute exemplar materials for students ranging from primary school to engineers in industry. EPiQC will also establish an academic-industry consortium which will share educational and research products and accelerate the pace of quantum computing design and applications. Because quantum computing is a new branch of computer science, it will require entirely new types of algorithms and software. In order to produce practical quantum computation in the near future, these elements cannot be developed in isolation. Instead, researchers must increase the efficiency of quantum algorithms running on quantum machines through the simultaneous design and optimization of algorithms, software and machines. New algorithms and software need to know what specific machine operations are easy or difficult in a given quantum technology and must be prepared to produce useful answers from imperfect results from imperfect machines. Software also needs to verify that the computation executed correctly as expected, an especially difficult task given that conventional machines cannot simulate even a modest-size quantum machine. The EPiQC Expedition unites experts on algorithms, software, architecture, and education to develop these elements in parallel. Overall, EPiQC will increase the efficiency of practical quantum computations by 100 to 1000 times, effectively bringing quantum computing out of the laboratory and into practical use 10-20 years sooner than through technology advances alone. The project identifies 4 thrusts: algorithmic innovations, compiler development, verification, and the broader impact tasks of developing education modules. The algorithmic tasks are organized into the subdomains of optimization, computational chemistry, and the discovery of separations between quantum and classical speedup. The compiler tasks are more milestone driven - development of technology libraries, development of various compilation techniques which leverages these libraries, as well as novel error correction schemes. The project will tie the tool chain closely to the underlying hardware and fault-tolerance mechanisms.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.

量子计算正处于一场革命的边缘。量子机可能很快就能在机器学习、计算机安全、化学和其他领域进行计算，这些领域对于今天的计算机来说是极其困难的，甚至是不可能的。在量子计算可能带来的这些无限可能性中，几乎没有几个是更好的药物发现、更高效的光伏、新的纳米材料，甚至更高效的食品生产。这些好处将通过大幅提高解决量子化学、量子模拟和优化中的计算问题的能力来实现。之所以出现这些戏剧性的改进，是因为每增加一个量子比特，机器的潜在计算能力就会翻一番，积累的指数级增长最终可能使世界上最大的超级计算机黯然失色。量子计算还将推动计算行业的一个新领域，为特定应用提供新的战略，这些战略可以提高计算能力，尽管物理限制减缓了经典硅芯片技术的进步。这个多机构项目，实现了实际规模的量子计算(EPiQC)考察，将通过缩小现有理论算法和实际量子计算体系结构之间的差距，帮助将这一新范式的巨大潜力变为现实。在五年的时间里，EPiQC探险队将共同开发新的算法、软件和机器设计，以适应具有100到1000个量子比特的量子器件技术的关键特性。这项工作将促进深刻的新科学发现，并对高性能计算的状况产生广泛影响。为了让美国劳动力为这场计算革命做好准备，我们需要教育公民从量子的角度思考计算，将概率和不确定性等概念整合到数字词典中。EPiQC考察队将为从小学到工业工程师的学生设计教学课程并分发示范材料。EPiQC还将建立一个学术-产业联盟，共享教育和研究产品，并加快量子计算设计和应用的步伐。由于量子计算是计算机科学的一个新分支，它将需要全新类型的算法和软件。为了在不久的将来产生实用的量子计算，这些元素不能孤立地发展。相反，研究人员必须通过同时设计和优化算法、软件和机器来提高量子算法在量子机器上运行的效率。新的算法和软件需要知道在给定的量子技术中，哪些特定的机器操作容易或困难，并且必须准备好从不完美的机器的不完美结果中产生有用的答案。软件还需要验证计算是否按预期正确执行，考虑到传统机器甚至无法模拟中等大小的量子机器，这是一项特别困难的任务。EPiQC考察队联合了算法、软件、体系结构和教育方面的专家，以并行开发这些要素。总体而言，EPiQC将把实际量子计算的效率提高100到1000倍，有效地将量子计算从实验室带入实际应用，比仅通过技术进步提前10-20年。该项目确定了四个推动力：算法创新、编译器开发、验证和开发教育模块的更广泛的影响任务。算法任务被组织成优化、计算化学和发现量子和经典加速比之间的分离的子域。编译器任务更具里程碑意义--开发技术库，开发利用这些库的各种编译技术，以及新的纠错方案。该项目将工具链与底层硬件和容错机制紧密联系在一起。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Using Spectral Graph Theory to Map Qubits onto Connectivity-limited Devices

使用谱图理论将量子位映射到连接受限的设备上

• DOI: 10.1145/3436752
• 发表时间: 2021
• 期刊: ACM Transactions on Quantum Computing
• 作者: Lin, Joseph X.;Anschuetz, Eric R.;Harrow, Aram W.
• 通讯作者: Harrow, Aram W.

Fault-tolerant weighted union-find decoding on the toric code

toric 码上的容错加权并查解码

• DOI: 10.1103/physreva.102.012419
• 发表时间: 2020
• 期刊: Physical Review A
• 影响因子: 2.9
• 作者: Huang, Shilin;Newman, Michael;Brown, Kenneth R.
• 通讯作者: Brown, Kenneth R.

Qunity: A Unified Language for Quantum and Classical Computing

Qunity：量子和经典计算的统一语言

• DOI: 10.1145/3571225
• 发表时间: 2023
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Voichick, Finn;Li, Liyi;Rand, Robert;Hicks, Michael
• 通讯作者: Hicks, Michael

Adaptive job and resource management for the growing quantum cloud

针对不断增长的量子云的自适应作业和资源管理

• DOI: 10.1109/qce52317.2021.00047
• 发表时间: 2021
• 期刊: 2021 IEEE International Conference on Quantum Computing and Engineering (QCE
• 作者: Ravi, Gokul Subramanian;Smith, Kaitlin N.;Murali, Prakash;Chong, Frederic T.
• 通讯作者: Chong, Frederic T.

Quantum algorithms for jet clustering

• DOI: 10.1103/physrevd.101.094015
• 发表时间: 2019-08
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Annie Y. Wei;P. Naik;A. Harrow;J. Thaler