NSF-BSF: Efficiently Modeling Continuous Quantum Measurements of High-Dimensional Multi-Qubit Systems

NSF-BSF：高维多量子位系统的连续量子测量的高效建模

• 批准号: 1915015
• 负责人: Justin Dressel
• 金额: $ 32.1万
• 依托单位: Chapman University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915015&HistoricalAwards=false
• 关键词: NSF; BSF; Efficiently; Modeling; Continuous

Modern computers developed rapidly, leading to a historically unprecedented wealth of technology. This technological revolution has improved standards of living globally and has become a cornerstone of the modern economy. Recently, the rapid growth of computational power has slowed, in part because the size of hardware components has shrunk to microscopic scales. At microscopic scales, hardware behaves according to the laws of quantum mechanics, which are quite different from the laws expected for traditional computers. These differences have impeded continued growth using established hardware techniques, but also allow for new possibilities. Efforts are ongoing to develop a paradigm of hardware that leverages the nuances of quantum mechanics to accelerate computation. This project contributes to these quantum computing efforts by addressing a pressing simulation problem for superconducting quantum circuits, which are a promising candidate for scalable quantum technology. The difficulty in accurately describing such a quantum circuit grows rapidly with the size of the system, making hardware design challenging. If successful, this work will provide numerical methods and open source software that dramatically simplify this modeling task for common scenarios, which should help accelerate the future development of superconducting quantum circuits.Large numbers of parameters are generally required to describe quantum circuits, making brute force simulation challenging. A particularly important example of this high dimensionality occurs during the standard measurement protocol for quantum circuits. In this protocol, traveling microwave fields couple with microwave resonators, which in turn couple with nonlinear oscillators that have several energy levels. As the traveling field is collected, the quantum system continuously evolves in accordance with the measured stochastic signal, producing complicated dynamics. This project will develop efficient methods for simulating these continuous quantum measurements using several design phases. After developing a full reference numerical model for the microwave amplification and readout circuitry on a multi-component chip, we will develop simplified semi-classical representations that compress the high dimensionality into a smaller number of parameters. These simplifications will extend known weak-field coherent steady-state approximations of the microwave dynamics to account for nonlinear effects. This project will explore the use of machine learning methods, particularly recurrent neural networks, to automatically learn how to compress the dynamics efficiently. In parallel, undergraduates will perform outreach to the local community through demonstrations, videos, and more to raise public literacy of quantum mechanics. This project will deliver open-source software, online interactive notes, and tutorials as part of its broad outreach effort.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.

现代计算机发展迅速，带来了前所未有的技术财富。这场技术革命提高了全球人民的生活水平，并成为现代经济的基石。最近，计算能力的快速增长已经放缓，部分原因是硬件组件的尺寸已经缩小到微观尺度。在微观尺度上，硬件的行为遵循量子力学定律，这与传统计算机的预期定律大不相同。这些差异阻碍了现有硬件技术的持续发展，但也带来了新的可能性。人们正在努力开发一种利用量子力学的细微差别来加速计算的硬件范例。该项目通过解决超导量子电路的紧迫模拟问题，为这些量子计算工作做出了贡献，超导量子电路是可扩展量子技术的有前途的候选者。精确描述这种量子电路的难度随着系统的大小而迅速增加，使得硬件设计具有挑战性。如果成功，这项工作将提供数值方法和开源软件，大大简化常见场景的建模任务，这将有助于加速超导量子电路的未来发展。描述量子电路通常需要大量的参数，这使得蛮力模拟具有挑战性。这种高维性的一个特别重要的例子发生在量子电路的标准测量协议中。在该协议中，行微波场与微波谐振器耦合，微波谐振器又与具有多个能级的非线性振荡器耦合。随着行场的收集，量子系统根据测量到的随机信号不断演化，产生复杂的动力学。该项目将开发有效的方法来模拟这些连续量子测量使用几个设计阶段。在开发了多元件芯片上微波放大和读出电路的完整参考数值模型后，我们将开发简化的半经典表示，将高维压缩成较少数量的参数。这些简化将扩展已知的微波动力学的弱场相干稳态近似，以解释非线性效应。该项目将探索使用机器学习方法，特别是循环神经网络，来自动学习如何有效地压缩动态。与此同时，本科生将通过演示、视频等方式向当地社区推广，以提高公众对量子力学的素养。该项目将提供开源软件、在线互动笔记和教程，作为其广泛推广工作的一部分。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。

项目成果

Counterfactual communication without a trace in the transmission channel

• DOI: 10.1038/s41534-023-00756-y
• 发表时间: 2023-08
• 期刊: npj Quantum Information
• 影响因子: 7.6
• 作者: W. Pan;Xiao Liu;Xiao-Ye Xu;Qin-Qin Wang-Qin;Ze-Di Cheng;Jian Wang;Zhao-Di Liu;Geng Chen;Zong-Quan Zhou;Chuan‐Feng Li;G. Guo;J. Dressel;L. Vaidman

Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning

使用机器学习的超导处理器的量子态估计和跟踪

• 发表时间: 2021
• 期刊: Ph.D. Thesis
• 作者: Barzili, Shiva L.

Failed attempt to escape from the quantum pigeon conundrum

逃离量子鸽子难题的尝试失败

• DOI: 10.1016/j.physleta.2021.127287
• 发表时间: 2021
• 期刊: Physics Letters A
• 影响因子: 2.6
• 作者: Aharonov, Yakir;Bagchi, Shrobona;Dressel, Justin;Reznik, Gregory;Ridley, Michael;Vaidman, Lev
• 通讯作者: Vaidman, Lev

Footprints of quantum pigeons

• DOI: 10.1103/physrevresearch.2.023004
• 发表时间: 2020-02
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: Gregory Reznik;Shrobona Bagchi;J. Dressel;L. Vaidman

Monitoring Fast Superconducting Qubit Dynamics Using a Neural Network

使用神经网络监控快速超导量子位动态

• DOI: 10.1103/physrevx.12.031017
• 发表时间: 2022
• 期刊: Physical Review X
• 影响因子: 12.5
• 作者: Koolstra, G.;Stevenson, N.;Barzili, S.;Burns, L.;Siva, K.;Greenfield, S.;Livingston, W.;Hashim, A.;Naik, R. K.;Kreikebaum, J. M.
• 通讯作者: Kreikebaum, J. M.