Collaborative Research: FET: Medium: Efficient Compilation for Dynamically Reconfigurable Atom Arrays

合作研究：FET：中：动态可重构原子阵列的高效编译

• 批准号: 2313083
• 负责人: Jason Cong
• 金额: $ 63万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313083&HistoricalAwards=false
• 关键词: Collaborative; Research; FET; Medium; Efficient

Quantum computing is considered one of the most promising alternatives to go beyond the Moore’s Law scaling and provide drastic acceleration for selected applications and further the information technology revolution. The groundbreaking research carried out over the past four decades indicates that large-scale quantum systems may be used for far-reaching applications ranging from simulations of complex quantum matter to general purpose quantum information processing. Several quantum hardware platforms have made substantial advances in the past decade. Neutral atoms trapped in arrays of optical tweezers have recently emerged as an exceptionally promising experimental platform for programmable quantum simulations and quantum computation. These systems are readily scaled to large numbers and demonstrated experimentally that the qubit coupling for entanglement can be reconfigured dynamically during the quantum computation process, thus, are named dynamically reconfigurable atom arrays (DRAAs). DRAA introduces a number of unique opportunities. In particular, it supports a cache-compute computation model, where temporary data can be “cached” in a specific atom array for later computation, mimicking the architecture of modern CPUs. Moreover, algorithms involving error-corrected logical qubits can be implemented very efficiently, with a number of controls that scales with a number of logical (rather than physical) qubits. However, to take full advantage of this unique architecture, novel methods for compilation need to be developed, as programming a DRAA involves not only qubit placement and gate scheduling, but also atom movement. In addition, error correction needs to be considered and optimized under the constraint of available resources.This project aims at developing a novel DRAA compiler that simultaneously considers the problems of qubit placement, gate scheduling, atom movement, and selected error correction under a common compilation framework. In particular, it addresses four interrelated problems, including (i) Scalable compilation for DRAA that can efficiently support mapping, scheduling, and atom movement for DRAAs with hundreds to tens of thousands of atoms; (ii) Efficient support of the cache-based DRAA architecture, which has a memory zone, an entanglement zone, and a readout zone, with data reuse and data movement optimization; (iii) Customized support for hardware-efficient error correction on DRAAs that takes full advantage of atom movement capability, transversal property, and DRAA-specific error-biasing; and (iv) Selective error correction under resource constraints, where error criticality is analyzed and identified. The algorithms and compilation flow will be tested experimentally on the DRAA quantum computer developed at Harvard University. The project is an interdisciplinary collaboration effort by a team of researchers from the University of California Los Angeles (UCLA) Computer Science Department and the Harvard Physics Department. The investigators plan to integrate the research with education to expose students to the exciting opportunities of quantum computing and train a new generation of students so that they have deep knowledge in both quantum computing device technologies and large-scale design automation and optimization. The research results from this project will be disseminated widely via publications and tutorials at various conferences. The team will further facilitate the technology transfer and community-wide participation using open-source releases of both the compilation system and the DRAA experimental data developed under this project. Finally, the investigators plan to broaden the participation in computing via high-school summer programs and partnerships with various diversity and outreach programs, such as the Center for Excellence in Engineering and Diversity at UCLA and CUAEngage at Harvard.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.

量子计算被认为是最有前途的替代方案之一，可以超越摩尔定律，为选定的应用程序提供大幅加速，并进一步推动信息技术革命。 过去四十年进行的开创性研究表明，大规模量子系统可用于影响深远的应用，从复杂量子物质的模拟到通用量子信息处理。过去十年中，一些量子硬件平台取得了实质性进展。光镊阵列中捕获的中性原子最近已成为用于可编程量子模拟和量子计算的非常有前途的实验平台。这些系统很容易扩展到大量，并通过实验证明用于纠缠的量子位耦合可以在量子计算过程中动态重新配置，因此被称为动态可重构原子阵列（DRAA）。 DRAA 引入了许多独特的机会。特别是，它支持缓存计算计算模型，可以将临时数据“缓存”在特定的原子数组中以供以后计算，模仿现代 CPU 的架构。此外，涉及纠错逻辑量子位的算法可以非常有效地实现，通过许多控制来扩展逻辑（而不是物理）量子位的数量。然而，为了充分利用这种独特的架构，需要开发新的编译方法，因为对 DRAA 进行编程不仅涉及量子位放置和门调度，还涉及原子移动。此外，还需要在可用资源的约束下考虑和优化纠错。该项目旨在开发一种新颖的DRAA编译器，该编译器在通用编译框架下同时考虑量子位放置、门调度、原子移动和选择性纠错问题。特别是，它解决了四个相互关联的问题，包括（i）DRAA的可扩展编译，可以有效地支持具有数百到数万个原子的DRAA的映射、调度和原子移动； (ii) 高效支持基于缓存的 DRAA 架构，该架构具有存储区、纠缠区和读出区，具有数据重用和数据移动优化功能； (iii) 对 DRAA 上硬件高效纠错的定制支持，充分利用原子移动能力、横向属性和 DRAA 特定的错误偏差； (iv) 资源限制下的选择性纠错，分析和识别错误的严重性。算法和编译流程将在哈佛大学开发的 DRAA 量子计算机上进行实验测试。该项目是加州大学洛杉矶分校 (UCLA) 计算机科学系和哈佛物理系研究人员团队的跨学科合作成果。研究人员计划将研究与教育结合起来，让学生接触到量子计算令人兴奋的机会，并培养新一代学生，使他们在量子计算设备技术和大规模设计自动化和优化方面拥有深入的知识。该项目的研究成果将通过各种会议的出版物和教程广泛传播。该团队将利用该项目下开发的编译系统和 DRAA 实验数据的开源版本，进一步促进技术转让和社区范围内的参与。最后，研究人员计划通过高中暑期项目以及与各种多样性和外展项目的合作来扩大对计算的参与，例如加州大学洛杉矶分校的工程和多样性卓越中心和哈佛大学的 CUAEngage。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。