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

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.

量子计算被认为是超越摩尔法律规模的最有希望的替代方案之一，并为选定的应用程序提供了急剧的加速，并进一步提供了信息技术革命。在过去的四十年中进行的开创性研究表明，大规模量子系统可用于深远的应用程序，从复杂量子物质的模拟到通用量子信息处理。在过去的十年中，一些量子硬件平台取得了重大进步。被困在光学镊子阵列中的中性原子最近已成为一个非常有承诺的实验平台，用于可编程量子模拟和量子计算。这些系统很容易缩放到大数字，并在实验上证明可以在量子计算过程中动态重新配置纠缠的量子耦合，因此，可以动态地命名可重新配置的原子阵列（DRAAS）。 Draa引入了许多独特的机会。特别是，它支持一个缓存计算模型，其中可以在特定的原子阵列中“缓存”以进行后期计算，从而模仿现代CPU的体系结构。此外，涉及错误校正的逻辑量子位的算法可以非常有效地实现，许多控件具有许多逻辑（而不是物理）Qubits的缩放。但是，要充分利用这种独特的体系结构，需要开发编译的新方法，因为编程DRAA不仅涉及Qubit放置和栅极调度，还涉及原子运动。此外，需要在可用资源的约束下考虑和优化错误校正。本项目旨在开发一种新颖的DRAA编译器，该编译器仅考虑量子位置，门调度，原子安排，原子移动以及在常见的编译框架下的选定误差校正的问题。特别是，它解决了四个相互关联的问题，包括（i）可以有效支持映射，调度和原子运动的DRAA的可扩展汇编，这些draas具有成千上万个原子的DRAA； （ii）基于缓存的DRAA体系结构的有效支持，该体系结构具有内存区域，纠缠区和一个读取区，并具有数据重用和数据移动优化； （iii）对DRAA上的硬件有效误差校正的自定义支持，该校正充分利用了原子运动能力，横向属性和特定于DRAA特定的错误偏见； （iv）在资源约束下进行选择性误差校正，其中分析和识别错误关键性。算法和汇编流将在哈佛大学开发的DRAA量子计算机上进行实验测试。该项目是加利福尼亚大学洛杉矶分校（UCLA）计算机科学系和哈佛物理系的研究人员团队的跨学科合作工作。调查人员计划将研究与教育融合，以使学生了解量子计算的激动人心的机会，并培训新一代学生，以便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具有深刻的知识。该项目的研究结果将通过各种会议的出版物和教程广泛传播。该团队将使用汇编系统的开源版本和该项目下开发的DRAA实验数据进一步支持技术转移和社区范围的参与。最后，调查人员计划通过高中夏季计划以及具有各种多样性和外展计划的合作伙伴关系来扩大计算的参与，例如UCLA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的法定任务，并通过评估基础的智力和广泛的评估来评估诚实的支持。