INSPIRE: Architectural Principles of Coherent Quantum Networks and Circuits

该INSPIRE项目由数学和物理科学理事会物理部的量子信息科学（QIS）计划，数学和物理科学理事会物理部的原子分子和光学物理实验（AMO-E）计划，体育基金会（AF）在计算机和信息科学与工程理事会，电子，工程理事会电气、通信和网络系统（ECCS）部门的光子学和磁器件（EPMD）计划，以及NSF综合活动办公室（OIA）。 一种革命性的计算方法正在开发中，使用量子信息处理。 然而，目前存在的量子信息处理测试平台与理想量子计算机所需的更完美的硬件之间仍然存在差距。 该项目旨在通过寻找使用可变数量的量子行为来逐步改进计算系统的方法来填补这一空白。 该项目将研究如何使用量子效应，以便为信号处理和机器学习应用提供速度，能量和硬件效率方面的好处。 该项目将开发用于量子网络建模的开源软件，并将培训研究生开发量子信息处理系统。这项工作建立在为光子架构中的自主连续时间量子纠错和超低功耗信息处理系统设计电路的基础上。 该项目的理论部分将提供工具来研究在开放量子系统中运行的各种计算机架构中的量子反馈。 这将使用量子随机微分方程（量子场论的一种形式，经过调整以类似于现代工程中广泛使用的普通随机微分方程）来完成，以改进公开可用的开源软件包（GitHub上的Mabuchilab/QNET）并探索将这些方法扩展到纳米光学力学和超导电路QED的方法。 该项目的实验部分将通过测试基于相干反馈量子控制的较低层次的架构原理来补充高级研究，通过结合非线性控制器动力学和脉冲信号场来超越以前的工作。该项目的理论和实验部分将共同推进量子信息社区快速构建复杂相干网络/电路的严格量子光学模型的能力，促进对新的高级架构原理的探索。

This INSPIRE project is jointly funded by the Quantum Information Science (QIS) Program in the Physics Division in the Mathematical and Physical Sciences Directorate, the Atomic Molecular and Optical Physics Experiment (AMO-E) Program in the Physics Division in the Mathematical and Physical Sciences Directorate, the Algorithmic Foundations (AF) Program in the Computing and Communications Foundations Division in the Computer and Information Science and Engineering Directorate, the Electronics, Photonics and Magnetic Devices (EPMD) Program in the Electrical, Communications and Cyber Systems (ECCS) Division in the Engineering Directorate, and the NSF Office of Integrative Activities (OIA). A revolutionary approach to computing is under development using quantum information processing. However, there is still a gap between the testbeds for quantum information processing that exist today, and the more perfect hardware needed for an ideal quantum computer. This project seeks to fill this gap by finding ways to use variable amounts of quantum behavior to incrementally improve computing systems. The project will examine how to use quantum effects in order to provide benefits in terms of speed, energy, and hardware efficiency for applications in signal processing and machine learning. The project will produce open source software for modeling quantum networks and will train graduate students to develop quantum information processing systems.This effort builds on work designing circuits for autonomous continuous-time quantum error correction and ultra-low power information processing systems in photonic architectures. The theoretical component of the project will provide tools to study quantum feedback in various computer architectures operating with open quantum systems. This will be done using quantum stochastic differential equations (a form of quantum field theory that is adapted to resemble ordinary stochastic differential equations that are widely used in modern engineering) in order to improve the publicly-available open source software package (Mabuchilab/QNET on GitHub) and explore ways to extend these methods to nano-optomechanics and superconducting circuit QED. The experimental component of this project will complement the high-level study by testing lower-level architectural principles based on coherent-feedback quantum control, going beyond previous work by incorporating nonlinear controller dynamics and pulsed signal fields. Together, the theoretical and experimental parts of this project will advance the quantum information community's ability to rapidly construct rigorous quantum-optical models for complex coherent networks/circuits, facilitating the exploration of new high-level architectural principles.