QII-TAQS: Quantum Machine Learning with Photonics

深度学习正在为计算带来革命性的变化，应用范围越来越广，从自然语言处理到粒子物理再到癌症诊断。通过算法设计和专用硬件开发的结合，这些进步成为可能。量子计算虽然还处于萌芽阶段，但正在经历类似的轨迹，目前的硬件与实际实施量子算法所需的规模之间的差距正在迅速缩小。但我们距离能够实现基于门的计算机体系结构的全尺寸量子计算机仍然非常遥远。这种架构需要量子纠错，以使系统对噪声具有健壮性，而现有的量子技术仍然无法实现这一点。该项目旨在通过采用机器学习领域的概念来开发一种新的量子计算方法。与将计算分解为逻辑门的传统方法不同，研究人员将专注于受机器学习和深度学习启发的量子计算体系结构，以实现自然高效和抗噪声的量子协议。这些体系结构非常适合最大限度地发挥当前可用的噪声量子处理器的计算能力，因为机器学习算法可以使用诸如反向传播等有效方法进行训练。该项目代表着一项高度多学科的努力，将量子硬件开发与算法和计算机体系结构设计相结合，以创建可用于量子模拟、机器学习、优化和量子通信的近期应用的量子协议和设备。该项目的成功可能会开启一种全新的量子计算方法，使现有的量子硬件能够有效地解决医学、生物、核物理和基础量子科学等广泛领域的问题。该计划还需要一个强有力的外展努力，通过一系列YouTube教育模块将高中、本科生和研究生教育与公共教育相结合。集成量子光子学能够动态、高保真地产生和操纵光的量子态，因此是开发基于芯片的量子机器学习体系结构的天然平台。该计划利用神经网络的多功能性和量子光学的计算复杂性，开发基于芯片的深度量子光学神经网络，应用于量子计算、模拟、通信、机器学习等领域。该硬件平台受神经网络这一新兴领域的启发，将半导体量子光源(输入编码)与可动态重构的线性光路(矩阵乘法)和强单光子非线性(量子神经元)相结合，开发出新一代量子处理器的新范式。同时，理论工作将开发一个强大的数值平台，以模拟基于硬件平台的量子机器学习协议，并为包括图像和模式识别、优化和量子通信在内的多种应用设计新的协议。因此，硬件和理论之间的强大协作互动将被用来开发一种全新的协议库，这些协议库利用光子的独特物理属性。该项目由Quantum Leap Big Idea Program和工程局电气、通信和网络系统部门共同资助。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。

Deep learning is revolutionizing computing for an ever-increasing range of applications, from natural language processing to particle physics to cancer diagnosis. These advances have been made possible by a combination of algorithmic design and dedicated hardware development. Quantum computing, while more nascent, is experiencing a similar trajectory, with a rapidly closing gap between current hardware and the scale required for practical implementation of quantum algorithms. But we are still extremely far away from a full-scale quantum computer that can implement gate-based computer architectures. Such architectures require quantum error correction to make the system robust against noise, which remains outside the reach of existing quantum technology. This project aims to develop a new approach to quantum computation by adopting concepts from the field of machine learning. In contrast to conventional approaches where computation is decomposed into logical gates, the investigators will focus on quantum computing architectures inspired by machine learning and deep learning to implement quantum protocols that are naturally efficient and robust to noise. These architectures are ideally suited to maximize the computational capabilities of currently available noisy quantum processors because machine learning algorithms can be trained using efficient methods such as back-propagation. The project represents a highly multi-disciplinary effort that combines quantum hardware development with algorithms and computer architecture design to create quantum protocols and devices that can be leveraged for near-term application in quantum simulation, machine learning, optimization, and quantum communication. Success of the project could open a completely new approach to quantum computing that enables currently available quantum hardware to efficiently solve problems in a broad range of fields such as medicine, biology, nuclear physics, and fundamental quantum science. The program also entails a strong outreach effort that integrates education at the high school, undergraduate, and graduate levels with public education through a series of YouTube educational modules. Integrated quantum photonics enables dynamic, high-fidelity generation and manipulation of quantum states of light, and is therefore a natural platform with which to develop chip-based quantum machine learning architectures. Leveraging both the versatility of neural networks and the computational complexity of quantum optics, the program develops chip-based deep quantum optical neural networks for applications in quantum computation, simulation, communication, machine learning, and beyond. Taking inspiration from the burgeoning field of neural networks, this hardware platform combines semiconductor quantum light sources (input encoding) with dynamically reconfigurable linear optical circuitry (matrix multiplication) and strong single photon nonlinearities (the quantum neuron), to develop a new paradigm for next generation quantum processors. In parallel, the theory effort will develop a robust numerical platform to simulate quantum machine learning protocols based on the hardware platform and design new protocols for multiple applications including image and pattern recognition, optimization, and quantum communication. The strong collaborative interactions between hardware and theory will thus be leveraged to develop an entirely new arsenal of protocols that exploit the unique physical properties of photons. This project is jointly funded by Quantum Leap Big Idea Program and the Division of Electrical, Communications, and Cyber Systems in the Directorate for Engineering.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.