Cryogenic system for the exploration of low-temperature neuromorphic photonic systems

这个NSERC-RTI提案将通过购买低温系统来支持低温神经形态光子学平台的建设，这可能是加拿大的第一个。我们在神经形态光子学方面的工作（Shastri等人，Nature Photonics 15，2021）使用光学物理学原理来模拟大脑中的神经元和突触（神经元到神经元的连接），以创建光子神经网络（即，由光驱动的芯片），其速度比电子处理器快几个数量级。我们一直在扩展这项工作，以构建与量子技术接口的低温光子神经网络，从而实现量子光子机器学习和实时量子态断层扫描的应用。机器学习和量子信息的物理混合将在两个领域都取得进展--量子用于机器学习，机器学习用于量子。该提案要求为购买低温系统提供资金，该系统包括低温恒温器、吸附式冰箱、温度控制器和传感器、水冷氦压缩机和真空泵系统。该低温系统将使我们能够将硅光子神经网络与单光子源和超导纳米线单光子探测器（SNSPD）的单光子量子门连接起来。我们正在研究量子信息处理，通过设计神经形态处理器和量子门之间的硬件接口，在单个硅光子芯片上实现混合量子经典神经网络，来改进光子神经网络实现的机器学习算法。相反，我们正在研究利用光子神经网络进行机器学习，以实现量子设备的实时控制。在与Rotenberg Group（Queen's）的合作中，我们正在光子神经电路和单光子源之间硬连接接口，这将使脆弱的量子光学状态的处理和控制成为实时的基础，所有量子光子技术。该低温系统将支持纳米光子学、机器学习和量子信息处理领域每年15 HQP的实验工作。2019年，全球人工智能和量子计算市场的价值分别为399亿美元和5.071亿美元。如果这些领域合并，它们的整体市场价值可能会大于它们部分的总和。在这个领域的交叉点工作的HQP将处于独特的地位，通过创建初创企业或成为该领域的领先学者来进入组合技术市场。

This NSERC-RTI proposal will support the building of a cryogenic neuromorphic photonics platform-perhaps the first in Canada-through the purchase of a cryogenic system. Our work on neuromorphic photonics (Shastri et al. Nature Photonics 15, 2021) uses principles of optical physics to mimic neurons and synapses (neuron-to neuron connections) in the brain to create photonic neural networks (i.e., chips powered by light) that are orders of magnitude faster than electronic processors. We have been extending this work to build low-temperature photonic neural networks interfaced with quantum technologies to enable applications in quantum photonic machine learning and real-time quantum state tomography. The physical hybridization of machine learning and quantum information stands to advance in both fields-quantum for machine learning, and machine learning for quantum. This proposal requests funding for the purchase of a cryogenic system that includes a cryostat, a sorption fridge, temperature controllers and sensors, a water-cooled helium compressor, and a vacuum pumping system. This cryosystem will allow us to interface our silicon photonic neural networks with single-photon quantum gates with single photon sources and superconducting nanowire single-photon detectors (SNSPDs). We are investigating quantum information processing to improve machine learning algorithms implemented with photonic neural networks by designing a hardware interface between neuromorphic processors and quantum gates enabling hybrid quantum-classical neural network on a single silicon photonic chip. Conversely, we are investigating machine learning with photonic neural networks for the real-time control of quantum devices. In collaboration with Rotenberg Group (Queen's), we are hardwiring an interface between a photonic neural circuit and single photon sources, that will enable processing and control of the fragile quantum optical states, that underlie all quantum photonic technologies, in real-time. The cryosystem will support the experimental work of 15 HQP/year across nanophotonics, machine learning, and quantum information processing. In 2019, the global AI and quantum computing markets were valued at $39.9B and $507.1M, respectively. If these fields merge, their overall market value could be greater than the sum of their parts. HQP working at the intersection of this field will be uniquely positioned to tap into the combined technology market by creating start-ups or become leading academics in this field.