Quantum computation for optical networks

Timothy C. Ralph, Geoff J. Pryde

Rishab Parthasarathy, Rohan Tan Bhowmik

Large machine learning models based on Convolutional Neural Networks (CNNs) with rapidly increasing numbers of parameters are being deployed in a wide array of computer vision tasks. However, the insatiable demand for computing resources required to train these models is fast outpacing the advancement of classical computing hardware, and new frameworks including Optical Neural Networks (ONNs) and quantum computing are being explored as future alternatives. In this work, we report a novel quantum computing based deep learning model, the Quantum Optical Convolutional Neural Network (QOCNN), to alleviate the computational bottleneck in future computer vision applications. Using the MNIST dataset, we have benchmarked this new architecture against a traditional CNN model based on the seminal LeNet architecture. We have also compared the performance against previously reported ONNs, the GridNet and ComplexNet, and a Quantum Optical Neural Network (QONN) that we built by combining the ComplexNet with quantum-inspired sinusoidal nonlinearities. Our work extends the prior research on QONN by adding quantum convolution and pooling layers. We have evaluated the models using the metrics of accuracies, confusion matrices, Receiver Operating Characteristic curves, and Matthews Correlation Coefficients. The performance of the models were similar, and the metrics indicated that the new QOCNN model is robust. Finally, we estimated the gains in computational efficiencies from executing this novel framework on a quantum computer, concluding that switching to a quantum computing based approach to deep learning may result in comparable accuracies to classical models while simultaneously achieving unprecedented boosts in computational performances and power allocation.

Jincheng Zhang

This paper investigates the potential of topological optical networks for realizing stable photonic quantum computation. The core claim centers on leveraging the non-Abelian anyonic effects within these networks to mitigate noise, a significant challenge in traditional photonic quantum computing. The proposed mechanism involves constructing a quantum computing system utilizing topological optical networks, specifically targeting the reduction of decoherence and errors. This approach offers a novel pathway toward robust quantum computation by harnessing the unique topological properties of the network. The research explores key aspects of this system, including network design, control strategies, and error correction protocols, emphasizing the advantages of topological protection. The theoretical framework presented demonstrates the feasibility of this method and highlights its potential to advance the field of quantum information processing. Further research is needed to fully realize the capabilities of this system.

Kae Nemoto, William J. Munro, Timothy P. Spiller

Hui Zhang

In recent years, optical computing becomes a powerful task-specific platform, under the increasing demand for computing power (especially for neural networks). This doctoral thesis is devoted to the design, simulation, and testing of integrated silicon photonic circuits for optical neural networks and machine-learning-assisted quantum computing tasks. In Chapter 3, we demonstrate an optical neural chip that implements complex-valued neural networks and benchmark its performance under practical settings, proving strong learning capabilities. In Chapter 4, we demonstrate an efficient, physics-agnostic, and closed-loop protocol for training optical neural networks on chip. In Chapter 5, we demonstrate a silicon photonic chip that achieves chip-to-chip teleportation of high-dimensional quantum states, using the trainable quantum autoencoders. High fidelities are achieved between the input qutrit and the qutrit decoded from the teleported state. Our results present a promising avenue towards realizing deep optical neural networks and challenging quantum computing tasks with dedicated integrated silicon photonic circuits.

Daniel F. Walls, Gerard J Milburn