A Comprehensive Review of Integrated Photonic Circuits in Quantum Computing

Fauzan Nusyura, Seri Mastura Mustaza, Aqilah Baseri Huddin +4 more

Jonathan C. F. Matthews, Alberto Politi, Anthony Laing +12 more

Dirk Englund

Recent advances in materials, control, and nanofabrication now open the prospect for scalable quantum technologies based on solid-state quantum systems. In particular, photonic integrated circuits (PICs) now allow routing photons with high precision and low loss, and solid-state artificial atoms provide high-quality spin-photon interfaces. The first part of this talk will review progress towards quantum memory-integrated PICs for quantum networks and modular quantum computers. The second part of the talk will consider new directions for processing classical and quantum information in deep learning neural networks architectures.

Rafael Pereira da Silva

Keisuke Kojima, Toshiaki Koike–Akino

The data reuploading trick was originally proposed for quantum computing to achieve the universal approximation property. In this paper, we introduce data reuploading to realize universal non-quantum photonic computing with practical photonic integrated circuits (PICs). We aim to comprehensively discuss the various advantages and implementation considerations of this approach. Our framework can eliminate the need of quantum squeezed lights, photon counters, and nonlinear photonics, which have been essential for enabling photonic neural networks in conventional configurations. Additionally, we explore ways to minimize the optical components by combining multiple functionalities into a single phase shifter, showing competitive performance when compared to using the same number of phase shifters, all without employing any nonlinear photonic devices. Considering these characteristics, our investigation into the use of PICs for data reuploading presents a novel architectural approach to realize photonic neural networks. This approach embodies unique features that distinctly set it apart from traditional photonic neural networks.

Syed Muntasir Mamun

This second volume systematically dissects advanced computational paradigms in quantum photonic computation, shifting the field from theoretical proofs-of-concept to complex, integrated hardware architectures. The work explores fundamental breakthroughs in scaling associative memory, including using multiphoton quantum interference to realize higher-order generalized Hopfield networks and exploiting driven-dissipative cavity quantum electrodynamics with atomic motion to achieve dynamic synaptic plasticity and surpass the theoretical Hopfield capacity limit. To address commercial viability, the volume analyzes the implementation of defect resilience through non-Abelian topological phenomena on photonic integrated circuits (PICs), as well as the development of quantum-limited stochastic optical neural networks that use intrinsic noise as regularization to achieve extreme energy efficiency. Furthermore, it details the heterogeneous integration of electronic-photonic hybrid accelerators (e.g., ADEPT and MIRAGE) for foundry-scale deployment. Finally, the volume investigates deployment in complex, Advanced Paradigms in Quantum Photonic Computation: Comprehensive Architectures, Topological Resilience, and Real-World Applications (Volume II) real-world applications, such as solving NP-hard combinatorial optimization problems in supply chain logistics and pioneering non-pharmacological neuro-lymphaphotonic interventions for Alzheimer's disease. Also available on Academia.edu: https://www.academia.edu/attachments/132159735/download_file?s=portfolio