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Ken Tanizawa

Publications and source records attributed to Ken Tanizawa.

3 recordsLinked to original sources
arXiv2026

QKD-Integrated Quantum Noise Stream Cipher: An Overview

Quantum Noise Stream Cipher (QNSC) has emerged as a physical-layer encryption technique that exploits quantum noise and non-orthogonal coherent-state modulation to secure optical communication. However, the security of QNSC relies exceedingly on the secrecy and freshness of its seed key. Quantum Key Distribution (QKD), on the other hand, provides information-theoretically secure key exchange rooted in the laws of quantum mechanics. The convergence of these two paradigms, i.e., integrated QKD-QNSC architectures, offers a compelling solution to each of their limitations. In such integrated systems, QKD continuously supplies and refreshes the secret seed key that governs QNSC modulation. Thus, governing a unified security framework that couples provably secure key establishment with high-speed quantum-enhanced physical-layer encryption. This work presents a comprehensive review of QNSC systems, examining their operating principles, security models under various attacks, and their integration with QKD systems. We analyze the security interplay between the key generation and encryption layers and survey experimental demonstrations and architectural progress toward practical deployment. Furthermore, we identify the open challenges and future research directions that must be addressed to realize fully integrated, quantum-secured optical communication networks at a practical scale.

quant-ph↗
arXiv2024

Symmetric silicon microring resonator optical crossbar array for accelerated inference and training in deep learning

Photonic integrated circuits are emerging as a promising platform for accelerating matrix multiplications in deep learning, leveraging the inherent parallel nature of light. Although various schemes have been proposed and demonstrated to realize such photonic matrix accelerators, the in-situ training of artificial neural networks using photonic accelerators remains challenging due to the difficulty of direct on-chip backpropagation on a photonic chip. In this work, we propose a silicon microring resonator (MRR) optical crossbar array with a symmetric structure that allows for simple on-chip backpropagation, potentially enabling the acceleration of both the inference and training phases of deep learning. We demonstrate a $4 \times 4$ circuit on a Si-on-insulator (SOI) platform and use it to perform inference tasks of a simple neural network for classifying Iris flowers, achieving a classification accuracy of 93.3%. Subsequently, we train the neural network using simulated on-chip backpropagation and achieve an accuracy of 91.1% in the same inference task after training. Furthermore, we simulate a convolutional neural network (CNN) for handwritten digit recognition, using a $9 \times 9$ MRR crossbar array to perform the convolution operations. This work contributes to the realization of compact and energy-efficient photonic accelerators for deep learning.

cs.ET↗