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A Quantum Photonic Chip for Binary Classification

Hsiao-Chung Lin, Hui Zhang, Hong Nian Cai, Paul Griffin, A. Q. Liu

Published Jan 1, 2023• 0 citations
Publisher

Abstract

On the silicon photonic chip, we apply Mach Zehnder Interferometer (MZI) and optical network to manipulate the phase difference and amplitude distribution of the input light. The two or three-dimensional input data is encoded in the light amplitude distribution and the genetic algorithm is applied to update the angle parameters of the MZIs to improve the classification result.

DOI
10.1364/cleo_at.2023.jw2a.64

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On the silicon photonic chip, we apply Mach Zehnder Interferometer (MZI) and optical network to manipulate the phase difference and amplitude distribution of the input light. The two or three-dimensional input data is encoded in the light amplitude distribution and the genetic algorithm is applied to update the angle parameters of the MZIs to improve the classification result.
Interest in quantum computing is rapidly growing in the scientific community as such technology could be the key to enable intensive use of machine learning and big data algorithms. Quantum computers can be conceived from basic photonic elements, such as Mach-Zehnder interferometers (MZI), but they still need control mechanisms and sensing elements from traditional VLSI technology. In this study, we present an ongoing study on a potential architecture for these basic photonic circuits. We realized two Photonic Integrated Circuit (PIC) test structures embedding metallic thermistors as phase shifters and silicon photodiodes as output detectors. We present a complete characterization of the phase shifter elements for an effective on-chip PIC interaction. To induce a consistent phase shift still retaining coherence of light, we act on the phase-shifters by means of a closed-loop control. By controlling through Pulse-Width Modulation the phase shifters, we drive the path of photons accurately in the PIC, demonstrating the effectiveness of the proposed configuration for the management of a photonic chip.
In recent years, optical neural networks have attracted widespread attention, due to their advantages of high speed, high parallelism, high bandwidth, and low power consumption. Photonic unitary neural network is a kind of neural networks that utilize the principles of unitary matrices and photonics to perform computations. In this paper, we design a photonic unitary neural network based on Mach–Zehnder interferometer arrays. The results show that the network has a good performance on both triangular and circular binary classification datasets, where most of the data points are correctly classified. The accuracies achieve 97% and 95% for triangular and circular datasets, with the loss function values of 0.023 and 0.046, respectively.
Programmable photonic integrated circuits have mainly been developed based on the single wavelength channel operation of fundamental building blocks consisting of Mach-Zehnder interferometers (MZIs) with tunable phase shifters. We propose and study optical circuit models consisting of cascaded optical resonators that enable the independent operation of multiple wavelength channels in a more compact footprint than the conventional MZIs. By adopting experimental values reported for silicon micro-ring resonators, the fidelities of different types of 2×2 unitary transformations and higher-dimensional unitary transformations are examined by employing the Reck algorithm and the Clements algorithm.