Therefore, this article reviews and summarizes the state of the art to gain insight into how quantum computation can benefit and optimize chemical engineering issues. The second contribution this publication tries to tackle is the fact that the chemical engineering literature still lacks a comprehensive review of the recent advances of QC. Thus, the paper begins by explaining the fundamental concepts of QC. The main goal of this paper is to give an overview to chemical and biochemical researchers and engineers who may not be familiar with quantum computation. It can be applied in a variety of areas, such as computer science, mathematics, chemical and biochemical engineering, and the financial industry. QC is at the early stage of large-scale adoption in various industry domains to take advantage of the algorithmic speed-ups it has to offer. Quantum computing (QC) is a computation model that uses quantum physical properties to solve such problems. However, there are many types of problems which, as they grow in size, their computational complexity grows larger than classical computers will ever be able to solve. We use the benefits and components of classical computers every day. The integrated diffractive optical network (IDNN) chip demonstrates a promising avenue towards scalable and low-power-consumption optical computational chips for optical-artificial-intelligence. A ~10-fold reduction in both footprint and energy consumption, as well as equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the MNIST and Fashion-MNIST datasets. The footprint and energy consumption scales linearly with the input data dimension, instead of the quadratic scaling in the traditional ONN framework. Here, we propose the integrated diffractive optical network for implementing parallel Fourier transforms, convolution operations and application-specific optical computing using two ultracompact diffractive cells (Fourier transform operation) and only N MZIs. Traditional experimental implementations need N 2 units such as Mach-Zehnder interferometers (MZIs) for an input dimension N to realize typical computing operations (convolutions and matrix multiplication), resulting in limited scalability and consuming excessive power. Large-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical computing.
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