[Paper Review] Quantum Computing: Fundamentals, Trends and Perspectives for Chemical and Biochemical Engineers

Amirhossein Nourbakhsh, Mark Jones+8 more|arXiv (Cornell University)|Jan 8, 2022

Quantum Computing Algorithms and Architecture4 citations

TL;DR

This paper provides a comprehensive introduction to quantum computing for chemical and biochemical engineers, covering fundamentals, recent advances, and applications in process optimization, scheduling, and machine learning. It demonstrates that quantum-inspired algorithms can improve fault detection in chemical processes and reduce logistics costs by 2–5%, with future potential as qubit counts and error correction improve.

ABSTRACT

We use the benefits and components of classical computers every day. 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. Quantum computing (QC) is a computation model that uses quantum physical properties to solve such problems. 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. It can be applied in a variety of areas, such as computer science, mathematics, chemical and biochemical engineering, and the financial industry. 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. Thus, the paper begins by explaining the fundamental concepts of QC. 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. 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. A bibliography analysis covers the comprehensive literature in QC and analyzes quantum computing research in chemical engineering on various publication topics, using Clarivate analytics covering the years 1990 to 2020. After the bibliographic analysis, relevant applications of QC in chemical and biochemical engineering are highlighted and a conclusion offers an outlook of future directions within the field.

Motivation & Objective

• To introduce quantum computing fundamentals to chemical and biochemical engineering researchers unfamiliar with the field.
• To address the lack of comprehensive reviews on quantum computing in chemical engineering literature.
• To analyze trends and research output in quantum computing applications within chemical and biochemical engineering from 1990 to 2020.
• To highlight practical applications of quantum computing in process scheduling, control, and optimization.
• To project future directions for quantum computing in chemical and biochemical engineering, especially with increasing qubit counts and error correction.

Proposed method

• Conducted a bibliographic analysis using Clarivate Analytics to examine research trends in quantum computing and chemical engineering from 1990 to 2020.
• Reviewed and summarized state-of-the-art quantum computing applications in chemical and biochemical engineering, including scheduling, process control, and optimization.
• Formulated scheduling problems as QUBO (Quadratic Unconstrained Binary Optimization) problems and implemented them on Fujitsu’s quantum-inspired digital annealer.
• Applied quantum-inspired machine learning techniques, including restricted Boltzmann machines (RBMs) and quantum sampling, to generate training data for deep learning models.
• Evaluated quantum machine learning (QML) and quantum-inspired regression for high-dimensional, low-rank matrices and sparse data.
• Used quantum circuit modeling and quantum annealing to solve unit commitment, facility location, and heat exchanger network problems.

Experimental results

Research questions

• RQ1 How can quantum computing be applied to optimize complex chemical and biochemical engineering processes?
• RQ2 What are the current trends and growth patterns in quantum computing research within chemical and biochemical engineering?
• RQ3 To what extent can quantum-inspired algorithms improve fault detection and process control in chemical systems?
• RQ4 How do quantum computing models compare to classical methods in scheduling and logistics optimization?
• RQ5 What are the practical limitations of current quantum hardware in solving large-scale chemical engineering problems?

Key findings

• The number of publications on quantum computing in chemical and biochemical engineering grew by 10.29% in 2020, with 1,010 papers published and an average of 43.51 citations per paper.
• Quantum-inspired digital annealing reduced logistics costs by 2–5% and determined optimal routes in 30 minutes, demonstrating practical scalability.
• Quantum sampling via RBMs improved fault detection rates in a CSTR model, outperforming classical deep learning for certain fault types.
• The Tennessee Eastman process showed superior fault detection with quantum-adapted deep learning models, particularly for specific fault categories.
• Quantum-inspired machine learning is effective for low-rank, high-dimensional matrices, while quantum machine learning excels with high-rank, sparse data.
• Mathematical proof confirms that quantum convolutional neural networks (QCNNs) can scale efficiently with large datasets, offering a potential advantage over classical CNNs.