Quantum computing for transport research: an introduction, systematic review, and perspective

Abstract

Transport engineering has significant potential to benefit from quantum computing. The rise of intelligent transport systems, autonomous vehicles, and the Internet of Things has created an unprecedented demand for efficient information processing and computational optimisation. Accordingly, transport engineers and scientists have explored the ever-improving capabilities of quantum computers in an effort to meet this demand. Motivated by this growing interest, this paper sets out four aims: (1) to introduce the fundamental aspects of quantum computing relevant to the transport domain, (2) to identify transport-related problems which are suitable for quantum acceleration, (3) to develop a pipeline for solving these problems, and (4) to provide a systematic review of the existing literature. For the latter, a systematic search of the Scopus database (and supplemented by additional citation sources) identified 103 studies for inclusion following PRISMA 2020 guidelines. While a diverse set of use cases have been proposed, we conclude that future research should prioritise problems where quantum computation offers a clear practical benefit. To this end, we suggest promising directions to guide further work in this burgeoning subfield.

Figures (9)

• Figure 1: Venn diagram displaying the relationship between computing hardware and their associated algorithms. Curly brackets indicate that algorithms below are a subset of algorithms above.
• Figure 2: Basic structure of the VQE. A classical subroutine optimises a set of variational parameters. These are passed to the quantum subroutine, where they parameterise components of a quantum circuit. The general structure of the circuit is termed the ansatz. The circuit depicted here is for the QAOA. The outcome of the circuit is a single bitstring. The quantum circuit is repeated many times to obtain statistics which describe the underlying quantum state produced by the circuit. The distribution is used to calculate the expectation value of the cost function. This value is passed back to the classical subroutine which updates the variational parameters if convergence is not yet reached. The standard circuit for QAOA consists of four key components. Firstly, the register is initialised such that each qubit state is $|0\rangle$. Secondly, a Hadamard gate is applied to each qubit to produce an equal superposition of bitstrings in the computational register. Thirdly, apply a sequence of cost and mixing unitaries which are parameterised by the set of angles determined in the classical subrourine. Repeat this process $p$ times. Finally, measure the qubits to obtain a single bitstring.
• Figure 3: Pipeline for integrating quantum computing into the transport engineering solution stack.
• Figure 4: PRISMA 2020 flow diagram for the systematic review on quantum computing for transport-related studiespage_prisma_2020. The database search was conducted in Scopus (n = 283); additional records were identified from Google Scholar and citation searching (n = 168).
• Figure 5: Number of transport-related quantum publications per year as included in this review.