Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency.The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.Quantum computing (QC) is the next frontier in computation and has attracted a lot of attention from the scientific community in recent years. QC provides a novel approach to help solve some of the most complex optimization problems while offering an essential speed advantage over classical methods [10]. This is evident from QC techniques like Shor's algorithm for integer factorization [11], Grover's search algorithm for unstructured databases [12], quantum algorithm for linear system of equations [13], and many more [14]. Quantum adiabatic algorithms too are efficient optimization strategies that quickly search over the solution space [15]. Quantum computers perform computation by inducing quantum speedups whose scaling far exceeds the capability of the most powerful classical computers. QC's major applications can be perceived in areas of optimization, machine learning, cryptography, and quantum chemistry [16]. Despite the contrasting views on QC's viability and performance, there is no doubt that QC holds great promise to open up a new era of computing.QC-based solution approaches are in their earliest stages of development compared to their much more matured classical counterparts. Current quantum machines have very limited functionality in the context of optimization, such that QC hardware and algorithms are inadequate for large-scale optimization problems. Although it has been shown that some optimization problems relevant to energy systems can be solved using quantum computers, their performance deteriorates with increasing size and complexity [17]. A number of technological limitations face commercially available quantum computers, such as relatively small number of qubits with limited connectivity, and lack of quantum memory. Therefore, harnessing the complementary strengths of classical and...

The purpose of this paper is to explore the applications of quantum computing to energy systems optimization problems and discuss some of the challenges faced by quantum computers with techniques to overcome them. The basic concepts underlying quantum computation and their distinctive characteristics in comparison to their classical counterparts are also discussed. Along

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