(243b) Keynote Talk 2: Utilizing Gate-Based Quantum Computers for Solving Realistic Engineering Optimization Problems

Binary optimization problems are utilized in many engineering challenges; for example, in fault diagnosis, predictive modeling, data processing, electric systems and energy management, scheduling, and equipment design [1]. For large problems, it can be infeasible to find the exact optimal solution, often necessitating the need to apply heuristic methods to find good (but suboptimal) solutions [2, 3]. Given that the scaling of these problems on classical computers is poor, quantum computers are an appealing alternative tool as they scale more favorably and they have been mathematically demonstrated to outperform classical algorithms in certain situations. For example, quantum computers may be able to leverage properties of quantum systems to solve binary problem types such as quadratic unconstrained binary optimization (QUBO) problems [4].

Quantum algorithms have been studied for a broad spectrum of chemical engineering-related applications including tasks in energy management and control [5, 6], routing and logistics optimization problems [7, 8], the process and product design fields [8], the determination of thermodynamic properties [9] and reaction mechanisms [10], and relating to computational chemistry for the study of biological compounds such as proteins [11] and peptides [12]. Additionally, several quantum algorithms have shown potential for solving common binary optimization problems such as QUBOs, including the quantum approximate optimization algorithm (QAOA) [13, 14] and the variational quantum eigensolver (VQE) [14]. We focus on another candidate known as amplitude amplification [15]. However, to date, quantum computers have been unable to outperform the best classical solvers in a fair head-to-head comparison involving a realistic engineering problem [16]. This is due to the wide variety of challenges that currently remain to practical quantum computing, including dealing with noisy quantum circuits, measurement error, short coherence times, restrictions in gate depths, limited numbers of qubits, and qubit connectivity. From an algorithms perspective, it is also unclear how to utilize the unique mathematics of quantum systems to accomplish quantum advantage for a real-world problem.

In this talk, we discuss the potential for the use of quantum computers for solving engineering-related optimization problems, including why quantum computing is so appealing and the current challenges towards practical implementation. First, after giving background on the mathematical framework behind quantum computing, we discuss algorithms that have driven the explosion of research in the field. This includes an overview of the current optimization algorithms proposed for solving QUBO problems on quantum computers. We discuss the advantages and disadvantages of each, motivating our focus on quantum amplitude amplification. Utilizing a discussion of the inner-workings of amplitude amplification, we explain the appeal of the algorithm and quantum computing in general for solving QUBO problems.

[1] Pan, Jeng-Shyang, et al. "A survey on binary metaheuristic algorithms and their engineering applications." Artificial Intelligence Review 56.7 (2023): 6101-6167.

[2] Weinand, Jann Michael, et al. "Research trends in combinatorial optimization." International Transactions in Operational Research 29.2 (2022): 667-705.

[3] Sachdeva, Natasha, et al. "Quantum optimization using a 127-qubit gate-model IBM quantum computer can outperform quantum annealers for nontrivial binary optimization problems." arXiv preprint arXiv:2406.01743 (2024).

[4] Koch, Daniel, et al. "Variational amplitude amplification for solving QUBO problems." Quantum Reports 5.4 (2023): 625-658.

[5] Ajagekar, Akshay, and Fengqi You. "Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization." Advances in Applied Energy 14 (2024): 100179.

[6] Ajagekar, Akshay, and Fengqi You. "Quantum computing for energy systems optimization: Challenges and opportunities." Energy 179 (2019): 76-89.

[7] Harwood, Stuart, et al. "Formulating and solving routing problems on quantum computers." IEEE transactions on quantum engineering 2 (2021): 1-17.

[8] Nourbakhsh, Amirhossein, et al. "Quantum computing: Fundamentals, trends and perspectives for chemical and biochemical engineers." arXiv preprint arXiv:2201.02823 (2022).

[9] Stober, Spencer T., et al. "Considerations for evaluating thermodynamic properties with hybrid quantum-classical computing work flows." Physical Review A 105.1 (2022): 012425.

[10] Reiher, Markus, et al. "Elucidating reaction mechanisms on quantum computers." Proceedings of the national academy of sciences 114.29 (2017): 7555-7560.

[11] Rofougaran, Rod, et al. "Encoding proteins as quantum states with approximate quantum state preparation by iterated sparse state preparation." Quantum Science and Technology (2025).

[12] Dhoriyani, Jeet, et al. "Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution." PNAS nexus 4.1 (2025): 572.

[13] Maciejewski, Filip B., et al. "A Multilevel Approach For Solving Large-Scale QUBO Problems With Noisy Hybrid Quantum Approximate Optimization." arXiv preprint arXiv:2408.07793 (2024).

[14] Volpe, Deborah, Giacomo Orlandi, and Giovanna Turvani. "Improving the Solving of Optimization Problems: A Comprehensive Review of Quantum Approaches." Quantum Reports 7.1 (2025): 3.

[15] Koch, Daniel, et al. "Gaussian amplitude amplification for quantum pathfinding." Entropy 24.7 (2022): 963.

[16] Bernal, David E., et al. "Perspectives of quantum computing for chemical engineering." AIChE Journal 68.6 (2022): e17651.