A Year in Review

It has been a busy year for the INFORMS Quantum Computing and Operations Research (QCOR) Ad Hoc Committee. With the growing interest in quantum computing within the INFORMS community, we would have expected nothing less. In the spring 2025 issue of OR/MS Today, we published an article discussing some of the ways in which quantum computing and operations research intersect. Here we highlight some of the many activities related to quantum computing that happened within INFORMS, or adjacent to INFORMS, during the past year.

The major INFORMS conferences (the International Conference and Annual Meeting) and some of the society conferences (the Computing Society Conference in 2025, the Optimization Society Conferences in 2024 and 2026) featured a noticeable presence of quantum computing research, including keynote talks, tutorials, panels, and parallel sessions. Among these were Stefan Woerner’s keynote presentation at the INFORMS International Conference, Sridhar Tayur’s keynote presentation and tutorial, and a QCOR Panel discussion at the INFORMS Annual Meeting. 

Outside INFORMS, our community was prominently featured at the ICCOPT conference and in two IEEE workshops: one on quantum optimization and one on quantum algorithms for finance, both held during IEEE Quantum Week. A recent episode of the INFORMS podcast series “Resoundingly Human” with Tamás Terlaky highlighted the importance of engaging the INFORMS community with QCOR activities.

In our spring 2025 OR/MS Today article, we gave a short overview of the overlap between operations research and quantum computing, focusing on four main topics: optimization, simulation, machine learning, and applications. There have been several advances in these fields, and, as a result, there are new and exciting opportunities for operations researchers to engage with quantum computing. We highlight these here.¹

Optimization

Quantum computing approaches for solving classically intractable optimization problems are still a bustling field of research, as mentioned in a 2025 seminal survey by Amira Abbas et. al. in Nature Review Physics.² This past year has seen advances in both near-term quantum algorithms, which yield approximate solutions and can be implemented on current quantum hardware, and fault-tolerant quantum algorithms, which yield exact solutions. 

For example, a 2025 study found that transfer learning can mitigate one of the main hurdles for a prominent near-term quantum algorithm, the Quantum Approximate Optimization Algorithm (QAOA), by helping to find parameters in the algorithm’s classical subroutine.³ An article by Zichang He, et. al. in Communications Physics bridges the gap between near-term and fault-tolerant quantum algorithms by applying error detection codes to QAOA.⁴ 

Distinguished from the Hamiltonian-based methods that exploit local landscape structures, recently introduced decoded quantum interferometry (DQI), an algorithm that uses the quantum Fourier transform to reduce optimization tasks to decoding problems.⁵ By leveraging the sparsity often found in the Fourier spectrum of combinatorial problems, it was shown theoretically that the algorithm outperforms the best-known classical heuristics on the Optimal Polynomial Intersection problem. However, as noted by Ojas Parekh in Quantum Physics, there is no quantum advantage for the most “natural” optimization problem representative of this class, namely, MaxCut.⁶

On the continuous optimization side, novel quantum and quantum-inspired interior point methods were developed for conic optimization problems, where innovative adaptation of iterative refinement helps mitigate the impact of quantum errors and noises and remove dependence on growing condition numbers. The results provide complexity advantage in some settings.⁷ Despite major advances, achieving practical quantum advantage for optimization problems remains an elusive task. 

Not only can quantum algorithms potentially solve intractable classical optimization problems, but classical optimization approaches can also be used to help improve quantum computing. Near-term quantum devices are limited by small numbers of logical qubits, high error rates in quantum gates, and short coherence times. Last year saw the advent of new classical optimization methods, such as mixed-integer optimization and combinatorics heuristics, being used to reduce gate count, optimize qubit mappings, efficiently prepare initial quantum states, decompose large quantum circuits into smaller disjoint circuits, and more.^8,9 However, finding provably optimal or near-optimal methods for allocating quantum resources is still critical for achieving practical quantum advantage.

Simulation

Simulation of quantum systems, one of the first proposed uses for quantum computers, remains a thriving area of research. A major breakthrough in 2025 indicates that quantum annealing can be used to simulate transverse-field Ising models, a type of quantum system, with high accuracy and with better scaling than classical approaches.¹⁰

It is worth noting that specialized classical algorithms exist for simulating quantum systems under specific conditions. Popular classical techniques for simulating quantum systems include tensor networks and Lie algebras. Furthermore, special circuits called stabilizer circuits can be simulated efficiently because they contain very specific quantum operations; however, they are not universal for computation, limiting their utility. While these methods can be used to explore and understand small quantum systems without requiring access to quantum hardware or quantum systems with strict properties, new techniques are needed to simulate larger quantum systems of interest to the quantum computing research community. Xiaosi Xu, et. al. provides a recent review of classical techniques for simulating quantum systems in Science Bullentin.¹¹

Machine learning

Analogous to classical machine learning, quantum machine learning (QML) leverages quantum-mechanical phenomena to improve performance on data-driven tasks. However, QML models, especially ones that rely on parameterized quantum circuits, can be challenging to use in practice due to phenomena such as barren plateaus and high estimator variance. An exciting result from 2025 shows that machine learning approaches can deliver an exponential speedup in learning quantum observables from classical data.¹²

Despite recent successes, however, there remain opportunities for OR/MS researchers to deliver classical data-driven solutions to challenging quantum computing problems. For example, classical learning models have been used to characterize quantum hardware parameters, such as noise and material irregularities. When using classical approaches to model quantum phenomena, there is always a risk that the models do not accurately represent quantum-mechanical phenomena or require intractably large amounts of data. 

Furthermore, while classical learning models for quantum circuit design and unitary synthesis can yield more compact, efficient quantum circuits, they are challenging to implement in practice due to scalability and training challenges. Thus, new, better data-driven solutions that more accurately and efficiently model quantum hardware are an area where OR/MS disciplines can make an impact. Yuri Alexeev provides a detailed overview of opportunities in the intersection of machine learning and quantum computing in Nature Communications.¹³

Applications

While finance, chemical engineering, and transportation remained widely studied applications of quantum computing during the past year, research at the intersection of quantum computing and other key operations research application areas has gained traction. Energy is one such domain. 

In the report, “A Review of Quantum Computing Technologies in Power System Optimization,” Yousu Chen and Long Vu provide a recent review highlighting the potential of quantum technologies to modernize power system optimization.¹⁴ For example, in the paper, “Leveraging Quantum Computing for Accelerated Classical Algorithms in Power Systems Optimization,” Rosemary Barrass,  Harsha Nagarajan, and Carleton Coffrin propose hybrid quantum-classical approaches to optimize the scheduling of power generators across the grid to meet real-time demand more efficiently.¹⁵ 

Healthcare and supply chain domains also face optimization challenges, including resource allocation and scheduling. Fault-tolerant quantum algorithms, such as the Harrow-Hassidim-Lloyd algorithm, can provide exponential speedups for estimating observables of linear systems, which can be used to address these types of problems. But quantum hardware is not sufficiently mature yet for fault-tolerant implementation. 

Near-term quantum algorithms and hybrid approaches, which can be implemented on current quantum hardware, can solve small-scale optimization problems. However, classical solvers still retain an advantage for solving large-scale, industry-relevant problems. There is an opportunity for OR/MS experts to apply classical optimization techniques to develop and improve hybrid quantum-classical workflows. A 2025 survey by James Chow on quantum computing techniques in healthcare published in Algorithms demonstrates this.¹⁶

As noted above, there are numerous interesting and challenging operations research problems in quantum computing. OR/MS methods have the potential to help address these challenges and enable practical quantum advantage. Check out the new Quantum Computing area of the INFORMS Journal on Computing for new and exciting advancements in the intersection of quantum computing and operations research.

Acknowledgments
The INFORMS Quantum Computing and Operations Research Ad Hoc Committee was formed in October 2024. This article is written by its members.

References

1. Tucker, Emily L., Mohammadisiahroudi, M., 2025, “A Gateway to Quantum Computing for Industrial Engineering,” arXiv preprint arXiv:2510.20620.
2. Abbas, A., Ambainis, A., Augustino, B., et.al., 2024, “Challenges and Opportunities in Quantum Optimization,” Nature Reviews Physics, Vol. 6, pp. 718-735.
3. Montanez-Barrera, J. A., Willsch, D., Michielsen, K., 2025, “Transfer Learning of Optimal QAOA Parameters in Combinatorial Optimization,” Quantum Information Processing, 24. No. 5.
4. He, Z., et. al., 2025, “Performance of Quantum Approximate Optimization with Quantum Error Detection,” Communications Physics, 8, No. 217.
5. Jordan, S., et al., 2025, “Optimization by Decoded Quantum Interferometry,” Nature, 636, pp. 831-836.
6. Parekh, O., 2025, “No Quantum Advantage in Decoded Quantum Interferometry for MaxCut,” arXiv: 2509.19966.
7. Mohammadisiahroudi, M., et al., 2025, “Quantum Computing Inspired Iterative Refinement for Semidefinite Optimization,” Mathematical Programming.
8. Ponce, Moises, et al., 2025, “Graph Decomposition Techniques for Solving Combinatorial Optimization Problems with Variational Quantum Algorithms,” Quantum Information Processing, 24, No. 2.
9. Quinton, F. A., et al., 2025, “Quantum Annealing Applications, Challenges and Limitations for Optimisation Problems Compared to Classical Solvers," Scientific Reports, 15, No. 12733.
10. King, A. D., et al., 2025, “Beyond-Classical Computation in Quantum Simulation," Science, 388, Issue 6743, pp. 99-204.
11. Xu, X., et al., 2025, “A Herculean Task: Classical Simulation of Quantum Computers.” Science Bulletin, 70, Issue 23, pp. 4104-4112.
12. Molteni, R., Gyurik, C., Dunjko, V., 2026, “Exponential Quantum Advantages in Learning Quantum Observables from Classical Data.” npj Quantum Information, 12, No. 19.
13. Alexeev, Y., et al., 2025, “Artificial Intelligence for Quantum Computing,” Nature Communications, 16, No. 12733.
14. Chen, Y., Vu, L., 2025, “A Review of Quantum Computing Technologies in Power System Optimization.,” U.S. Department of Energy Office of Scientific and Technical Information, https://doi.org/10.2172/2571592.
15. Barrass, R., Nagarajan, H., Coffrin, C., 2025, “Leveraging Quantum Computing for Accelerated Classical Algorithms in Power Systems Optimization,” International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-95973-8_4.
16. Chow, J., 2025, “Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review,” Algorithms, 18, No. 3.