Quantum computing has been marketed as the next great computational revolution. A technology capable of reshaping materials science, leading to drug discovery, disrupting cryptography, helping in discovering new batteries, climate mitigation strategies, optimizing global energy systems [1-6], or supposedly cracking optimization problems that have resisted decades of classical computing, if you believe the brochures. Quantum computing is marketed as the next computing panacea, a trope we’ve experienced so many times that many of us have grown numb to it.
So let’s strip away all this noise and ask a question that actually matters for practitioners working on scalable power system optimization and long-term decarbonization planning: Can quantum computers actually help us solve enormous, multi-decadal, uncertainty-driven planning problems that yield optimal, here-and-now decisions for the uncertain future? To answer this question, we first need to understand the appeal of quantum computing, which rests on its distinctly non-classical foundations. Concepts like superposition, which, in principle, allow the exploration of enormous search spaces, and entanglement, which encodes correlations in ways classical bits cannot, give the field an almost paradoxical allure. It is the idea of using the fabric of reality to simulate reality itself. It’s a seductive narrative, especially when framed as a pathway to accelerating the solution of massive combinatorial problems. But for now, much of this remains theoretical rather than practical.
In practice, the hardware tells a far less glamorous story. We now have a few types of quantum computers, with superconducting circuits dominating the hardware landscape. Photonic and trapped-ion systems are advancing, as are other emerging and experimental systems, but none are yet capable of supporting the logical-qubit counts required for real-world planning models. In fact, we are still in what’s called the NISQ era (Noisy Intermediate-Scale Quantum) [7]. Qubits, which are the quantum equivalent of classical bits, have limited coherence times (i.e., their quantum state decoheres quickly), they have error-prone gates, and systems can hold only tens to hundreds of qubits at a time. Thus, one near-term pathway many major players in the field are adopting is to develop hybrid quantum-classical computing platforms that embed a quantum processor within a classical loop. Those actually show significant speedups and accuracy improvements [8], which are quite promising for the field. Though the hybrid trajectory risks ending in a technical cul-de-sac: an impressive hybrid performance, yes, but still far from the quantum paradigm these methods are supposed to foreshadow.
More than that, the claims that are made every week are dramatic and suspicious. We have Quantum Motion claiming mass-producible million-qubit superconducting chips [9]. PsiQuantum is building a photonic-qubit factory [10]. Fujitsu is launching a 10,000-qubit development program [11]. And overall, an intensifying hype of government investments across the US, China, Europe, and Japan [12] in a race towards technological domination. These loud headlines have many researchers arguing that the sector is riding a speculative bubble, with hardware still far from delivering the promised computational performance. Even if we had perfectly coherent, fully quantum hardware tomorrow, we would still face another massive obstacle: Quantum Algorithms. We don’t have practical quantum algorithms. The global investment and research effort poured into the hardware has not been matched by algorithmic innovation. Developing even the simplest quantum algorithm is non-trivial, and this might be the most significant hurdle that we would face in converting any real power system optimization model into a practical quantum-amenable representation. Fortunately, the community moved quickly to experiment with these ideas, giving us a clear view of how far quantum modeling of standard problems can realistically go.
A comprehensive review into Quantum Computation in Power Systems [13] that explored demonstrations of quantum modeling of problems such as Power Flow, Unit Commitment, Contingency Analysis, State Estimation, and Transient Simulation, represents an impressively early adaptation of quantum algorithms to power-system contexts, far more advanced than one might expect at this stage. However, the advantages gained were not demonstrated, and the overall verdict on the usefulness of the early-stage models was inconclusive. The same message is echoed in other studies that model power system problems [8], with encoding overheads, noise, and general NISQ limitations that prevent valid conclusions, even though the formulations and cases are small-scale and conceptual.
So, returning to the original question, can quantum computers meaningfully tackle the multi-decadal, uncertainty-driven planning problems that actually matter to us? As things stand, the answer is not yet, and possibly not for quite a while. Does all of this mean that quantum computing is doomed? Of course not. But neither is it destined for the spectacular success implied by the marketing narrative. Quantum computing remains fundamentally experimental; both the hardware and the algorithms are still far from the maturity required to deliver meaningful advantages in real engineering applications. Much of the hype simply runs ahead of what is technically feasible today.
Yet the hype itself has value. Without it, the field would never attract the capital, talent, or institutional momentum needed to push through its current limitations. For us, power-system planners and researchers, the rational stance is to continue developing and deploying advanced classical high-performance computing methods for the problems we aspire to solve now and in the foreseeable future, while maintaining a long-term curiosity about quantum-accelerated algorithms to ensure we are prepared when the technology finally catches up to the narrative.
References
[1] Pasqal, “Innovate in manufacturing & material sciences with quantum computing,” Pasqal, 2025. [Online]. Available: https://www.pasqal.com/innovate-in-manufacturing-material-sciences-with-quantum-computing/. Accessed: Dec. 4, 2025.
[2] S. K. Kandula, N. Katam, P. R. Kangari, A. Hijmal, R. Gurrala, and M. Mahmoud, “Quantum computing applications in computational intelligence,” in Proc. Int. Conf. Computational Science and Computational Intelligence (CSCI), 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10590603. Accessed: Dec. 4, 2025.
[3] RocketMeUp Cybersecurity, “Quantum computing’s impact on cryptography: The future of encryption,” Medium, 2024. [Online]. Available: https://medium.com/@RocketMeUpCybersecurity/quantum-computings-impact-on-cryptography-the-future-of-encryption-1f8804205d86. Accessed: Dec. 4, 2025.
[4] IonQ, “Improving battery chemistry with quantum computing,” IonQ, 2025. [Online]. Available: https://ionq.com/resources/improving-battery-chemistry-with-quantum-computing. Accessed: Dec. 4, 2025.
[5] D. Root, “Quantum technologies in the context of climate change: Emphasizing sustainability in a responsible innovation approach to quantum innovation,” NanoEthics, vol. 19, 2025, doi: 10.1007/s11569-025-00468-x.
[6] A. Ajagekar and F. You, “Quantum computing for energy systems optimization: Challenges and opportunities,” Energy, vol. 179, pp. 76–89, 2019, doi: 10.1016/j.energy.2019.04.186.
[7] J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum, vol. 2, p. 79, 2018, doi: 10.22331/q-2018-08-06-79.
[8] Y. Chen and T. Vu, A Review of Quantum Computing Technologies in Power System Optimization, Pacific Northwest National Laboratory, Richland, WA, USA, 2025. [Online]. Available: https://www.pnnl.gov/publications/review-quantum-computing-technologies-power-system-optimization. Accessed: Dec. 4, 2025.
[9] M. Swayne, “Quantum Motion delivers the industry’s first full-stack silicon CMOS quantum computer,” The Quantum Insider, Sep. 15, 2025. [Online]. Available: https://thequantuminsider.com/2025/09/15/quantum-motion-delivers-the-industrys-first-full-stack-silicon-cmos-quantum-computer/. Accessed: Dec. 4, 2025.
[10] M. Swayne, “PsiQuantum raises $1 billion to build million-qubit scale fault-tolerant quantum computers,” The Quantum Insider, Sep. 10, 2025. [Online]. Available: https://thequantuminsider.com/2025/09/10/psiquantum-raises-1-billion-to-build-million-qubit-scale-fault-tolerant-quantum-computers/. Accessed: Dec. 4, 2025.
[11] Fujitsu Limited, “Fujitsu starts official development of plus-10,000 qubit superconducting quantum computer targeting completion in 2030,” press release, Aug. 1, 2025. [Online]. Available: https://global.fujitsu/en-global/pr/news/2025/08/01-01-en/. Accessed: Dec. 4, 2025.
[12] Spinquanta, “Quantum computing funding: Explosive growth and strategic investment in 2025,” Spinquanta, 2025. [Online]. Available: https://www.spinquanta.com/news-detail/quantum-computing-funding-explosive-growth-strategic-investment-2025. Accessed: Dec. 4, 2025.
[13] S. Golestan, P. Zare, R. Zamora, and J. M. Guerrero, “Quantum computation in power systems: An overview of applications and challenges,” Electric Power Systems Research, vol. 224, art. no. 109617, 2023, doi: 10.1016/j.epsr.2023.109617.
