Scientific applications of quantum computing: challenges and opportunities

• URL: http://arxiv.org/abs/2608.16568v1
• Date: Mon, 17 Aug 2026 13:35:14 GMT
• Title: Scientific applications of quantum computing: challenges and opportunities
• Abstract summary: We discuss where quantum computing may provide a genuine scientific advantage in chemistry, materials science, and biochemistry.<n>The central challenge is not qubit scaling alone, but demonstrably meaningful gains in predictive reliability.<n>Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects.
• Score: 1.4716197659238566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic correlation, excited states, and complex energy landscapes. Quantum computing offers a fundamentally different computational paradigm in which quantum states are encoded and manipulated directly rather than approximated on classical hardware. Here we discuss where this approach may provide a genuine scientific advantage in chemistry, materials science, and biochemistry. Promising directions include the high-accuracy treatment of correlated active spaces, improved excited-state simulations, and accelerated exploration of combinatorial structure spaces. The central challenge is therefore not qubit scaling alone, but demonstrably chemically meaningful gains in predictive reliability. We argue that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods. Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects, and claims of usefulness must be tied to the specific regime being discussed. Quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.

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