What is a good use case for quantum computers?

TL;DR

The paper presents the ITBQ four-step framework (Identify, Transform, Benchmark, Show Quantum Advantage) to systematically evaluate quantum-use cases and highlights the gaps in current literature regarding problem translation and classical benchmarking. Through three case studies—NMR, multireference chemistry, and diradicals—it demonstrates how careful Transform-to-Quantum procedures, coupled with rigorous classical baselines and targeted quantum algorithms, shape realistic assessments of quantum advantage. It argues that connecting abstract quantum models to real-world problems is essential and that robust software tools (e.g., ASF) and problem-aware benchmarking are critical to progress. The work underscores that while quantum advantage is plausible in carefully constructed maps (e.g., active spaces with RPA corrections or spin-boson mappings), mature classical methods and workflow integration presently limit universal, industry-wide gains, making transparent evaluation and targeted investment crucial for practical impact.

Abstract

Identify, Transform, Benchmark, Show Quantum Advantage (ITBQ): Evaluating use cases for quantum computers. We introduce a four-step framework for assessing quantum computing applications -- from identifying relevant industry problems to demonstrating quantum advantage -- addressing steps often overlooked in the literature, such as rigorous benchmarking against classical solutions and the challenge of translating real-world tasks onto quantum hardware. Applying this framework to cases like NMR, multireference chemistry, and radicals reveals both significant opportunities and key barriers on the path to practical advantage. Our results highlight the need for transparent, structured criteria to focus research, guide investment, and accelerate meaningful quantum progress.

Figures (10)

• Figure 1: Steps to assess quantum computing use cases: Identify, Transform, Benchmark, Show Quantum Advantage (ITBQ).
• Figure 2: Evaluation of Industrial Optimization.
• Figure 3: Evaluation of Abstract Spin Models.
• Figure 4: Principles of an NMR spectrometer: the sample is placed in a strong magnetic field, causing a net magnetization of the nuclear spins by the static field. Pulses from radio frequency coils, in resonance with the Larmor frequency of the nuclei, cause an oscillating transverse magnetization. Measurement of the decaying response after completion of the pulse sequence leads to the NMR spectrum after processing.
• Figure 5: Two NMR applications -- structure determination and mixture analysis -- are combined in a single workflow. Experimental spectra for Psoralen and Angelicin were provided by PhytoLab GmbH & Co. KG, while no experimental data were available for Bakuchicin; its spectrum is simulated only. Although all three molecules share the same molecular formula ($\mathrm{C}_{11}\mathrm{H}_6\mathrm{O}_3$), their spectra are distinct, highlighting the necessity for precise NMR analysis.