Finding Good Use Cases For Quantum Computing

A four-step framework, Identify, Transform, Benchmark, Show Advantage (ITBQ), assesses computing applications, revealing opportunities and barriers to practical advantage in areas like nuclear magnetic resonance (NMR) spectroscopy and multireference calculations. The research emphasises structured criteria to direct research and investment, accelerating progress in the field.

The pursuit of practical applications for quantum computers remains a central challenge in the field, moving beyond theoretical potential to demonstrable benefit. Identifying genuinely advantageous use cases requires a systematic approach, encompassing not only problem selection but also rigorous comparison with existing classical methods and careful translation onto available quantum hardware. Researchers at HQS Quantum Simulations GmbH, including Michael Marthaler, Peter Pinski, Pascal Stadler, Vladimir Rybkin, and Marina Walt, address this need with a four-stage framework, detailed in their work entitled ‘What is a good use case for quantum computers?’. Their analysis, focusing on applications within nuclear magnetic resonance (NMR), multireference calculations, and radical chemistry, provides a structured evaluation of opportunities and obstacles, advocating for transparent criteria to direct future research and investment in the field.

Recent progress in computational chemistry necessitates robust validation and precise molecular modelling, particularly for systems exhibiting significant multireference character, where conventional single-reference electronic structure methods frequently prove inadequate. These methods, based on the assumption that a single electronic configuration dominates the wave function, struggle with molecules where multiple electronic configurations contribute significantly to the overall electronic structure. Consequently, researchers increasingly employ computationally intensive techniques such as Random Phase Approximation (RPA), Multireference Coupled Cluster (MRCC), and Multiconfiguration Self-Consistent Field (MCSCF) to accurately account for electron correlation, the complex interplay between electrons, and describe the behaviour of challenging molecular species. Electron correlation is a fundamental aspect of molecular behaviour, and accurately modelling it is crucial for obtaining reliable predictions of molecular properties.

The field currently requires structured evaluation frameworks, prompting the development of the ‘Identify, Transform, Benchmark, Show Advantage’ (ITBQ) protocol. This systematic approach assesses the viability of computational advancements by prioritising rigorous benchmarking against established classical solutions. The ITBQ protocol aims to ensure that new methods demonstrably outperform existing techniques before being widely adopted. Researchers advocate for transparent and objective assessment of the true benefits offered by new methods and technologies, ensuring that advancements are validated against existing standards and that claims of improvement are substantiated.

Researchers actively pursue the accurate computational description of diradicals and open-shell systems, recognising the limitations of traditional quantum chemical methods in these cases. Diradicals, molecules containing two unpaired electrons, and open-shell systems, those with an incomplete electron shell, present a particular challenge due to their complex electronic structure and the need to accurately describe the interactions between unpaired electrons. Achieving chemically accurate results requires employing advanced computational methods, including those incorporating many-body effects, which account for the interactions between multiple electrons simultaneously.

The computational determination of singlet-triplet gaps also receives considerable attention, as this property governs excited-state behaviour and photochemical processes. Accurate prediction of singlet-triplet gaps is essential for understanding and designing molecules with specific optical and electronic properties, with applications in areas such as organic light-emitting diodes and photocatalysis. The Libint library, a tool for efficient calculation of molecular integrals, and the NumPy array programming library, are identified as essential components of the computational toolkit, streamlining the process of complex calculations. Molecular integrals, mathematical expressions arising from the solution of the Schrödinger equation, are computationally demanding to calculate, and efficient libraries like Libint are crucial for accelerating these calculations.

Investigation extends beyond purely theoretical and computational advancements, exploring the potential of quantum computing to address intractable problems in quantum chemistry. Researchers simulate the spin-boson model, a fundamental model in quantum chemistry describing the interaction between a two-level system and a harmonic bath, as an example of how quantum hardware might eventually surpass the capabilities of classical computers. This direction acknowledges the limitations of even the most advanced classical algorithms and seeks to leverage the unique capabilities of quantum mechanics, such as superposition and entanglement, to achieve computational breakthroughs.

This consistent referencing of advanced methods underscores their importance in achieving chemically accurate predictions for diradicals, conjugated systems like acenes, and other challenging molecular species. Acenes, fused aromatic rings, are particularly challenging due to their extended pi-system and the strong correlation effects that arise from the delocalised electrons. This emphasis on practical demonstration ensures that quantum computing research remains grounded in real-world applications and that theoretical advancements translate into tangible improvements in molecular modelling.