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Peptide binding classification on quantum computers
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DOI：10.1007/s42484-024-00154-3.png)
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Abstract
En 中文
We conduct an extensive study on using near-term quantum computers for a task in the domain of computational biology. By constructing quantum models based on parameterised quantum circuits, we perform sequence classification on a task relevant to the design of therapeutic proteins and find competitive performance with classical baselines of a similar scale. To study the effect of noise, we run some of the best-performing quantum models with favourable resource requirements on emulators of state-of-the-art noisy quantum processors. We then apply error mitigation methods to improve the signal. We further execute these quantum models on the Quantinuum H1-1 trapped-ion quantum processor and observe very close agreement with noiseless exact simulation. Finally, we perform feature attribution methods and find that the quantum models indeed identify sensible relationships, at least as well as the classical baselines. This work constitutes the first proof-of-concept application of near-term quantum computing to a task critical to the design of therapeutic proteins, opening the route toward larger-scale applications in this and related fields, in line with the hardware development roadmaps of near-term quantum technologies.
Keywords:
Quantum computing
Quantum machine learning
Parameterised quantum circuits
Computational biology
Bioinformatics
Peptide binding