Peptide Binding Classification on Quantum Computers

TL;DR

This study demonstrates the potential of near-term quantum computers for classifying peptide sequences relevant to therapeutic protein design, showing competitive results and effective noise mitigation on actual quantum hardware.

Contribution

It is the first to apply near-term quantum models to peptide classification, achieving competitive performance and validating noise mitigation techniques on real quantum processors.

Findings

Quantum models perform competitively with classical baselines.

Error mitigation improves quantum model accuracy.

Quantum models identify meaningful biological features.

Abstract

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 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.