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  • Open Access

Designing lattice proteins with variational quantum algorithms

Hanna Linn1,*, Lucas Knuthson2, Anders Irbäck2, Sandipan Mohanty3, Laura García-Álvarez1, and Göran Johansson1

  • *Contact author: hannlinn@chalmers.se
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Phys. Rev. Applied 26, 034065 – Published 28 September, 2026

DOI: https://doi.org/10.1103/kpf7-fx7t

Abstract

Quantum heuristics have shown promise in solving various optimization problems, including lattice protein folding. Equally relevant is the inverse problem, protein design, where one seeks sequences that fold to a given target structure. The latter problem is often split into two steps: (i) searching for sequences that minimize the energy in the target structure, and (ii) testing whether the generated sequences fold to the desired structure. Here, we investigate the utility of variational quantum algorithms for the first of these two steps on today’s noisy intermediate-scale quantum devices. We focus on the sequence optimization task, which is less resource-demanding than folding computations. We test the quantum approximate optimization algorithm and variants of it, with problem-informed quantum circuits, as well as the hardware-efficient ansatz, with problem-agnostic quantum circuits. While the former approach, with a careful choice of mixer, yields good results in noiseless simulations (success probability ≥0.95 in all instances), its performance drops drastically under noise. With the problem-agnostic circuits, which are more compatible with hardware constraints, improved performance is observed in noisy simulations, compared to their problem-informed counterparts. When running the problem-agnostic circuits on a real quantum device, with parameters taken from simulations, we obtain a significant success probability (≥0.2) in most instances.

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Phys. Rev. Applied 26, 034065– Published 28 September, 2026
  • Received 6 August 2025
  • Revised 16 April 2026
  • Accepted 28 May 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by Bibsam.

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