Designing lattice proteins with variational quantum algorithms

TL;DR

This paper explores the use of variational quantum algorithms for protein sequence optimization in lattice protein design, highlighting their potential and current limitations on noisy quantum hardware.

Contribution

It evaluates different variational quantum algorithms and circuit designs for protein sequence optimization, comparing their performance in simulations and on real quantum devices.

Findings

Problem-agnostic circuits perform better under noise.

Quantum algorithms show promise but face challenges on real hardware.

Simulated noise models do not fully capture real hardware noise effects.

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 algorithms yield acceptable results in noiseless simulations, their performance drops under noise. With the problem-agnostic circuits, which are more compatible with hardware constraints, an improved performance is observed in both noisy and noiseless simulations. However, the results deteriorate when running on a real quantum device. We attribute this discrepancy to features not captured by the simulated noise model, such as the temporal aspect of the hardware noise.