Protein folding on a 64 qubit trapped-ion hardware via counterdiabatic quantum optimization

TL;DR

This paper demonstrates the use of counterdiabatic quantum optimization on a 64-qubit trapped-ion system to solve complex protein-folding problems, showing promising results in reaching low-energy configurations.

Contribution

First large-scale trapped-ion quantum hardware implementation of lattice protein-folding optimization using a novel non-variational bias-feedback quantum algorithm.

Findings

BF-DCQO shifts energy distributions toward lower energies.

Hybrid post-processing improves solution quality.

Achieved classical reference energy in multiple instances.

Abstract

We report the largest trapped-ion hardware demonstration of lattice protein-folding optimization to date, using bias-field digitized counterdiabatic quantum optimization (BF-DCQO) on a fully connected 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. Six peptide sequences with 14-16 amino-acid residues are encoded using a coarse-grained tetrahedral lattice model, yielding higher-order spin-glass Hamiltonians with long-range interactions involving up to five-body terms and mapped to 46-61 qubits. The resulting instances are demanding for near-term quantum hardware because low-energy configurations must satisfy backbone-geometry constraints while optimizing dense residue-contact interactions. BF-DCQO uses a non-variational bias-feedback mechanism, where low-energy samples from each round define longitudinal fields that guide subsequent quantum evolutions. Across the studied instances, BF-DCQO shifts raw sampled energy distributions toward lower energies than uniform random sampling, with the strongest improvements appearing in residue-contact variables. To preserve this signal, we introduce a consensus-based post-processing pipeline that combines quantum-learned contact information with feasible backbone geometries. The resulting hybrid workflow reaches the classical reference energy in multiple instances and improves over the corresponding random-seeded pipeline. These results show that BF-DCQO can generate structured samples for dense protein-folding Hamiltonians at previously unexplored trapped-ion scales.