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A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding
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Gate-based universal quantum computers form a rapidly evolving field of quantum computing hardware technology. In previous work, we presented a quantum algorithm for lattice protein folding on a cubic lattice, tailored for quantum annealers. In this paper, we introduce a novel approach for solving the lattice protein folding problem on universal gate-based quantum computing architectures. Lattice protein models are coarse-grained representations of proteins that have been used extensively over the past thirty years to examine the principles of protein folding and design.These models can be used to explore a vast number of possible protein conformations and to infer structural properties of more complex atomistic protein structures. We formulate the problem as a quantum alternating operator ansatz, a member of the wider class of variational quantum/classical hybrid algorithms. To increase the probability of sampling the ground state, we propose splitting the optimization problem into hard and soft constraints. This enables us to use a previously under-utilised component of the variational algorithm to constrain the search to the subspace of solutions that satisfy the hard constraints.
Forward citations
Cited by 5 Pith papers
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Penalty-free quantum optimization applied to lattice protein folding
A QAOA variant without quadratic penalties, using independent sets in a conflict graph, is applied to lattice protein folding and validated on proteins up to length 14 via simulation and heuristic search.
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Learning to learn with quantum neural networks via classical neural networks
Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
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Graph-VQE: A CUDA-Q Multi-QPU Simulation Framework for Hamiltonian-Aware Protein-Folding VQE
Hamiltonian-aware Louvain partitioning plus block-restricted full-objective VQE and CUDA-Q multi-QPU batching yields lower lattice-protein energies than fixed-ansatz baselines while remaining competitive on reconstruc...
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QFoldAgent: An Autonomous Quantum Optimization Multi-Agent System for Protein Structure Prediction
An LLM-driven closed-loop controller that refines VQE Hamiltonian penalties improves structural validity and optimization behavior for 5-residue lattice protein folding.
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Designing lattice proteins with variational quantum algorithms
For a simplified 2D lattice protein design problem, shallow hardware-efficient circuits outperform problem-aware QAOA under hardware noise, but only for chains up to 12 amino acids.
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