Abstract Near-term quantum devices remain constrained by limited qubit counts, noise, and shallow circuit depth, restricting their direct application to peptide folding. This work addresses this limitation through a hybrid quantum–classical framework that separates global contact-space exploration from local configuration-space refinement. The pipeline was evaluated on the p53 transactivation peptide. The peptide was encoded using discrete torsional states and Miyazawa–Jernigan contact potentials. Global conformational search was performed using simulated annealing, followed by fragment-level CVaR-based variational quantum eigensolver refinement. Overlapping fragments were stitched and reconstructed into three-dimensional backbone coordinates for structural comparison. Three independent simulated annealing restarts produced negative-energy conformers. The workflow identified a best global conformer at E = −3.70 MJ using simulated annealing. Fragment-level CVaR-VQE refinement generated 30 candidate conformers across three overlapping fragments, the best assembled conformer reaching E = −2.58 MJ after DP stitching and post-stitch refinement. In this trial, the classical SA solution outperformed the VQE-assembled structure and was selected as the final output. The final 65-atom model was successfully reconstructed, but full-backbone alignment against 1YCR yielded an RMSD of 4.65 Å, improving to 2.90 Å after trimmed alignment. Our work demonstrates a practical quantum-enabled peptide folding workflow for short, flexible peptides, but the present structural accuracy is insufficient for claims of native fold recovery. Its main value is as an auditable framework for testing NISQ-compatible folding strategies.
Abstract Near-term quantum devices remain constrained by limited qubit counts, noise, and shallow circuit depth, restricting their direct application to peptide folding. This work addresses this limitation through a hybrid quantum–classical framework that separates global contact-space exploration from local configuration-space refinement. The pipeline was evaluated on the p53 transactivation peptide. The peptide was encoded using discrete torsional states and Miyazawa–Jernigan contact potentials. Global conformational search was performed using simulated annealing, followed by fragment-level CVaR-based variational quantum eigensolver refinement. Overlapping fragments were stitched and reconstructed into three-dimensional backbone coordinates for structural comparison. Three independent simulated annealing restarts produced negative-energy conformers. The workflow identified a best global conformer at E = −3.70 MJ using simulated annealing. Fragment-level CVaR-VQE refinement generated 30 candidate conformers across three overlapping fragments, the best assembled conformer reaching E = −2.58 MJ after DP stitching and post-stitch refinement. In this trial, the classical SA solution outperformed the VQE-assembled structure and was selected as the final output. The final 65-atom model was successfully reconstructed, but full-backbone alignment against 1YCR yielded an RMSD of 4.65 Å, improving to 2.90 Å after trimmed alignment. Our work demonstrates a practical quantum-enabled peptide folding workflow for short, flexible peptides, but the present structural accuracy is insufficient for claims of native fold recovery. Its main value is as an auditable framework for testing NISQ-compatible folding strategies.
Protein structure prediction remains central to computational biology and drug discovery. While deep learning models such as AlphaFold have achieved remarkable accuracy for known fold families, they struggle with novel folds, intrinsically disordered regions, and conformational dynamics, and they do not explicitly optimize physics-based energy functions. We present a quantum-assisted approach to protein conformation optimization that uses parameterized quantum circuits within a variational framework to explore the energy landscape of small proteins and fragments from first-principles or coarse-grained physics. Our method encodes backbone torsion angles (ϕ, ψ) and selected side-chain degrees of freedom into qubit registers and employs a variational quantum eigensolver (VQE)-inspired optimization to minimize an approximate Hamiltonian incorporating bonded interactions, sterics, hydrogen bonding, and hydrophobic contacts. We introduce problem-inspired ansätze that respect chain connectivity and secondary-structure motifs (helices, beta-sheets) and propose hierarchical circuit growth strategies for scaling from peptides to larger domains. Through classical simulations of small peptides and secondary-structure motifs, we demonstrate that quantum variational optimization can identify low-energy conformations consistent with experimentally determined structures. We provide explicit resource estimates for NISQ and early fault-tolerant quantum computers and discuss hybrid classical-quantum strategies for refining quantum solutions with classical molecular dynamics and machinelearning priors. While full protein-folding quantum advantage remains distant, we identify concrete subproblems where nearterm quantum devices offer complementary physics-based insight beyond data-driven approaches, opening pathways toward practical quantum-classical protein structure and drug discovery pipelines.
Variational quantum algorithms provide a direct, physics-based approach to protein structure prediction, but their accuracy is limited by the coarse resolution of the energy landscapes generated on current noisy devices. We propose a hybrid framework that combines quantum computation with deep learning, formulating structure prediction as a problem of energy fusion. Candidate conformations are obtained through the Variational Quantum Eigensolver (VQE) executed on IBM’s 127 qubit superconducting processor, which defines a global yet low resolution quantum energy surface. To refine these basins, secondary structure probabilities and dihedral angle distributions predicted by the NSP3 neural network are incorporated as statistical potentials. These additional terms sharpen the valleys of the quantum landscape, resulting in a fused energy function that enhances effective resolution and better distinguishes native like structures. Evaluation on 375 conformations from 75 protein fragments shows that, on this short fragment-only benchmark (10–14 residues without surrounding chain context), the hybrid pipeline achieves a mean RMSD of 4.9 Å with statistical significance ( p < 0.001), outperforming both quantum-only re-rankings and fragment-level runs of AlphaFold3 and ColabFold. We caution that this conclusion is restricted to the fragment-scale regime accessible to current quantum hardware and does not extend to the full-chain regime in which AlphaFold3 and ColabFold are usually evaluated. The findings demonstrate that energy fusion offers a systematic method for combining data driven models with quantum algorithms, improving the practical applicability of near term quantum computing to molecular and structural biology.
Classical and current quantum approaches to protein structure prediction (QPSP) face limitations, notably massive qubit requirements restricting near-term models to simplistic on-lattice simulations. We propose a logarithmic-scale variational quantum eigensolver (VQE) that reduces qubit requirements for N torsional degrees of freedom to O(log2N), enabling off-lattice, all-atom simulations. Our architecture extracts molecular torsions from relative phases in statevector simulations. On quantum hardware, a decoder maps the empirical cumulative distribution function (CDF) from basis-state probabilities to bounded torsional variables. These feed a classical algorithm to build heavy-atom coordinates. We use an EfficientSU2 ansatz and multi-stage relaxation to mitigate barren plateaus. Structures are evaluated via a custom hybrid quantum-classical Hamiltonian, alongside Rosetta and OpenMM benchmarks.
Evaluation on chignolin and Trp-cage yielded native-like conformations. Chignolin reached a 0.623 Å Cα RMSD in retained snapshots and 1.199 Å in final models; Trp-cage achieved a 2.501 Å RMSD among snapshots (3.512 Å in final models). Execution on IBM processors (ibm_cleveland, ibm_miami) successfully recovered native-like structures with a best RMSD of 1.758 Å. The custom energy function performed best overall, though energy-ranking imbalances persisted across sampled landscapes for all functions.
This introduces the first all-atom, continuous-space quantum algorithm for QPSP. By converting physical qubit constraints into circuit depth constraints, it proves high-resolution prediction is feasible with exponentially fewer qubits. Despite current limits like computational overhead and energy function sensitivity, it establishes a scalable foundation for hybrid quantum biophysics.
Protein folding is one of the age-old biological problems that refers to the mechanism of understanding and predicting how a protein's linear sequence of amino acids folds into its specific three dimensional structure.This structure is critical, as a protein's functionality is inherently linked to its final folded form. Misfolding can lead to severe diseases such as Alzheimer's and cystic fibrosis, highlighting the biological and clinical importance of understanding protein folding mechanisms. This work presents a novel turn based encoding optimization algorithm for predicting the folded structures of peptides and small proteins. Our approach builds upon our previous research, where our objective function focused on hydrophobic collapse, a fundamental phenomenon underlying the protein folding process. In this work, we extend that framework by not only incorporating hydrophobic interactions but also including all non bonded interactions modeled using the Miyazawa Jernigan potential. We constructed a Hamiltonian from the defined objective function that encodes the folding process on a three dimensional face centered cubic lattice, offering superior packing efficiency and a realistic representation of protein conformations. This Hamiltonian is then solved using classical and quantum solvers to explore the vast conformational space of proteins. To identify the lowest-energy folded configurations, we utilize the Variational Quantum Eigensolver implemented on IBM 133 qubit hardware. The predicted structures are validated against experimental data using root mean square deviation as a metric and compared against classical simulated annealing and molecular dynamics simulation results. Our findings highlight the promise of hybrid classical and quantum approaches in advancing protein folding predictions, particularly for sequences with low homology.
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.
ABSTRACT Near-term quantum algorithms such as the variational quantum eigensolver (VQE) have been widely explored for small-molecule electronic structure calculations, yet their relevance for biologically motivated peptide systems remains largely untested. Here, we apply a rigorously controlled, fragment-based VQE workflow to a tau-derived peptide fragment implicated in protein aggregation in Alzheimer’s Disease. Using an identical active space, basis set, and frozen-core treatment, we benchmark VQE electronic energies against classical restricted Hartree-Fock (RHF) calculations and molecular dynamics (MD) force-field energies across an ensemble of peptide conformations. While VQE and active-space RHF energies show systematic agreement within the defined electronic subspace, both exhibit weak correlation with MD-derived energetics, highlighting the fundamentally different physical contributions captured by electronic structure methods and classical force fields. These results demonstrate that NISQ-era quantum chemistry provides complementary, rather than redundant, information relative to classical MD and delineate the scope and limitations of applying VQE to biologically relevant peptide fragments. Our study establishes a disease-motivated benchmark framework for integrating quantum electronic structure calculations with classical simulation approaches in peptide biophysics.