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.
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.
Accurate prediction of protein active-site structures remains a central challenge in structural biology, especially for short and flexible peptide fragments where conventional and simulation-based methods often fail. Here, we present a quantum computing framework designed for utility-level quantum processors to address this problem. Starting from an amino acid sequence, we cast structure prediction as a ground-state energy minimization task using the Variational Quantum Eigensolver (VQE). Amino acid connectivity is represented on a tetrahedral lattice, and steric, geometric, and chirality constraints are encoded into a problem-specific Hamiltonian expressed as sparse Pauli operators. A two-stage architecture separates energy estimation from measurement decoding, enabling noise mitigation under realistic device conditions. We evaluate the method on 23 real protein fragments from the PDBbind dataset and 7 fragments from therapeutically relevant proteins, executing all experiments on the IBM-Cleveland Clinic quantum processor. Structural predictions are benchmarked against AlphaFold3 (AF3) and classical simulation-based approaches using identical postprocessing and docking procedures. Our quantum framework outperforms both AF3 and classical baselines in Root-Mean-Square Deviation (RMSD) and docking efficacy, demonstrating a practical end-to-end pipeline for biologically relevant structure prediction on real quantum hardware and highlighting its engineering feasibility for near-term quantum devices.
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.
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.
Hybrid artificial intelligence–quantum (AI–Q) models arise at the intersection of two rapidly advancing computational paradigms, promising unprecedented capabilities for real-time molecular structure prediction. In classical computational chemistry, the accurate determination of ground-state energies and optimized geometries relies on ab initio methods such as density functional theory (DFT) and coupled-cluster approaches, which, while highly precise, exhibit steep polynomial or exponential scaling with system size. Conversely, deep neural networks (DNNs) have demonstrated remarkable success in learning potential energy surfaces (PES) from large datasets, achieving rapid inference but sometimes lacking the ultimate chemical accuracy required for predictive applications in drug discovery or materials design. On the other hand, variational quantum eigensolvers (VQEs) leverage the intrinsic parallelism of quantum hardware to approximate electronic structure problems, yet they remain constrained by qubit coherence times, gate fidelities, and circuit depth limitations inherent to noisy intermediate-scale quantum (NISQ) devices.
Background: Understanding electronic interactions in protein active sites is fundamental to drug discovery and enzyme engineering, but remains computationally challenging due to exponential scaling of quantum mechanical calculations. Results: We present a quantum-classical hybrid framework for simulating protein fragment electronic structure using variational quantum algorithms. We construct fermionic Hamiltonians from experimentally determined protein structures, map them to qubits via Jordan-Wigner transformation, and optimize ground state energies using the Variational Quantum Eigensolver implemented in pure Python. For a 4-orbital serine protease fragment, we achieve chemical accuracy (< 1.6 mHartree) with 95.3% correlation energy recovery. Systematic analysis reveals three-phase convergence behaviour with exponential decay (α = 0.95), power law optimization (γ = 1.21), and asymptotic approach. Application to SARS-CoV-2 protease inhibition demonstrates predictive accuracy (MAE=0.25 kcal/mol), while cytochrome P450 metabolism predictions achieve 85% site accuracy. Conclusions: This work establishes a pathway for quantum-enhanced biomolecular simulations on near-term quantum hardware, bridging quantum algorithm development with practical biological applications.
The rapid expansion of biomolecular datasets presents significant challenges for computational biology. Quantum computing emerges as a promising solution to address these complexities. This study introduces a novel quantum framework for analyzing TART-T and TART-C gene data by integrating genomic and structural information. Leveraging a Quantum Neural Network (QNN), we classify hotspot mutations, utilizing quantum superposition to uncover intricate relationships within the data. Additionally, a Variational Quantum Eigensolver (VQE) is employed to estimate molecular ground-state energies through a hybrid classical-quantum approach, overcoming the limitations of traditional computational methods. Implemented using IBM Qiskit, our framework demonstrates high accuracy in both mutation classification and energy estimation on current Noisy Intermediate-Scale Quantum (NISQ) devices. These results underscore the potential of quantum computing to advance the understanding of gene function and protein structure. Furthermore, this research serves as a foundational blueprint for extending quantum computational methods to other genes and biological systems, highlighting their synergy with classical approaches and paving the way for breakthroughs in drug discovery and personalized medicine.