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.
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.
• Published on bioRxiv, successfully applied the variational quantum eigensolver (VQE) algorithm to calculate the electronic structure of a tau peptide fragment. • Overcame the exponential limits of existing classical computers, demonstrating the possibility of precise biomolecular energy calculations even on NISQ-era hardware. • Presents a scalable pathway for quantum chemical analysis of complex large protein systems through a fragment-based workflow. [Quantum Biology Society] Quantum computing technology, which overcomes the limitations of classical computers, has been deployed to elucidate the aggregation mechanism of the tau protein, one of the core causes of Alzheimer's disease. By precisely calculating the electronic structure of complex biological systems like peptides at the subatomic level, revolutionary changes are expected in the design of new drugs for treating neurodegenerative diseases in the future. Recently, an empirical study titled "Quantum Computing Reveals Energetics of Tau Peptide Fragments" was published on the preprint repository bioRxiv. A research team led by Dr. Anuj Guruacharya and Dr. Binita Rajbanshi at Nanil Therapeutics Inc. in the USA announced the results of successfully applying a quantum algorithm to a peptide system through this paper. ■ 'Variational Quantum Eigensolver (VQE)' Breaking Through Classical Limits Accurately predicting electron interactions within biomolecules is the core of new drug development, but there has been a fatal problem where the amount of computation required by classical computers increases exponentially as the molecule size grows. To solve this, the research team introduced the variational quantum eigensolver (VQE) algorithm, which can be run even on current noisy intermediate-scale quantum (NISQ) devices. The research team targeted a peptide fragment, which is a core region of the tau protein aggregates observed in the brains of Alzheimer's disease patients, and successfully derived its electronic structure and ground state energy under a quantum environment. ■ Elucidating the Differences Between Electronic Structure Methods and Classical Force Fields One of the greatest achievements of this study is that it rigorously compared and verified the energy calculations from the quantum algorithm against existing classical methods. As a result of the analysis, the quantum energy values from VQE showed systematic agreement with classical restricted Hartree-Fock (RHF) calculations, but exhibited a weak correlation with classical molecular dynamics (MD) force-field energies widely used in conventional drug design. This suggests that classical force-field models have limitations in fully capturing the electronic properties of biomolecules, proving that quantum mechanical treatment is essential for more precise drug design. ■ Securing Fragment-Based Scalability for Macromolecular Protein Analysis Instead of calculating the entire macromolecular protein at once, the researchers integrated a fragment-based workflow into VQE, dividing the molecule into smaller pieces for computation. This approach presents a practical pathway to scale to large systems using current quantum computing resources efficiently, while preserving the essential quantum characteristics of complex biological peptide systems. This empirical study is evaluated as a significant milestone demonstrating how quantum biology theory can be integrated into actual drug development platforms. Experts predict that as this technology advances, ultra-precise peptide drug design and the prediction of complex protein-protein interactions—which were impossible in the past due to computational cost issues—will become possible in near real-time.
Abstract Quantum computing in biology is one of the most rapidly evolving field of technology. Protein folding is one of the key challenges which requires accurate and efficient algorithms with a quick computational time. Structural conformations of proteins with disordered regions need colossal amount of computational resource to map its least energy conformation state. In this regard, quantum algorithms like Variational quantum eigensolver (VQE) are applied in the current research work to predict the lowest energy value of 50 peptides of 7 amino acids each. VQE is initially used to calculate the energy values over which Variational Quantum Optimization is applied via Conditional Value at Risk (CVaR) over 100 iterations of 500000 shots each to obtain least ground state energy value. This is compared to the molecular dynamics-based simulations of 50 nanoseconds each to calculate the energy values along with the folding pattern. The results suggest efficient folding outcomes from CvaR-VQE compared to MD based simulations. With the ever-expanding quantum hardware and improving algorithms the problem of protein folding can be resolved to obtain in depth insights on the biological process and drug design.
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.
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.
The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for noisy intermediate-scale quantum (NISQ) hardware to estimate eigenvalues of many-body Hamiltonians. Unlike fully quantum approaches such as quantum phase estimation (QPE), VQE trades deep coherent circuits for repeated state preparation, measurement, and classical optimization, making it more compatible with limited qubit counts and finite coherence times. Recent developments have focused on reducing quantum resource requirements while retaining chemically meaningful wavefunction structure. In this paper, we examine recent progress in VQE methods for molecular electronic structure with an emphasis on three themes: (i) strategies for circuit and ansatz complexity reduction, including adaptive and selectively screened approaches, (ii) chemically motivated workflows that combine VQE with orbital optimization, fragmentation, and localized active-space ideas to better address strong correlation, and (iii) extensions of VQE to excited-state calculations. Throughout, we emphasize the tradeoffs among parameter count, gate depth, symmetry preservation, measurement overhead, and classical preprocessing, and discuss where these approaches may become most useful for chemically challenging active spaces. We also highlight benchmarking considerations for assessing both accuracy and resource requirements, and conclude with a perspective on regimes in which VQE may offer the greatest long-term value, particularly multireference active spaces and low-lying excited-state manifolds.
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.