A Hybrid Quantum-AI Framework for Protein Structure Prediction on NISQ Devices

2025-10-07

AI Summary

To address the limited energy landscape resolution of variational quantum algorithms on NISQ devices—which hinders accuracy in protein structure prediction—this paper introduces the first quantum-classical energy fusion framework. It employs the Variational Quantum Eigensolver (VQE) on an IBM 127-qubit processor to generate a coarse-grained global energy surface, which is then refined via calibration with statistical potentials predicted by the NSP3 neural network. This work achieves the first end-to-end, energy-function-level integration of quantum computation and data-driven modeling, overcoming the accuracy bottlenecks of purely quantum or purely classical approaches. In benchmarking across 75 protein fragments and 375 conformations, the method attains a mean RMSD of 4.9 Å—significantly outperforming AlphaFold3, ColabFold, and a pure quantum baseline (p < 0.001). The framework establishes a scalable, hybrid quantum-classical paradigm for biomolecular simulation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: 3D Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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 consistent improvements over AlphaFold3, ColabFold, and quantum-only predictions, achieving a mean RMSD of 4.9 Å with statistical significance (p < 0.001). 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.

Problem

• Improving protein structure prediction accuracy on noisy quantum devices
• Combining quantum algorithms with deep learning for energy fusion
• Refining low-resolution quantum energy landscapes using neural networks

Innovation

• Hybrid quantum-AI framework for protein structure prediction
• Combines VQE quantum computation with deep learning
• Fuses quantum energy landscapes with neural network potentials