arXiv subjects

Cheng-Chang Lu

Publications and source records attributed to Cheng-Chang Lu.

4 recordsLinked to original sources

arXiv2025

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

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 {\AA} 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.

cs.ET↗

arXiv2025

QDockBank: A Dataset for Ligand Docking on Protein Fragments Predicted on Utility-Level Quantum Computers

Protein structure prediction is a core challenge in computational biology, particularly for fragments within ligand-binding regions, where accurate modeling is still difficult. Quantum computing offers a novel first-principles modeling paradigm, but its application is currently limited by hardware constraints, high computational cost, and the lack of a standardized benchmarking dataset. In this work, we present QDockBank-the first large-scale protein fragment structure dataset generated entirely using utility-level quantum computers, specifically designed for protein-ligand docking tasks. QDockBank comprises 55 protein fragments extracted from ligand-binding pockets. The dataset was generated through tens of hours of execution on superconducting quantum processors, making it the first quantum-based protein structure dataset with a total computational cost exceeding one million USD. Experimental evaluations demonstrate that structures predicted by QDockBank outperform those predicted by AlphaFold2 and AlphaFold3 in terms of both RMSD and docking affinity scores. QDockBank serves as a new benchmark for evaluating quantum-based protein structure prediction.

cs.ET↗

arXiv2025

A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors

Accurate prediction of protein active-site structures remains a central challenge in structural biology, particularly for short and flexible peptide fragments where conventional and simulation-based methods often fail. Here, we present a quantum computing framework specifically developed for utility-level quantum processors to address this problem. Starting from an amino acid sequence, we formulate structure prediction as a ground-state energy minimization problem using the Variational Quantum Eigensolver (VQE). Amino acid connectivity is encoded on a tetrahedral lattice model, and structural constraints-including steric, geometric, and chirality terms-are mapped into a problem-specific Hamiltonian represented as sparse Pauli operators. Optimization is performed with a two-stage architecture that separates energy estimation from measurement decoding, enabling noise mitigation under realistic device conditions. We evaluate the framework on 23 randomly selected protein fragments from the PDBbind dataset and 7 fragments from therapeutically relevant proteins, and execute experiments on the IBM-Cleveland Clinic quantum processor. Predictions are benchmarked against AlphaFold 3 (AF3) and classical simulation-based approaches using identical postprocessing and docking procedures. Our method outperforms both AF3 and classical baselines in RMSD (root-mean-square deviation) and docking efficacy. These results demonstrate an end-to-end, hardware-executable pipeline for biologically relevant structure prediction on real quantum processors, highlighting its engineering feasibility and practical advantages over existing classical and deep learning approaches.

cs.ET↗

arXiv2024

PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud

Benefiting from cloud computing, today's early-stage quantum computers can be remotely accessed via the cloud services, known as Quantum-as-a-Service (QaaS). However, it poses a high risk of data leakage in quantum machine learning (QML). To run a QML model with QaaS, users need to locally compile their quantum circuits including the subcircuit of data encoding first and then send the compiled circuit to the QaaS provider for execution. If the QaaS provider is untrustworthy, the subcircuit to encode the raw data can be easily stolen. Therefore, we propose a co-design framework for preserving the data security of QML with the QaaS paradigm, namely PristiQ. By introducing an encryption subcircuit with extra secure qubits associated with a user-defined security key, the security of data can be greatly enhanced. And an automatic search algorithm is proposed to optimize the model to maintain its performance on the encrypted quantum data. Experimental results on simulation and the actual IBM quantum computer both prove the ability of PristiQ to provide high security for the quantum data while maintaining the model performance in QML.

quant-ph↗