Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation

• URL: http://arxiv.org/abs/2506.01177v2
• Date: Fri, 25 Jul 2025 02:17:57 GMT
• Title: Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation
• Abstract summary: We systematically optimize the quantum-classical bridge architecture of generative adversarial networks (GANs) for molecule discovery.<n>Our optimized model (BO-QGAN) significantly improves performance, achieving a 2.27-fold higher Drug Candidate Score (DCS) than prior quantum-hybrid benchmarks.<n>This work provides the first empirically-grounded architectural guidelines for hybrid models, enabling more effective integration of current quantum computers into pharmaceutical research pipelines.
• Score: 0.3683202928838613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hybrid quantum-classical machine learning offers a path to leverage noisy intermediate-scale quantum (NISQ) devices for drug discovery, but optimal model architectures remain unclear. We systematically optimize the quantum-classical bridge architecture of generative adversarial networks (GANs) for molecule discovery using multi-objective Bayesian optimization. Our optimized model (BO-QGAN) significantly improves performance, achieving a 2.27-fold higher Drug Candidate Score (DCS) than prior quantum-hybrid benchmarks and 2.21-fold higher than the classical baseline, while reducing parameter count by more than 60%. Key findings favor layering multiple (3-4) shallow (4-8 qubit) quantum circuits sequentially, while classical architecture shows less sensitivity above a minimum capacity. This work provides the first empirically-grounded architectural guidelines for hybrid models, enabling more effective integration of current quantum computers into pharmaceutical research pipelines.

Related papers

• Generative Quantum-inspired Kolmogorov-Arnold Eigensolver [12.645057942348794]
  We present the generative quantum-inspired Kolmogorov-Arnold eigensolver (GQKAE) for quantum chemistry.<n>GQKAE replaces the parameter-heavy feed-forward network components in GPT-style generative eigensolvers with hybrid quantum-inspired Kolmogorov-Arnold network modules.
  arXiv  Detail & Related papers  (2026-05-06T07:53:55Z)
• Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics [54.3410887955176]
  Crime pattern analysis is critical for law enforcement and predictive policing.<n>The surge in criminal activities from rapid urbanization creates high-dimensional, imbalanced datasets.<n>This study presents a quantum-classical comparison framework for crime analytics.
  arXiv  Detail & Related papers  (2026-04-08T06:05:25Z)
• Parallel Multi-Circuit Quantum Feature Fusion in Hybrid Quantum-Classical Convolutional Neural Networks for Breast Tumor Classification [0.0]
  We present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset.<n>Our results indicate that hybrid QCNN architectures can leverage entanglement and quantum feature fusion to enhance medical image classification tasks.
  arXiv  Detail & Related papers  (2025-11-29T17:47:14Z)
• Hybrid Quantum-Classical Selective State Space Artificial Intelligence [1.4896509623302832]
  We propose a Hybrid Quantum Classical selection mechanism for the Mamba architecture for temporal sequence classification problems.<n>Our approach leverages Variational Quantum Circuits (VQCs) as quantum gating modules that both enhance feature extraction and improve suppression of irrelevant information.<n>We analyze how introducing quantum subroutines into large language models (LLMs) impacts their generalization capability, expressivity, and parameter efficiency.
  arXiv  Detail & Related papers  (2025-11-11T15:26:57Z)
• Solving the Hubbard model with Neural Quantum States [66.55653324211542]
  We study the state-of-the-art results for the doped two-dimensional (2D) Hubbard model.<n>We find different attention heads in the NQS ansatz can directly encode correlations at different scales.<n>Our work establishes NQS as a powerful tool for solving challenging many-fermions systems.
  arXiv  Detail & Related papers  (2025-07-03T14:08:25Z)
• Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification [3.8876018618878585]
  Quantum convolutional neural networks (QCNNs) have demonstrated promising results in classifying both quantum and classical data.<n>This study proposes a novel strategy to enhance feature processing and a QCNN architecture for improved classification accuracy.
  arXiv  Detail & Related papers  (2025-07-02T18:51:56Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM [2.348041867134616]
  Identifying molecular properties, including side effects, is a critical yet time-consuming step in drug development.<n>Recent advancements have been made in the classical domain using augmented variations of the Simplified Molecular Line-Entry System (SMILES)<n>This study presents the first analysis of these approaches, providing novel insights into their potential for enhancing molecular property prediction and side effect identification.
  arXiv  Detail & Related papers  (2025-04-29T14:03:31Z)
• QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs [0.70224924046445]
  Physics-informed neural networks (PINNs) have emerged as promising methods for solving partial differential equations (PDEs)<n>We present a quantum-classical physics-informed neural network (QCPINN) that combines quantum and classical components.<n>QCPINN achieves stable convergence and comparable accuracy, while requiring approximately 10% of the trainable parameters used in classical approaches.
  arXiv  Detail & Related papers  (2025-03-20T19:52:26Z)
• Hybrid Quantum Neural Networks with Variational Quantum Regressor for Enhancing QSPR Modeling of CO2-Capturing Amine [0.9968037829925945]
  We develop hybrid quantum neural networks (HQNN) to improve structure-property relationship modeling for CO2-capturing amines.<n>HQNNs improve predictive accuracy for key solvent properties, including basicity, viscosity, boiling point, melting point, and vapor pressure.
  arXiv  Detail & Related papers  (2025-03-01T07:26:45Z)
• Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset [0.0]
  We introduce a Quantum-Classical Bayesian Neural Network (QCBNN) that is capable to perform uncertainty-aware classification of medical dataset. We track multiple behavioral metrics that capture both predictive performance as well as model's uncertainty. It is our ambition to create a hybrid model that is capable to classify samples in a more uncertainty aware fashion.
  arXiv  Detail & Related papers  (2024-06-10T14:23:25Z)
• Towards Efficient Quantum Hybrid Diffusion Models [68.43405413443175]
  We propose a new methodology to design quantum hybrid diffusion models. We propose two possible hybridization schemes combining quantum computing's superior generalization with classical networks' modularity.
  arXiv  Detail & Related papers  (2024-02-25T16:57:51Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Pooling techniques in hybrid quantum-classical convolutional neural networks [0.0]
  In-depth study of pooling techniques in hybrid quantum-classical convolutional neural networks (QCCNNs) for classifying 2D medical images is performed. We find similar or better performance in comparison to an equivalent classical model and QCCNN without pooling. It is promising to study architectural choices in QCCNNs in more depth for future applications.
  arXiv  Detail & Related papers  (2023-05-09T16:51:46Z)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing [75.75419308975746]
  This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification. Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
  arXiv  Detail & Related papers  (2022-02-17T09:55:21Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron Correlation [58.720142291102135]
  We present a novel hybrid-classical algorithm that computes a molecule's all-electron energy and properties on the classical computer. We demonstrate the ability of the quantum-classical hybrid algorithms to achieve chemically relevant results and accuracy on currently available quantum computers.
  arXiv  Detail & Related papers  (2021-06-22T18:00:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.