Although recent advances in quantum machine learning (QML) offer significant potential for enhancing generative models, particularly in molecular design, a large array of classical approaches still face challenges in achieving high fidelity and validity. In particular, the integration of QML with sequence-based tasks, such as Simplified Molecular Input Line Entry System (SMILES) string reconstruction, remains underexplored and usually suffers from fidelity degradation. In this work, we propose a hybrid quantum-classical architecture for SMILES reconstruction that integrates quantum encoding with classical sequence modeling to improve quantum fidelity and classical similarity. Our approach achieves a quantum fidelity of approximately 84% and a classical reconstruction similarity of 60%, surpassing existing quantum baselines. Our work lays a promising foundation for future QML applications, striking a balance between expressive quantum representations and classical sequence models and catalyzing broader research on quantum-aware sequence models for molecular and drug discovery.
Although recent advances in quantum machine learning (QML) offer significant potential for enhancing generative models, particularly in molecular design, a large array of classical approaches still face challenges in achieving high fidelity and validity. In particular, the integration of QML with sequence-based tasks, such as Simplified Molecular Input Line Entry System (SMILES) string reconstruction, remains underexplored and usually suffers from fidelity degradation. In this work, we propose a hybrid quantum-classical architecture for SMILES reconstruction that integrates quantum encoding with classical sequence modeling to improve quantum fidelity and classical similarity. Our approach achieves a quantum fidelity of approximately 84% and a classical reconstruction similarity of 60%, surpassing existing quantum baselines. Our work lays a promising foundation for future QML applications, striking a balance between expressive quantum representations and classical sequence models and catalyzing broader research on quantum-aware sequence models for molecular and drug discovery.
We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps Simplified Molecular Input Line Entry System (SMILES) strings directly to quantum states using parameterized rotation gates, preserving vital structural information. Its quantum encoder-decoder framework enables latent space compression and reconstruction. A dual-objective strategy optimizes fidelity and minimizes trash state deviation. Our evaluations demonstrate effective capture of molecular characteristics and a remarkable preservation of fidelity, approaching robust molecular reconstruction even with substantial dimensionality reduction. Our model introduces a novel quantum pathway in cheminformatics, capable of processing complete molecular structural information with a dedicated quantum architecture considering the Noisy Intermediate-Scale Quantum (NISQ)-era development and promising significant advances in drug and materials discovery.
Finding drug-like molecules is a time- and money-consuming task that requires virtual screening techniques capable of accurately representing non-linear intermolecular relationships. Classical ML models usually consider molecular graphs or SMILES sequences, but cannot jointly exploit both structural and sequential information, let alone simultaneously. Of greater importance, numerous deep learning methods are hindered by poor generalisation, black-box predictions, and inadequate interpretability, reducing their feasibility for application in real-life drug discovery pipelines. In this paper, we present QDrugDiscoverAI, a hybrid quantum–classical deep learning framework for predicting molecular properties and optimising molecules to overcome these challenges. We present a framework for multimodal molecular representation learning that combines graph attention networks and transformer-based sequence encoders with variational quantum circuits to increase expressive capacity within a single architecture. It implements a dual-stream representation-learning mechanism with a multimodal feature fusion layer. It includes an explainability module that uses attention weights and SHAP values to identify substructures that contribute to model predictions and enhance interpretability. Moreover, QDrugDiscoverAI also includes a quantum–assisted reinforcement-learning module for a molecule-optimisation workflow, enabling more efficient exploration of the chemical design space while leveraging the controllability of quantum simulation. The experiments on three benchmark datasets, i.e., Lipophilicity, HIV, and BBBP, show improvements of 12.4%, 9.6%, and 11.1% in accuracy, AUC, and F1-score, respectively, over the matched classical and unimodal baselines. Quantitative evaluation of quantum components and fusion strategies is also conducted through ablation studies to demonstrate their roles further. In summary, the framework proposed serves as a systematic integration of interpretable hybrid learning, showing one way it could be organised to become increasingly efficient, and demonstrating the advantage of using quantum-simulated components to enhance classical deep learning models for exploration and predictive performance in drug discovery workflows. The implementation code and supporting scripts for the proposed QDrugDiscoverAI framework are publicly available at: https://github.com/Sripada-RamaSree/QuantumDrugAI
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via quantum parallelism, motivating research in quantum machine learning (QML). Among QML techniques, quantum autoencoders show promise for compressing high-dimensional quantum and classical data. However, designing effective quantum circuit architectures for quantum autoencoders remains challenging due to the complexity of selecting gates, arranging circuit layers, and tuning parameters.
This paper proposes a neural architecture search (NAS) framework that automates the design of quantum autoencoders using a genetic algorithm (GA). By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders for data reconstruction without becoming trapped in local minima. We demonstrate effectiveness on image datasets, highlighting the potential of quantum autoencoders for efficient feature extraction within a noise-prone, near-term quantum era. Our approach lays a foundation for broader application of genetic algorithms to quantum architecture search, aiming for a robust, automated method that can adapt to varied data and hardware constraints.
Classical machine learning models and quantum kernel methods often struggle to capture quantum-coherent molecular features under the constraints of noisy intermediate-scale quantum (NISQ) hardware, limiting both predictive accuracy and scalability. This paper introduces the Molecular Quantum Particle Algorithm (MQPA), a hybrid quantum–classical framework designed to achieve chemically accurate property prediction by integrating handcrafted molecular descriptors with parameterized quantum circuits. Molecular inputs, expressed as SMILES strings, are processed via RDKit and encoded through angle-based quantum gates with entangling layers in Qiskit [1]. Quantum parameters are optimized using simultaneous perturbation stochastic approximation (SPSA) [2], while classical regression layers leverage Adam [3] with Low-Rank Adaptation (LoRA) fine-tuning [4]. Across 38 systematically varied subsets of the QM9 benchmark [5,6], MQPA achieves a mean absolute error (MAE) of 0.03 kcal/mol, outperforming both deep learning and quantum kernel baselines by 41% and surpassing the chemical accuracy threshold of 0.1 kcal/mol [7–9]. Extensive cross-platform benchmarking—including IBM Quantum, Qiskit Aer, and NVIDIA H100 clusters—demonstrates strong reproducibility, with inter-backend MAE variance below 3%. Positioned as a scalable and hardware-aware solution, MQPA offers practical advantages for quantum-enhanced molecular property prediction across applications in quantum chemistry, drug discovery, materials design, and environmental modeling.
Quantum machine learning (QML) has become a key research field on the interface between quantum computing, artificial intelligence, chemistry and pharmaceutical science. By enhancing the molecular representation, predictive modeling, generative chemistry, and quantum-assisted simulations workflows, researchers are increasingly arguing that QML can aid work in molecular design and drug discovery. Meanwhile, this field is still in its new frontiers with much of the published literature only comprising reviews, simulations, proof-of-concept applications, and hybrid quantum-classical demonstrations as opposed to large-scale pharmaceutical application. In this qualitative study, the literature emerging is reviewed to determine how QML is being conceptualized, areas of application seem the most promising, and what obstacles remain that hamper its practical implementation in actual drug discovery pipelines. The paper presents five large themes segmented into a thematic analysis of recent reviews and representative studies and includes relevant themes as QML as a representational innovation, QML in predictive tasks, e.g., drug-target interaction and ADMET modeling, QML in generative molecular design, hybrid quantum-classical workflows as the most plausible approach in the near term, and the continuation of restrictions in terms of hardware, trainability, data This research paper concludes that the idea of QML must be seen at present as a field not as an alternative to the classical machine learning in drug discovery, but it is an opportunity with strategic and scientific potential whose future realization may be found in the subproblems carefully chosen where the influence of molecular quantum effects, the complexity of learning, and the hybrid implementations converge. Keywords: quantum machine learning, molecular design, drug discovery, qualitative study, quantum computing, generative chemistry, ADMET, drug-target interaction
Quantum machine learning (QML) has attracted significant interest in drug discovery due to its potential to model complex molecular interactions and improve prediction accuracy for protein-ligand binding affinity. However, most existing hybrid quantum-classical approaches rely on static architectures that apply quantum computation uniformly across all samples, regardless of molecular complexity or prediction confidence. Such static execution strategies lead to inefficient quantum resource utilization and limit scalability on near-term noisy intermediatescale quantum (NISQ) devices. In this paper, we propose an uncertainty-aware adaptive hybrid quantum-classical learning framework for protein-ligand binding affinity prediction that treats quantum computation as a selectively invoked resource rather than a constant component of the learning pipeline. The proposed framework first employs a classical ensemble-based regression model to generate binding affinity predictions along with an associated uncertainty estimate derived from ensemble variance. This uncertainty information is then used by a decision mechanism to dynamically select between classical-only inference, shallow quantum circuit execution, or deep quantum circuit execution. By adapting the quantum circuit depth and invocation frequency to the predicted uncertainty, the framework enables decision-centric allocation of quantum resources. Experimental evaluation of binding affinity data demonstrates that the recommended adaptive framework utilizes around 76 % less quantum resources than static hybrid quantum learning methods that use fixed-depth quantum circuits for all samples. The framework also preserves decision-level functionality and a steady inference workflow. These findings highlight the effectiveness of uncertainty-guided adaptivity as a practical strategy and offer a scalable foundation for future quantum-enhanced molecular learning.method for incorporating QML into drug development pipelines while taking hardware constraints into consideration.