We formalize an inverse, data-conditioned variant of the Variational Quantum Eigensolver (VQE) for clinical biomarker discovery. Given patient-encoded quantum states, we construct a task-specific Hamiltonian whose coefficients are inferred from clinical associations, and interpret its expectation value as a calibrated energy score for prognosis and treatment monitoring. The method integrates principled coefficient estimation, ansatz specification with basis rotations, commuting-group measurements, and a practical shot-budget analysis. Evaluated on public infectious-disease datasets under severe class imbalance, the approach yields consistent gains in balanced accuracy and precision-recall over strong classical baselines, with stability across random seeds and feature ablations. This variational energy-scoring framework bridges Hamiltonian learning and clinical risk modeling, offering a compact, interpretable, and reproducible route to biomarker prioritization and decision support.
We formalize an inverse, data-conditioned variant of the Variational Quantum Eigensolver (VQE) for clinical biomarker discovery. Given patient-encoded quantum states, we construct a task-specific Hamiltonian whose coefficients are inferred from clinical associations, and interpret its expectation value as a calibrated energy score for prognosis and treatment monitoring. The method integrates principled coefficient estimation, ansatz specification with basis rotations, commuting-group measurements, and a practical shot-budget analysis. Evaluated on public infectious-disease datasets under severe class imbalance, the approach yields consistent gains in balanced accuracy and precision-recall over strong classical baselines, with stability across random seeds and feature ablations. This variational energy-scoring framework bridges Hamiltonian learning and clinical risk modeling, offering a compact, interpretable, and reproducible route to biomarker prioritization and decision support.
Optimizing personalized cancer treatment plans that combine chemotherapy, radiation, and immunotherapy presents a complex challenge due to the need to balance efficacy with minimizing side effects across diverse patient profiles. This study introduces a novel hybrid quantum-classical framework leveraging the Variational Quantum Eigensolver (VQE) for multi-modal optimization. Treatment planning is formulated as a parameterized Hamiltonian, where the ground state corresponds to an optimal therapy regimen tailored to individual genomic and clinical data. The quantum VQE algorithm estimates ground state energy via quantum simulators, while classical optimizers iteratively refine circuit parameters. Benchmarking on synthetic datasets demonstrates that the hybrid approach enhances treatment personalization and computational efficiency over classical methods. Although current quantum hardware constraints limit immediate deployment, our findings validate the potential for quantum-enhanced optimization in clinical oncology. This research lays foundational groundwork for integrating near-term quantum algorithms into medical decision-making, with promising implications for improving cancer treatment outcomes and patient quality of life.
Combining variational quantum process with classical neural learning offers a flexible route to improve ground-state estimation. We establish an integrated framework that connects neural transformations, measurement statistics, and physical stability through three requirements: self-contained training, polynomial resource scaling, and variational consistency. Its constructive realization, termed unitary variational quantum-neural hybrid eigensolver (U-VQNHE), couples a variational quantum circuit to a neural phase function through norm-preserving post-processing. The learned transformation is evaluated from measurement records, preserves the exact variational bound, and admits range-independent concentration guarantees for independent finite-shot evaluations. Complementing this construction, we characterize the statistical and representational constraints of amplitude reweighting: sampled-support mismatch can destabilize empirical normalization, while polynomial-range reweighting exactly matches only an exponentially small fraction of Haar-random target distributions from a fixed ansatz, and exact matching for structured ansatz--target pairs can require exponentially large dynamic range under specified near-tensorizability and mismatch conditions. Finite-shot simulations on Ising and disordered XYZ spin models demonstrate improved energy accuracy over the underlying variational quantum eigensolver and greater robustness than the amplitude-reweighting baselines. Together, these results provide a principled foundation for quantum--neural eigensolvers in which physical consistency, expressive capacity, and measurement cost are treated as a single design problem.
Abstract Quantum computing has emerged as a powerful tool for modeling molecular systems with high accuracy, accelerating the search for novel therapeutic targets. In this study, we present a two-stage hybrid workflow that combines a Quantum Graph Neural Network with a Variational Quantum Eigensolver to identify potent serine neutralizers in the QM9 dataset. In the first stage, an advanced quantum graph neural network architecture incorporating attention layers, self-distillation, and an adaptive learning-rate schedule is trained to predict ionization potentials and binding free energies. Across five independent random-seed trials, our adaptive-thresholded QGNN–VQE pipeline achieves an average R2 R 2 of 0.990±0.008 0.990 ± 0.008 and a mean absolute error of 0.034±0.001 0.034 ± 0.001 eV ( ≈0.79±0.03 ≈ 0.79 ± 0.03 kcal/mol) on the QM9 validation set, demonstrating robust, chemical-accuracy-level predictions. Building on these high-precision predictions, the second stage employs a QAOA-inspired hybrid ranking scheme that merges quantum graph neural network outputs, feature-space similarity (via and cosine similarity), and variational quantum eigensolver-derived energy stability. This α α -weighted ( α=0.95 α = 0.95 ) scoring framework is applied to over 133,000 molecules, efficiently parsed in parallel. The final ranked list is validated through an improved bijection test, featuring an adaptive similarity threshold to ensure both structural and functional robustness. The top-scoring candidate identified by our pipeline is 5,6,7-tetrahydro-4 H-pyrazolo[4,3-c]pyridin-4-one, highlighting the efficacy of quantum-enhanced modeling in pinpointing complex pharmacological targets. These findings underscore the transformative potential of hybrid quantum-classical workflows in drug discovery, offering a flexible blueprint for advanced screening of other critical biomolecular interactions in the rapidly expanding field of quantum biology. GraphicAbstract VQE for Serine Neutralization
Cardiovascular disease (CVD) remains a major global health challenge, necessitating advanced predictive models for early detection and risk assessment. This paper presents a quantum-enhanced machine learning framework for multi-modal cardiac biomarker analysis. The model integrates quantum feature encoding with a deep learning-based classification pipeline. Multi-modal data from imaging, electrophysiological signals, and genetic biomarkers are mapped into quantum-enhanced feature spaces using Cirq. These transformed features are then fed into a hybrid quantum-classical neural network (QNN) to improve prediction accuracy. The proposed approach is benchmarked against classical machine learning methods such as Support Vector Machines (SVM) and Random Forest (RF). Results show the potential of quantum computing in advancing precision medicine by leveraging high-dimensional feature transformations.
The Variational Quantum Eigensolver (VQE) has emerged as a transformative approach in quantum computing, providing a practical means to tackle complex quantum systems. This chapter delves into the essential components and strategies for enhancing VQE performance, focusing on key areas such as hybrid optimization techniques, quantum circuit design, and measurement efficiency. By integrating classical and quantum resources, hybrid optimization methods significantly improve convergence rates while reducing the computational burden on quantum devices. The chapter explores the design of quantum circuits, emphasizing the importance of selecting appropriate variational ansätze to achieve optimal results. Challenges related to Hamiltonian simulation and measurement are critically examined, alongside innovative solutions to mitigate noise and enhance measurement fidelity. Through these comprehensive insights, this chapter aims to illuminate the potential of VQE in advancing quantum machine learning applications across diverse scientific domains.
Asthma remains a prevalent chronic respiratory disorder characterized by complex pathophysiological mechanisms and heterogeneous clinical manifestations. Accurate and timely diagnosis is critical for mitigating disease progression and optimizing patient outcomes. Conventional machine learning approaches often struggle with high-dimensional biomedical datasets, where redundant or irrelevant features can obscure discriminative patterns and degrade predictive performance. In this study, we propose a hybrid quantum-classical learning framework for asthma diagnosis that integrates Quantum Approximate Optimization Algorithm (QAOA)-based feature selection with a Variational Quantum Classifier (VQC) using angle embedding. The publicly available Asthma Disease Dataset from Kaggle, comprising demographic, lifestyle, environmental, and clinical variables, serves as the evaluation benchmark. The QAOA module exploits quantum parallelism, superposition, and entanglement to identify an optimal subset of predictive features, achieving an average dimensionality reduction of 73.3%. The selected features are subsequently encoded into quantum states and classified by a multi-layer VQC with chain entanglement. Simulations were conducted on a noiseless Pennylane default.qubit backend to assess the theoretical performance ceiling. Experimental results demonstrate that the proposed pipeline achieves an accuracy of 98.4%, surpassing classical baselines such as SVM, Random Forest, and MLP, while maintaining competitive precision, recall, and F1-scores. These findings underscore the potential of hybrid quantum-classical architectures for high-dimensional diagnostic tasks and lay the groundwork for future deployment of quantum-assisted clinical decision support systems in respiratory healthcare.