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.
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.
The optimization of multidrug cancer treatment schedules is a high-dimensional combinatorial optimization problem involving nonlinear tumor dynamics, pharmacokinetic interactions, and stringent toxicity constraints. When exploring large therapeutic search spaces, conventional optimization algorithms often exhibit limited scalability and become trapped in local optima. This simulation study proposes a hybrid quantum-classical optimization framework that employs QA to generate adaptive treatment schedules for precision oncology. The treatment scheduling problem is formulated as a quadratic unconstrained binary optimization (QUBO) model, which encodes drug combinations, dosing intervals, toxicity constraints, and synergistic interactions within an Ising Hamiltonian compatible with D-Wave quantum annealing hardware. Global combinatorial optimization is performed using a D-Wave Advantage quantum annealer, while sequential least squares programming refines continuous dosage parameters under pharmacokinetic and toxicity constraints. Tumor progression is represented using a modified Gompertz growth model coupled with first-order pharmacokinetic dynamics. The proposed framework was evaluated using synthetic Digital Twin datasets generated from clinically realistic pharmacokinetic and tumor growth parameters. Simulation results demonstrated a maximum tumor regression ratio of 0.98 ± 0.02, compared with 0.82 ± 0.05 for standard-of-care protocols, while maintaining a lower cumulative toxicity index (40 ± 5 versus 75 ± 8) and reducing optimization time to less than 2 s for 120-variable scheduling problems. The hybrid framework exhibited near-linear computational scalability and maintained robustness under stochastic dosage perturbations (TRR > 0.95 at σ = 0.25). However, predictive classification performance remained limited (AUC = 0.49), highlighting the need for validation using longitudinal clinical datasets and multimodal patient-specific data. These findings demonstrate the potential of hybrid quantum-classical optimization as a scalable computational framework for adaptive treatment scheduling and personalized precision oncology.
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.
Variational Quantum Eigensolver (VQE) has emerged as a prominent hybrid quantum-classical algorithm for near-term quantum computing, particularly in applications like quantum machine learning. However, the performance of VQE is significantly constrained by the limitations of classical optimization methods employed to minimize the expectation value of the Hamiltonian. Traditional VQE approaches often suffer from slow convergence and sensitivity to initial conditions, particularly when dealing with complex, high-dimensional problems encountered in machine learning scenarios. This work introduces a novel adaptive quantum computing framework for VQE optimization, designed to dynamically adjust the quantum circuit parameters during the optimization process. By continuously monitoring the optimization progress and intelligently modifying the parameters, we aim to accelerate convergence and improve the robustness of VQE algorithms for machine learning tasks. The core idea is to shift from a static, fixed-parameter VQE to a dynamic, adaptive one. We explore various adaptive control strategies and demonstrate their potential to significantly reduce the number of iterations required to reach a solution, leading to improved computational efficiency and enabling VQE to tackle more complex machine learning problems within the constraints of current quantum hardware. The research focuses on the theoretical underpinnings and the design principles of this adaptive approach, laying the foundation for future implementations and experimental validation.
The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems that are currently intractable on classical computers. VQE trains parameterized quantum circuits using a classical optimizer to approximate the eigenvalues and eigenstates of a given Hamiltonian. However, VQE faces challenges in task-specific design and machine-specific architecture, particularly when running on noisy quantum devices. This can have a negative impact on its trainability, accuracy, and efficiency, resulting in noisy quantum data. We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE by learning from noisy VQE outputs. Our approach can significantly decrease energy estimation errors and increase fidelities with ground states compared to noisy input data for the H2 , LiH, and BeH2 molecular Hamiltonians and the transverse field Ising model. Surprisingly, it only requires noisy data for training. Variational denoising can be integrated into quantum hardware, increasing its versatility as an end-to-end quantum processing for quantum data. Published by the American Physical Society 2024
Quantum machine learning (QML) leverages quantum phenomena such as superposition, entanglement, and interference to address computational challenges that are difficult for classical systems. In radiation oncology, where treatment planning, image guidance, adaptive replanning, and multi-modal data integration require high-dimensional optimisation and complex pattern recognition, QML may offer advantages in computational efficiency and representational capacity. This review systematically maps quantum algorithms to the clinical radiation oncology workflow. We outline foundational concepts in quantum computing, including complexity insights from the BQP class and practical constraints of the noisy intermediate-scale quantum (NISQ) era. The radiation oncology pipeline-from consultation and simulation to treatment planning, delivery, and follow-up-is analysed to identify computational bottlenecks where quantum methods could provide benefit. Current developments in quantum-enabled diagnostic modelling, quantum-inspired clinical decision support for precision radiotherapy, quantum reinforcement learning for adaptive treatment policies, and emerging concepts such as quantum digital twins are reviewed. Key challenges-including hardware limitations, barren plateaus, data encoding constraints, limited datasets, regulatory considerations, and the gap between theoretical speedups and clinical implementation-are critically examined. Finally, a three-horizon roadmap is proposed outlining near-term proof-of-concept research, mid-term integration with treatment planning systems, and long-term prospects for fault-tolerant quantum simulation in oncology.
Modern radiation oncology depends heavily on computational algorithms for inverse treatment planning, Monte Carlo dose simulations, and real-time image guidance. Classical computing architectures face critical scaling bottlenecks as clinical techniques advance toward ultra-high-resolution volumetric modulated arc therapy (VMAT) and adaptive radiotherapy. This paper explores the transformation of radiotherapy by integration of quantum computing for handling complex planning problems. Quantum computing in radiation oncology promises drastic reduction in planning time by leveraging quantum algorithms for optimization and dose estimation. Preliminary.