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.
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.
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.
High-fidelity dose calculation is a still computationally intensive task in radiation therapy planning. While the Monte Carlo method is considered to be most accurate, its clinical application is often hindered by long computation times. Quantum computing offers a potential pathway to accelerate these simulations. We applied Quantum Monte Carlo integration (QMCI) as a proof-of-concept for radiotherapy dose calculation. Our QMCI framework consists of two main components: State preparation using quantum generative adversarial network (qGAN) and integration by applying quantum amplitude estimation (QAE) algorithm. We calculate the depth-dose distribution for a 6 MeV photon beam by focusing on the dominant firstorder Compton scattering. Our QMCI results closely reproduce the key features of the analytical and Geant4 reference depthdose curves, validating the framework’s potential as a scalable foundation for future, clinically relevant quantum applications in radiotherapy.
BACKGROUND: Quantum computing (QC) is emerging as a transformative tool for solving complex optimization problems across various fields, including biomedical applications. While classical optimization methods are well-established, they frequently face limitations when applied to complex and large-scale problems in radiotherapy planning. PURPOSE: This study aims to explore the implementation and evaluate the effectiveness of quantum optimization methods, specifically quantum annealing and the quantum approximate optimization algorithm (QAOA), in radiotherapy planning. In particular, we employ an Ising Hamiltonian formulation of the cost function, empirically implementing it on annealing-based quantum hardware and for the first time on circuit-based one. METHODS: We formulated a simplified radiotherapy optimization problem and solved it using quantum annealing on a D-Wave quantum annealer. Subsequently, we adapted this optimization problem for the QAOA framework and implemented it on IBM Quantum circuit-model hardware. Comparative analyses were conducted between classical and quantum methods and implementations, highlighting QC's potential advantages and limitations in specific optimization contexts. To demonstrate that the Hamiltonian formulation is valid and practically usable, we first tested it in simplified proof-of-principle examples and then extended it to a more clinically relevant bilateral prostate proton plan. In this new example, dose parameters were extracted directly from a commercial treatment planning system (RayStation) and incorporated into the Hamiltonian optimization workflow. Both one-qubit-per-voxel and two-qubits-per-voxel encodings were evaluated to illustrate scalability. Additionally, we discussed scalability considerations, practical challenges, and future research directions necessary for integrating quantum algorithms into routine clinical radiation therapy practices. RESULTS: To our knowledge, this study presents the first demonstration of using QC circuit-model hardware for radiotherapy planning optimization. The quantum annealing approach successfully determined the optimal solution. Convergence was achieved after 20 iterations on a quantum simulator (noise free) and after 100 iterations on actual quantum hardware (due to inherent hardware noise). In the bilateral prostate proton plan derived from realistic data, the Hamiltonian-based optimization assigned higher dose to the prostate relative to surrounding organs-at-risk, confirming the feasibility of applying QC optimization directly to clinically sourced parameters. CONCLUSIONS: Quantum optimization techniques demonstrate potential advantages over classical methods, particularly in complex optimization scenarios relevant to radiation therapy. The formulation of a Hamiltonian cost function, its validation on real quantum hardware, and its application to realistic data collectively represent a first concrete step toward QC-based treatment planning optimization in medical physics. Future research should focus on addressing scalability, overcoming practical implementation challenges, and advancing the development of scalable, fault-tolerant quantum systems suitable for clinical integration.
This research introduces a novel quantum-enhanced intelligent system tailored for personalized adaptive radiotherapy dose estimation. The system efficiently models radiation transport and predicts patient-specific dose distributions by integrating quantum algorithms, deep learning, and Monte Carlo simulations. Quantum-enhanced Monte Carlo simulations, employing algorithms such as Harrow-Hassidim-Lloyd (HHL) and Variational Quantum Eigensolver (VQE), achieve computational speedups of 8-15 times compared to classical methods while maintaining high accuracy. The deep learning architecture leverages convolutional and recurrent neural networks to capture complex anatomical and dosimetric patterns. Validation on simulated datasets demonstrates a 50-70% reduction in mean absolute error and 2-3% improvements in gamma index metrics compared to conventional approaches. Dose-volume histogram analysis further highlights enhanced Dice coefficients and reduced Hausdorff distances. These advancements underscore the potential for precise, efficient, and clinically relevant dose estimations, paving the way for improved outcomes in personalized adaptive radiotherapy.
Background: Quantum computing represents a paradigm leap in computational capabilities, with potential applications across various scientific disciplines. The complicated computations required for imaging, radiation therapy, and molecular modeling in medical physics give novel prospects for quantum computing. Objective: This article investigates the role of quantum computing in medical physics research, with an emphasis on its potential to improve computational efficiency and accuracy and develop novel treatment approaches. Methods: A thorough literature analysis was undertaken to investigate recent advances in quantum computing and their implications in medical physics. Case studies were reviewed to demonstrate practical applications and possible advantages. Theoretical models were tested to forecast future developments. Statistical data from quantum computing implementations were gathered from many sources, particularly on improving processing speed and accuracy in medical physics applications. Results: The findings show that quantum computing can significantly improve the processing speed of complex simulations and imaging techniques, with some quantum algorithms outperforming classical algorithms by up to 100 times. In radiation therapy planning, quantum computing has exhibited a 25% boost in dose distribution calculation precision. Quantum algorithms have lowered calculation times in molecular modeling by about 40%, potentially boosting drug development and customized therapy by 30%. Conclusion: Quantum computing has the potential to alter medical physics by increasing computational capacity, which could lead to advancements in diagnostics, treatment planning, and medication development. The statistical results corroborate the promise of quantum computing in these domains, emphasizing the importance of ongoing interdisciplinary collaboration and research to fully achieve these benefits and address present hurdles in incorporating quantum computing into clinical practice.
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.