Quantum computing (QC) is based on the principles of quantum mechanics (superposition, entanglement, and quantum interference) and uses fundamentally different approach then classical computing [1,2]. Classical computers process information sequentially whereas in contrast, a quantum computer performs computation simultaneously. Because of this quantum computers have advantage of performing complex calculations more efficiently than classical computers.

QC is emerging as a potential tool with applications in health care sector. Some possible applications include genomic analysis, drug discovery, protein folding, medical imaging, personalized medicine, cancer research and treatment [1,2,10]. Radiation therapy treatment planning is heavily dependent on the advanced computational methods for planning, as it involves optimization and precise radiation delivery to anatomical targets. Modern radiation therapy like IMRT (Intensity-Modulated Radiation Therapy), IGRT (Image Guided Radiation Therapy), VMAT (Volumetric Modulated Arc Therapy), SBRT (Stereotactic Body Radiotherapy) utilize inverse planning to determine optimal aperture or beamlet weights and shapes [3].

Computational Challenges in Radiation Oncology

Advanced technologies like IMRT, IGRT, SBRT and Particle Therapy relies on highly complex spatial configurations i.e. conformal doses to tumors while strictly sparing surrounding healthy tissue resulting highly non-uniform, steep dose gradients tailored to irregular or concave target geometries [3]. This requires high-dimensional optimization and complex pattern recognition resulting computational challenges that are difficult to handle by classical computing systems.

In Real-time adaptive radiotherapy, daily replanning is required for adjusting dose distribution to accommodate target (tumors) motion and changing patient anatomy, resulting classical computers hit an operational bottleneck. Further, tight clinical timelines forces the practitioners to resolve non-convex dose distribution problems by approximations.

Quantum computing overcomes these constraints by employing quantum algorithms for radiation therapy planning [10]. Quantum algorithms can perform complex calculation exponentially faster than traditional computing systems.

Quantum Algorithms for Radiotherapy Optimization:

Radiotherapy optimization demands evaluating exponential number of possible beam angle permutations and beamlet intensities. Quantum mechanics fundamental principle of superposition and quantum tunneling permits processing of high- dimensional data simultaneously. Quantum algorithms can handle radiotherapy optimization by solving NP-hard task in a fraction of time compare to what is required by classical algorithms.

Quantum Tunnel Annealing (QTA) and Quantum Approximate Optimization Algorithm (QAOA):

Quantum annealing algorithm achieves the global minimum of dose-volume planning objectives without getting trapped in local minima. Solving radiotherapy optimization problem has been recently demonstrated using quantum annealing on a D-Wave quantum annealer [4,7,11].

Quantum Approximate Optimization Algorithm (QAOA) is a quantum-classical hybrid algorithm and can be used for finding the optimal combination of radiation beam angles, weights, and apertures or beamlets. Hybrid algorithm generate highly optimal beam angles and spot intensities much faster than classical algorithm like simulated annealing [4,5,6,7].

Quantum Monte Carlo Integration (QMCI): Monte Carlo (MC) simulation in radiotherapy is considered as a gold standard for radiation transport modeling through heterogeneous human anatomy and dose calculation. But major limitation with clinical application of MC simulation is long computational processing time that hinders real-time adaptive radiotherapy planning [8]

QMCI framework uses quantum generative adversarial network (qGAN) for state preparation and quantum amplitude estimation (QAE) algorithm for integration, to map photon scattering profiles [9]. Study indicates that QMCI successfully reproduces standard percentage depth dose curves, with drastic reduction in computation time [9].

Current Challenges [1,2,4]:

Despite being potential candidate, there are some technical impediments that constraints quantum computing in radiation oncology. Hardware limitations like imperfection in qubit (qubit decay), environmental interference (noise) and temperature fluctuations results major challenge. Error correction without affecting the ongoing computational process and achieving a fault-tolerant quantum system suitable for clinical application is yet to be realized.

CONCLUSION:

Integrating quantum computing with radiation oncology marks a disruptive paradigm shift for high- precision conformal radiation therapy. Implementing quantum frameworks for optimization and dose estimation moves radiation oncology closer to true real-time, personalized adaptive radiotherapy. As hardware limitations are resolved, quantum frameworks are bound to become a standard infrastructure in clinical physics departments.

REFERENCES:

1. Thomas J S Durant  et.al., A primer for quantum computing and its applications to healthcare and biomedical research, Journal of the American Medical Informatics Association, Vol. 31, Issue 8, August 2024, 1774–1784.
2. Chow, J.C.L., Quantum Computing in Medicine. Sci., 2024, 12, 67.
3. Mayles W.P.M. et.al., Handbook of Radiotherapy Physics Theory and Practice, 2^nd, 2022.
4. Rahimi R, et al. Quantum computing for radiation therapy optimization. Medical Physics, 2026;53(1):312-325.
5. Farhi, E., Goldstone, J., & Gutmann, S. (2014). "A Quantum Approximate Optimization Algorithm." arXiv:1411.4028
6. Christos Papalitsas, et.al., Quantum Approximate Optimization Algorithms for Molecular Docking arXiv:2503.04239v1 [cs.ET]
7. Julia M Pakelaal., Quantum-inspired algorithm for radiotherapy planning optimization, Medical Physics, 2020 Jan; 47(1):5-18
8. Chetty, I. J., et al. (2007). Report of the AAPM Task Group No. 105: Issues associated with clinical implementation of Monte Carlo-based photon and electron dose calculation algorithms. Medical Physics, 34(12), 4818-4853.
9. Chaemin Kimal., Study of Cancer Radiation Therapy with Quantum Computing, Oct 21, 2025 (https://inspirehep.net/literature/3105324)
10. Ramesh S, et.al., Quantum Computing for Oncology, Nature Cancer, 2024;5:411–419
11. Smith J, et al., Quantum tunnel annealing algorithm for intensity-modulated radiation therapy optimization, Medical Physics, 2019; 46(10):4311-4322.