Treffer: Quantum computing for radiation therapy optimization.
Title:
Quantum computing for radiation therapy optimization.
Authors:
Rahimi R; Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA., SaiToh A; Department of Computer and Information Sciences, Sojo University, Kumamoto, Japan., Modiri A; Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA., Nakano Y; Center for Quantum Information and Quantum Biology, Osaka University, Toyonaka, Japan., Okada KN; Center for Quantum Information and Quantum Biology, Osaka University, Toyonaka, Japan., Tsukano S; Center for Quantum Information and Quantum Biology, Osaka University, Toyonaka, Japan., Zhang B; Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA., Fujii K; Center for Quantum Information and Quantum Biology, Osaka University, Toyonaka, Japan., Kitagawa M; Center for Quantum Information and Quantum Biology, Osaka University, Toyonaka, Japan., Sawant A; Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Source:
Medical physics [Med Phys] 2026 Jan; Vol. 53 (1), pp. e70269.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE
Imprint Name(s):
Publication: 2017- : Hoboken, NJ : John Wiley and Sons, Inc.
Original Publication: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics.
Original Publication: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics.
MeSH Terms:
References:
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Aghaee M, Ramirez AA, Alam Z et al. Interferometric single‐shot parity measurement in InAs–Al hybrid devices. Nature. 2025;638:651‐655.
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Pellow‐Jarman A, Mcfarthing S, Sinayskiy I, Park DK, Pillay A, Petruccione F. The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices. Sci Rep. 2024;14:16011. doi:10.1038/s41598‐024‐66625‐6.
Preskill J. Quantum computing in the NISQ era and beyond. Quantum. 2018;2:79. doi:10.22331/q‐2018‐08‐06‐79.
Nazareth DP, Spaans JD. First application of quantum annealing to IMRT beamlet intensity optimization. Phys Med Biol. 2015;60:4137‐4148. doi:10.1088/0031‐9155/60/10/4137.
Pakela JM, Tseng H‐H, Matuszak MM, Ten Haken RK, Mcshan DL, El Naqa I. Quantum‐inspired algorithm for radiotherapy planning optimization. Med Phys. 2020;47:5‐18. doi:10.1002/mp.13840.
Dirac PaM. A new notation for quantum mechanics. Math Proc Cambr Philos Soc. 1939;35:416‐418. doi:10.1017/S0305004100021162.
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Shiekh AY. The role of quantum interference in quantum computing. Int J Theor Phys. 2006;45:1646‐1648. doi:10.1007/s10773‐005‐9025‐8.
Stahlke D. Quantum interference as a resource for quantum speedup. Phys Rev A. 2014;90:022302. doi:10.1103/PhysRevA.90.022302.
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Kandala A, Temme K, Córcoles AD, Mezzacapo A, Chow JM, Gambetta JM. Error mitigation extends the computational reach of a noisy quantum processor. Nature. 2019;567:491‐495. doi:10.1038/s41586‐019‐1040‐7.
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PsiQuantum. https://www.psiquantum.com.
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Hagiwara M. Introduction to quantum deletion error‐correcting codes. IEICE Trans Fund Electr Commun Comp Sci. 2025;E108‐A(3):363–375. doi:10.1587/transfun.2024TAI0001.
Johnson MW, Bunyk P, Maibaum F, et al. A scalable control system for a superconducting adiabatic quantum optimization processor. Supercond Sci Technol. 2010;23:065004. doi:10.1088/0953‐2048/23/6/065004.
Krantz P, Kjaergaard M, Yan F, Orlando TP, Gustavsson S, Oliver WD. A quantum engineer's guide to superconducting qubits. Appl Phys Rev. 2019;6:021318. doi:10.1063/1.5089550.
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Yarkoni S, Raponi E, Bäck T, Schmitt S. Quantum annealing for industry applications: introduction and review. Rep Prog Phys. 2022;85:104001. doi:10.1088/1361‐6633/ac8c54.
Cohen J, Khan A, Alexander C, Portfolio Optimization of 40 Stocks Using the DWave Quantum Annealer. arXiv.2007.01430 (2020).
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Grover LK. Quantum mechanics helps in searching for a needle in a haystack. Phys Rev Lett. 1997;79:325‐328. doi:10.1103/PhysRevLett.79.325.
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Matoušek M, Pernal K, Pavošević F, Veis L. Variational quantum eigensolver boosted by adiabatic connection. J Phys Chem A. 2024;128:687‐698.
Misiewicz JP, Evangelista FA. Implementation of the projective quantum eigensolver on a quantum computer. J Phys Chem A. 2024;128:2220‐2235. doi:10.1021/acs.jpca.3c07429.
Kandala A, Mezzacapo A, Temme K, et al. Hardware‐efficient variational quantum eigensolver for small molecules and quantum magnets. Nature. 2017;549:242‐246. doi:10.1038/nature23879.
Faílde D, Viqueira JD, Mussa Juane M, Gómez A. Using differential evolution to avoid local minima in variational quantum algorithms. Sci Rep. 2023;13:16230.
Zhou L, Wang S‐T, Choi S, Pichler H, Lukin MD. Quantum approximate optimization algorithm: performance, mechanism, and implementation on near‐term devices. Phys Rev X. 2020;10:021067.
Farhi E, Goldstone J, Gutmann S, Zhou L. The quantum approximate optimization algorithm and the sherrington‐kirkpatrick model at infinite size. Quantum. 2022;6:759. doi:10.22331/q‐2022‐07‐07‐759.
Boev AS, Rakitko AS, Usmanov SR, et al. Genome assembly using quantum and quantum‐inspired annealing. Sci Rep. 2021;11:13183. doi:10.1038/s41598‐021‐88321‐5.
Arrazola JM, Delgado A, Bardhan BR, Lloyd S. Quantum‐inspired algorithms in practice. Quantum. 2020;4:307. doi:10.22331/q‐2020‐08‐13‐307.
Montiel Ross OH. A review of quantum‐inspired metaheuristics: going from classical computers to real quantum computers. IEEE Access. 2020;8:814‐838. doi:10.1109/ACCESS.2019.2962155.
Mikki SM, Kishk AA. Quantum particle swarm optimization for electromagnetics. IEEE Trans Antennas Propagat. 2006;54:2764‐2775. doi:10.1109/TAP.2006.882165.
Alvarez‐Alvarado MS, Alban‐Chacón FE, Lamilla‐Rubio EA, Rodríguez‐Gallegos CD, Velásquez W. Three novel quantum‐inspired swarm optimization algorithms using different bounded potential fields. Sci Rep. 2021;11:11655. doi:10.1038/s41598‐021‐90847‐7.
Agarwal P, Sahoo A, Garg D. An improved quantum inspired particle swarm optimization for forest cover prediction. Ann Data Sci. 2024:2217‐2233. doi:10.1007/s40745‐023‐00509‐w.
Mccaskey AJ, Parks ZP, Jakowski J, et al. Quantum chemistry as a benchmark for near‐term quantum computers. npj Quantum Inf. 2019;5:1‐8. doi:10.1038/s41534‐019‐0209‐0.
Farhi E, Goldstone J, Gutmann S, A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem. arXiv.1412.6062 (2015).
Azure Quantum. https://quantum.microsoft.com/.
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Quantum information science. https://www.nist.gov/.
Quantum @ ORNL | ORNL. https://www.ornl.gov/quantum.
Forschungszentrum Jülich. https://www.fz‐juelich.de/.
RIKEN Center for Quantum Computing. https://www.riken.jp/.
Fakhimi R, Validi H. In: Pardalos P M & Prokopyev O A, eds. Quantum Approximate Optimization Algorithm (QAOA). in Encyclopedia of Optimization:1‐7. doi:10.1007/978‐3‐030‐54621‐2_854‐1.
Pellow‐Jarman A, Mcfarthing S, Sinayskiy I, Park DK, Pillay A, Petruccione F. The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices. Sci Rep. 2024;14:16011. doi:10.1038/s41598‐024‐66625‐6.
Preskill J. Quantum computing in the NISQ era and beyond. Quantum. 2018;2:79. doi:10.22331/q‐2018‐08‐06‐79.
Nazareth DP, Spaans JD. First application of quantum annealing to IMRT beamlet intensity optimization. Phys Med Biol. 2015;60:4137‐4148. doi:10.1088/0031‐9155/60/10/4137.
Pakela JM, Tseng H‐H, Matuszak MM, Ten Haken RK, Mcshan DL, El Naqa I. Quantum‐inspired algorithm for radiotherapy planning optimization. Med Phys. 2020;47:5‐18. doi:10.1002/mp.13840.
Dirac PaM. A new notation for quantum mechanics. Math Proc Cambr Philos Soc. 1939;35:416‐418. doi:10.1017/S0305004100021162.
Heisenberg W. Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik. Z Physik. 1927;43:172‐198. doi:10.1007/BF01397280.
Bužek V, Braunstein SL, Hillery M, Bruß D. Quantum copying: a network. Phys Rev A. 1997;56:3446‐3452.
Merzbacher E. The early history of quantum tunneling. Phys Today. 2002;55:44‐49. doi:10.1063/1.1510281.
Shiekh AY. The role of quantum interference in quantum computing. Int J Theor Phys. 2006;45:1646‐1648. doi:10.1007/s10773‐005‐9025‐8.
Stahlke D. Quantum interference as a resource for quantum speedup. Phys Rev A. 2014;90:022302. doi:10.1103/PhysRevA.90.022302.
Shor PW. Scheme for reducing decoherence in quantum computer memory. Phys Rev A. 1995;52:R2493‐R2496. doi:10.1103/PhysRevA.52.R2493.
Steane AM. Error correcting codes in quantum theory. Phys Rev Lett. 1996;77:793‐797. doi:10.1103/PhysRevLett.77.793.
Einstein A, Podolsky B, Rosen N. Can quantum‐mechanical description of physical reality be considered complete?. Phys Rev. 1935;47:777‐780. doi:10.1103/PhysRev.47.777.
Bell JS. On the Einstein Podolsky Rosen paradox. Phys Phys Fizika. 1964;1:195‐200. doi:10.1103/PhysicsPhysiqueFizika.1.195.
D‐Wave Ocean. https://docs.ocean.dwavesys.com/.
Russell J, D‐Wave Embraces Gate‐Based Quantum Computing; https://www.hpcwire.com/.
Shin SW, Smith G, Smolin JA, Vazirani U, How ‘Quantum’ is the D‐Wave Machine? arXiv.1401.7087 (2014).
DiVincenzo DP. The physical implementation of quantum computation. Fortschritte der Physik. 2000;48:771‐783. doi:10.1002/1521‐3978(200009)48:9/11%3c771::AID‐PROP771%3e3.0.CO;2‐E.
Georgescu I. The DiVincenzo criteria 20 years on. Nat Rev Phys. 2020;2:666‐666. doi:10.1038/s42254‐020‐00256‐4.
Arute F, Arya K, Babbush R, et al. Quantum supremacy using a programmable superconducting processor. Nature. 2019;574:505‐510. doi:10.1038/s41586‐019‐1666‐5.
Kandala A, Temme K, Córcoles AD, Mezzacapo A, Chow JM, Gambetta JM. Error mitigation extends the computational reach of a noisy quantum processor. Nature. 2019;567:491‐495. doi:10.1038/s41586‐019‐1040‐7.
AbuGhanem M, Eleuch H. NISQ computers: a path to quantum supremacy. IEEE Access. 2024;12:102941‐102961. doi:10.1109/ACCESS.2024.3432330.
Chen S, Cotler J, Huang H‐Y, Li J. The complexity of NISQ. Nat Commun. 2023;14:6001. doi:10.1038/s41467‐023‐41217‐6.
IBM Quantum. https://quantum.ibm.com/.
IonQ. https://ionq.com.
Rigetti Computing. https://www.rigetti.com.
PsiQuantum. https://www.psiquantum.com.
Xanadu. https://www.xanadu.ai.
Nielsen MA, Chuang IL. Quantum Computation and Quantum Information. Cambridge University Press; 2000.
Preskill J. Sufficient condition on noise correlations for scalable quantum computing. Quant Inf Comp. 2013;13(34):181‐194. doi:10.26421/QIC13.3‐4‐1.
Hagiwara M. Introduction to quantum deletion error‐correcting codes. IEICE Trans Fund Electr Commun Comp Sci. 2025;E108‐A(3):363–375. doi:10.1587/transfun.2024TAI0001.
Johnson MW, Bunyk P, Maibaum F, et al. A scalable control system for a superconducting adiabatic quantum optimization processor. Supercond Sci Technol. 2010;23:065004. doi:10.1088/0953‐2048/23/6/065004.
Krantz P, Kjaergaard M, Yan F, Orlando TP, Gustavsson S, Oliver WD. A quantum engineer's guide to superconducting qubits. Appl Phys Rev. 2019;6:021318. doi:10.1063/1.5089550.
Weinberg SJ, Sanches F, Ide T, Kamiya K, Correll R. Supply chain logistics with quantum and classical annealing algorithms. Sci Rep. 2023;13:4770. doi:10.1038/s41598‐023‐31765‐8.
Yarkoni S, Raponi E, Bäck T, Schmitt S. Quantum annealing for industry applications: introduction and review. Rep Prog Phys. 2022;85:104001. doi:10.1088/1361‐6633/ac8c54.
Cohen J, Khan A, Alexander C, Portfolio Optimization of 40 Stocks Using the DWave Quantum Annealer. arXiv.2007.01430 (2020).
Montanaro A. Quantum algorithms: an overview. NPJ Quantum Inf. 2016;2:15023. doi:10.1038/npjqi.2015.23.
Grover LK. Quantum mechanics helps in searching for a needle in a haystack. Phys Rev Lett. 1997;79:325‐328. doi:10.1103/PhysRevLett.79.325.
Peruzzo A, Mcclean J, Shadbolt P, et al. A variational eigenvalue solver on a photonic quantum processor. Nat Commun. 2014;5:4213. doi:10.1038/ncomms5213.
Matoušek M, Pernal K, Pavošević F, Veis L. Variational quantum eigensolver boosted by adiabatic connection. J Phys Chem A. 2024;128:687‐698.
Misiewicz JP, Evangelista FA. Implementation of the projective quantum eigensolver on a quantum computer. J Phys Chem A. 2024;128:2220‐2235. doi:10.1021/acs.jpca.3c07429.
Kandala A, Mezzacapo A, Temme K, et al. Hardware‐efficient variational quantum eigensolver for small molecules and quantum magnets. Nature. 2017;549:242‐246. doi:10.1038/nature23879.
Faílde D, Viqueira JD, Mussa Juane M, Gómez A. Using differential evolution to avoid local minima in variational quantum algorithms. Sci Rep. 2023;13:16230.
Zhou L, Wang S‐T, Choi S, Pichler H, Lukin MD. Quantum approximate optimization algorithm: performance, mechanism, and implementation on near‐term devices. Phys Rev X. 2020;10:021067.
Farhi E, Goldstone J, Gutmann S, Zhou L. The quantum approximate optimization algorithm and the sherrington‐kirkpatrick model at infinite size. Quantum. 2022;6:759. doi:10.22331/q‐2022‐07‐07‐759.
Boev AS, Rakitko AS, Usmanov SR, et al. Genome assembly using quantum and quantum‐inspired annealing. Sci Rep. 2021;11:13183. doi:10.1038/s41598‐021‐88321‐5.
Arrazola JM, Delgado A, Bardhan BR, Lloyd S. Quantum‐inspired algorithms in practice. Quantum. 2020;4:307. doi:10.22331/q‐2020‐08‐13‐307.
Montiel Ross OH. A review of quantum‐inspired metaheuristics: going from classical computers to real quantum computers. IEEE Access. 2020;8:814‐838. doi:10.1109/ACCESS.2019.2962155.
Mikki SM, Kishk AA. Quantum particle swarm optimization for electromagnetics. IEEE Trans Antennas Propagat. 2006;54:2764‐2775. doi:10.1109/TAP.2006.882165.
Alvarez‐Alvarado MS, Alban‐Chacón FE, Lamilla‐Rubio EA, Rodríguez‐Gallegos CD, Velásquez W. Three novel quantum‐inspired swarm optimization algorithms using different bounded potential fields. Sci Rep. 2021;11:11655. doi:10.1038/s41598‐021‐90847‐7.
Agarwal P, Sahoo A, Garg D. An improved quantum inspired particle swarm optimization for forest cover prediction. Ann Data Sci. 2024:2217‐2233. doi:10.1007/s40745‐023‐00509‐w.
Mccaskey AJ, Parks ZP, Jakowski J, et al. Quantum chemistry as a benchmark for near‐term quantum computers. npj Quantum Inf. 2019;5:1‐8. doi:10.1038/s41534‐019‐0209‐0.
Farhi E, Goldstone J, Gutmann S, A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem. arXiv.1412.6062 (2015).
Azure Quantum. https://quantum.microsoft.com/.
Ising E. Beitrag zur theorie des ferromagnetismus. Z Physik. 1925;31:253‐258. doi:10.1007/BF02980577.
Faddegon B, Descovich M, Chen K, et al. A digital male pelvis phantom series showing anatomical variations over the course of fractionated radiotherapy treatment. Med Phys. 2024;51:3034‐3044. doi:10.1002/mp.16865.
Kadowaki T, Nishimori H. Quantum annealing in the transverse Ising model. Phys Rev E. 1998;58:5355‐5363. doi:10.1103/PhysRevE.58.5355.
Lucas A. Ising formulations of many NP problems. Front Phys. 2014;2. doi:10.3389/fphy.2014.00005.
Solve utility‐scale quantum optimization problems. https://learning.quantum.ibm.com/.
Blekos K, Brand D, Ceschini A, et al. A review on quantum approximate optimization algorithm and its variants. Phys Rep. 2024;1068:1‐66. doi:10.1016/j.physrep.2024.03.002.
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Contributed Indexing:
Keywords: cancer treatment; medical physics; optimization; quantum computing; quantum optimization algorithms; quantum technology in healthcare; radiation therapy
Entry Date(s):
Date Created: 20260106 Date Completed: 20260106 Latest Revision: 20260120
Update Code:
20260120
DOI:
10.1002/mp.70269
PMID:
41491010
Database:
MEDLINE
Weitere Informationen
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.
(© 2026 American Association of Physicists in Medicine.)