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How can quantum technologies be applied in healthcare, medicine and the life sciences?

Published online by Cambridge University Press:  08 February 2023

Frederik F. Flöther*
Affiliation:
IBM Quantum, IBM Research, Säumerstrasse 4, CH–8803 Rüschlikon, Switzerland QuantumBasel, uptownBasel Infinity Corp., Schorenweg 44, CH-4144 Arlesheim, Switzerland
Paul F. Griffin
Affiliation:
Department of Physics, University of Strathclyde, Glasgow G4 0NG, United Kingdom
*
Author for correspondence: Frederik F. Flöther, Email: [email protected]
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Extract

Quantum technologies, including computing, communication/security and sensing, have significantly advanced over the last years. Industry-specific applications are now being intensely researched and healthcare, medicine and the life sciences represent one of the focus areas.

Type
Question
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Context

Quantum technologies, including computing, communication/security and sensing, have significantly advanced over the last years. Industry-specific applications are now being intensely researched and healthcare, medicine and the life sciences represent one of the focus areas.

For medical quantum computing, the initial focus was on biochemical and computational biology problems (Emani et al., Reference Emani2021; Fedorov and Gelfand, Reference Fedorov and Gelfand2021; Outeiral et al., Reference Outeiral2021; Marchetti et al., Reference Marchetti2022; Cordier et al., Reference Cordier2022; Santagati et al., Reference Santagati2023); recently, clinical quantum computing experiments have increasingly drawn interest (Prousalis and Konofaos, Reference Prousalis and Konofaos2019; Abbott, Reference Abbott2021; Moradi et al., Reference Moradi2022). In the last few years alone, over 40 studies on medical proof-of-concept quantum computing applications have been conducted, spanning genomics, clinical research and discovery, diagnostics and treatments/interventions. In particular, quantum machine learning/artificial intelligence has rapidly evolved and shown to be competitive with classical approaches in certain cases.

As concerns quantum security, the sensitivity of medical data is a key driving force. Due to the risk of “harvest now, decrypt later” attacks (Harishankar et al., Reference Harishankar2023), quantum-safe standards are already being developed (NIST Announces First Four Quantum-Resistant Cryptographic Algorithms, 2023), and there are governmental directives around the preparation and implementation of quantum-safe cryptography (Migrating to Post-Quantum Cryptography, 2023).

Finally, quantum sensors seek to enhance diagnosis and treatment through highly sensitive measurements of physical and biological parameters. Diverse examples exist. First, single-photon detection techniques achieve improved low-light resolutions for applications such as cell dynamics (Callenberg et al., Reference Callenberg2021). Second, quantum correlations may be harnessed to improve signal-to-noise ratios in magnetic resonance imaging (MRI) and positron emission tomography (PET) (Watts et al., Reference Watts2021). Third, quantum dots are being used in cancer therapy to enhance the targeting and delivery of drugs to tumours (Ruzycka-Ayoush et al., Reference Ruzycka-Ayoush2021). Fourth, atomic magnetometers enable accurate non-invasive magnetic sensing and imaging of relevant biological activity, including cardiac (MCG) (Bison et al., Reference Bison2009), foetal (fMCG) (Strand et al., Reference Strand2019), muscular (Broser et al., Reference Broser2018), and brain activity (MEG) (Brookes et al., Reference Brookes2022). The latter is an exemplar of the technology uptake, moving from the first demonstrations (Xia et al., Reference Xia2006) to complete commercial systems in little over a decade.

This research question entails mapping the landscape of quantum technology applications in healthcare, medicine and the life sciences and providing a near-term and long-term outlook. Key threads include:

  1. 1. What is the state of quantum computing applications and which quantum algorithms have shown promise in the sector?

  2. 2. What are key technical challenges, for instance around clinical and real-world data, error handling and real-time computations, and how might they be addressed on the path towards full-scale quantum computing implementations?

  3. 3. What is the state of quantum communication and security applications and what sector-specific considerations apply?

  4. 4. What is the state of quantum sensing applications in the sector?

  5. 5. How are the needs of the medical researchers and practitioners guiding development of quantum sensors?

  6. 6. What are the challenges to be met, ethical and otherwise, in order to speed up quantum technology translation and achieve widespread adoption by clinicians, medical practitioners and researchers?

  7. 7. What role should medicine-focussed quantum computing consortia and ecosystems play?

Quantum technology is poised to become a key enabler for progress towards precision medicine: keeping people healthy through proactive medical care and guidance at the level of an individual. Addressing the research threads above is essential in order to fully realise the technological promises in this space.

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Competing interests

The authors declare none.

References

Abbott, A (2021) Quantum computers to explore precision oncology. Nature Biotechnology 39, 13241325.CrossRefGoogle ScholarPubMed
Bison, G, et al. (2009) A room temperature 19-channel magnetic field mapping device for cardiac signals. Applied Physics Letters 95, 173701.CrossRefGoogle Scholar
Brookes, MJ, et al. (2022) Magnetoencephalography with optically pumped magnetometers (OPM-MEG): the next generation of functional neuroimaging. Trends in Neurosciences 45, 621634.CrossRefGoogle ScholarPubMed
Broser, P, et al. (2018) Optically pumped magnetometers for magneto-myography to study the innervation of the hand. IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, 22262230.CrossRefGoogle Scholar
Callenberg, C, et al. (2021) Super-resolution time-resolved imaging using computational sensor fusion. Scientific Reports 11, 1689.CrossRefGoogle ScholarPubMed
Cordier, BA, et al. (2022) Biology and medicine in the landscape of quantum advantages. Journal of the Royal Society Interface 19, 20220541.CrossRefGoogle ScholarPubMed
Emani, PS, et al. (2021) Quantum computing at the frontiers of biological sciences. Nature Methods 18, 701709.CrossRefGoogle ScholarPubMed
Fedorov, AK and Gelfand, M (2021) Towards practical applications in quantum computational biology. Nature Computational Science 1, 114119.CrossRefGoogle Scholar
Harishankar, R, et al. (2023) Security in the quantum computing era. IBM Institute for Business Value, 25 January. Available at https://www.ibm.com/downloads/cas/EZEGKEB5 Google Scholar
Marchetti, L, et al. (2022) Quantum computing algorithms: Getting closer to critical problems in computational biology. Briefings in Bioinformatics 23, bbac437.CrossRefGoogle ScholarPubMed
Migrating to Post-Quantum Cryptography (2023) Executive Office of the President, 25 January. Available at https://www.whitehouse.gov/wp-content/uploads/2022/11/M-23-02-M-Memo-on-Migrating-to-Post-Quantum-Cryptography.pdf Google Scholar
Moradi, S, et al. (2022) Clinical data classification with noisy intermediate scale quantum computers. Scientific Reports 12, 19.CrossRefGoogle ScholarPubMed
NIST Announces First Four Quantum-Resistant Cryptographic Algorithms (2023) NIST, 25 January. Available at https://www.nist.gov/news-events/news/2022/07/nist-announces-first-four-quantum-resistant-cryptographic-algorithms Google Scholar
Outeiral, C, et al. (2021) The prospects of quantum computing in computational molecular biology. Wiley Interdisciplinary Reviews: Computational Molecular Science 11, e1481.Google Scholar
Prousalis, K and Konofaos, N (2019) A quantum pattern recognition method for improving pairwise sequence alignment. Scientific Reports 9, 111.CrossRefGoogle ScholarPubMed
Ruzycka-Ayoush, M, et al. Quantum dots as targeted doxorubicin drug delivery nanosystems in human lung cancer cells. Cancer Nanotechnology 12, (2021): 8.CrossRefGoogle Scholar
Santagati, R, et al. Drug design on quantum computers. arXiv preprint arXiv:2301.04114 (2023).Google Scholar
Strand, S, et al. (2019) Low-cost fetal magnetocardiography: A comparison of superconducting quantum interference device and optically pumped magnetometers. Journal of the American Heart Association 8, e013436.CrossRefGoogle ScholarPubMed
Watts, DP, et al. (2021) Photon quantum entanglement in the MeV regime and its application in PET imaging. Nature Communications 12, 2646.CrossRefGoogle ScholarPubMed
Xia, H, et al. (2006) Magnetoencephalography with an atomic magnetometer. Applied Physics Letters 89, 211104.CrossRefGoogle Scholar