Quantum computing > Life sciences and biotech
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essential to finding remission mechanisms and guiding the appropriate treatments. Scientists are battling to beat these challenges from different perspectives. The most conservative ones keep trying to replicate cancer in animal models even though science itself has demonstrated that using animals does not work. However, other scientists are banking on developing organ-on-a-chip technology and exploring how to apply quantum computing mechanisms to foster better and more contemporary science. In this sense, Dr Akhtar thinks these technologies can help advance cancer research, proclaiming that “It is going to be one of the major technologies that are going to replace animal testing for drug development, but also for disease modelling.” And she is not wrong. Dr Dipesh
Niraula[11] , an applied research scientist at Florida’s Moffitt Cancer Center[12] , is working
in the radiotherapy field and developed a quantum deep reinforcement learning (qDRL[13]
) framework to support clinical
decisions in radiotherapy treatments. He explains the importance of this framework by making a parallelism with the decision process of buying a shirt available in different colours: “Before a shirt is purchased, shirt options are like superimposed quantum states. We wouldn’t know their decision until they pick a shirt.” He moves this analogy to clinical decisions and says: “When doctors don’t have complete information on the patient’s state, disease progression, treatment response, and so on, the clinical decision will be based on the physician’s professional experience leading to inter-physician variability. And quantum computing helps in modelling such intrinsic uncertainty in human decision-making.” But the potential of quantum computing
for cancer does not end there. Scientists are exploring the potential of quantum machine learning from different angles. Quantum transfer learning could help in histopathological cancer detection, while quantum convolutional neural networks could assist in breast cancer detection and brain tumour screening. Moreover, the utility of quantum
computing extends beyond the applied field. Theoretical approaches are trying to reach a better understanding of cancer by leveraging the potential of quantum computing wave functions to model the quantum mechanisms of genetic mutations involved in cancer development.
Advancing genetics and genomics Many diseases have a genetic cause, but the genome size makes it hard to
8 Scientific Computing World Summer 2023
‘It is going to be one of the major technologies to replace animal testing for drug development [and] disease modelling’
Dr Aysha Akhtar, Founder and CEO of the Center for Contemporary Sciences
understand some mechanisms with current technology. DNA and RNA sequencing, analysing, and assembling are essential to understand such diseases and, at the same time, are potential candidates to leverage the advantages of quantum computing. This is the case with phylogenetic trees, fundamental tools for understanding the evolution of certain organisms. Here, some scientists are exploring reconstruction via graph cutting using quantum annealing. Another crucial tool where quantum annealing is playing a key role is the de novo assembly, where the overlapping of DNA sequences benefits from the annealing’s optimisation process. But the quantum computing potential in genetics and genomics is even more promising. Quantum gene regulatory networks, for example, can aid in diagnoses
detection, dementia prediction and diabetic retinopathy classification. Dr Joseph Davids[14]
, a clinical
research fellow at Imperial College London, specialising in nanomedicine and neurosurgery, thinks that quantum computing could be the future tool to help in medical diagnosis: “Quantum computing will be responsible for not just diagnostics but treatments too.” He also notes[15] the importance of initiatives such as the National Quantum Computing Centre in Oxfordshire, which is aimed at accelerating quantum computing development in the UK and fostering patient-tailored therapeutics and diagnostics.
Quantum computing: the future of biomedical sciences Many are the advancements of this emerging technology in the biosciences field. In just a few years, scientists have succeeded in exploring the quantum computing potential not only in a theoretical stage but also developing practical applications. Quantum computing is already underway and carving out a promising path. Dr Akhtar thinks switching to these
and developing targeted treatments, while quantum comparison algorithms could detect DNA and RNA mutations to advance disease diagnosis and understanding. The potential of quantum computing
to predict and identify diseases under uncertainties is crucial. As Dr Niraula notes, “Human decision-making process in the face of uncertainty gets unpredictable”, and as he previously said, such uncertainties are like quantum states, which can be modelled with the help of quantum computing. Scientists are using quantum computing
to solve such uncertainties in many ways. Quantum neural networks, for example, are being explored for applications as different as electroencephalographic signals classification and personalised treatment for osteoarthritis, while quantum machine learning and quantum deep learning are being studied for heart failure
emerging technologies, may be expensive initially but asserts that over time “you don’t have to worry about feeding computer chips”. Excited by the conception of combining quantum computing with biological sciences, she pictures a future where “biological models like body-on-a-chip technology will be connected with computing models, where quantum computing will probably increasingly play a role because it’s going to take a combination of these different techniques to give the best-combined understanding of human biology and human diseases.” Emphasising the existence of “lots of data that computing technology can screen to combine the information with biological models like organ-on-a-chip technology”, she says, “This is going to be the future of biomedical science.” SCW
References: [1]
[2] [3] [4] [5] [6] [7] [8] [9]
https://en.wikipedia.org/wiki/Luis_Falc%C3%B3n www.gnusolidario.org/ https://wyss.harvard.edu/
https://contemporarysciences.org/ https://polarisqb.com/
www.linkedin.com/in/anna-b-petroff/
www.linkedin.com/in/santiago-vilar-9423b8a/
www.linkedin.com/in/ayshaakhtar
[10] [11] [12] [13] [14] [15]
www.linkedin.com/in/teppei-suzuki-3b312634/
www.linkedin.com/in/shahar-keinan-9b729b1/
www.linkedin.com/in/dipeshniraula/
www.moffitt.org/
https://doi.org/10.1038/s41598-021-02910-y www.linkedin.com/in/joseph-davids/
https://doi.org/10.1007/w978-3-030-64573-1_338
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