Quantum algorithms for health

Quantum algorithms for health

The field of computational life sciences can and should invest today in the development of quantum algorithms to take advantage of the short-term improvements and long-term transformative potential of quantum technology.

Advances in technology have always led to breakthroughs in medicine. Robert Hooke’s detailed drawings of cells relied on his compound microscope, and the development of the COVID-19 vaccine relied on computer-powered genetic research. The arrival of quantum technology will likely lead to another significant revolution in medical science, impacting the art of the possible in life sciences and biological research.

Quantum computing is one of many subfields of quantum information science (QIS), which take advantage of quantum mechanics to enhance existing technologies and enable new ones. In particular, quantum computers should make it possible to model certain large complex systems more accurately, more efficiently or, in some cases, more quickly. They show great promise as optimization tools or as engines for simulating very small things such as the interactions between individual amino acids. For health, life and medical sciences to be among the first areas to benefit from the coming advances in paradigm-shifting technology, experts in these fields – biologists, chemists, vaccinologists, etc. – must start today in prototypes of quantum applications.

A promising technology, still in its infancy

To realize the full potential of quantum computing, its hardware must be improved and scaled. In short, the quantum hardware available today consists of prototypes with less than 1000 quantum bits or qubits, the analog of the bits used in non-quantum (eg, classical) computers. Tech giants, small startups, government labs, and universities are now exploring how to make better hardware. Some research groups predict that they will exceed one million qubits as early as 2030. A quantum computer of this size will enable what is called “large-scale” quantum computing.

Since quantum computing is based on different mathematical principles than classical computers – probabilistic as opposed to deterministic – new software must also be created to manage and use it. Fortunately, quantum software and algorithms can be designed and even tested without the need for mature quantum hardware. In some cases, this is accomplished through rigorous mathematical proofs. In others, the algorithm can be tested on prototype hardware available today, or run on a simulation of a quantum computer on a classical machine. Many organizations, including Oak Ridge National Labs, have devoted time and resources to creating versatile software that can scale alongside hardware.

Reinventing the future workforce

Hardware and software, however, are only part of what is needed to develop custom-designed quantum computers to solve our world’s most pressing problems. QIS researchers often lack the domain knowledge needed to apply quantum computing to other fields. For health sciences to benefit from what will be available in 2030, biologists, geneticists, vaccinologists and other medical researchers must ensure that their knowledge is shared with teams of quantum experts. The best quantum computer in the world will not be able to help a biologist design the next mRNA vaccine if a biologist is not familiar with quantum algorithms; this general lack of exposure to quantum computing is perhaps one of the biggest obstacles facing QIST as a whole, but it is one that can be overcome with interdisciplinary teams.

How to get started with Quantum for Health

It can be hard to imagine how advances in medicine or the life sciences will be fueled by prototype quantum machines. However, many quantum algorithms currently under development are hybrid in nature. That is to say, these algorithms rely on both “classical” computers and today’s quantum computers, which makes it possible to exploit the prototype of quantum hardware. Even in cases where the hybrid algorithm does not work as expected, the process of attempting to create a quantum algorithm sometimes leads to the discovery of a new classical algorithm, dubbed “quantum-inspired”, which is better than the original method.

Hybrid quantum and quantum-inspired algorithms are natural steps in the evolution towards purely quantum methods. However, those who invent these new algorithms often lack knowledge in other areas, making it difficult to translate incremental algorithmic research into ready-to-use software. The importance of this intersection has not gone unnoticed; for example, the UK spent $8.4 billion in 2021 on experimenting with the “Quantum Enhanced Computing Platform for Pharmaceutical R&D” and several pharmaceutical companies have all partnered with quantum ventures to explore quantum applications in drug discovery.

The robust quantum hardware that will allow the full potential of quantum computing to be realized could be a decade away. However, we can begin to harness the benefits of the quantum revolution today by leveraging quantum algorithms in hybrid computing systems that leverage quantum and classical computers side-by-side. The true success of these hybrid systems will also require hybrid teams of quantum data scientists and life scientists to apply these systems to biological research. This will not only determine which areas are ripe for further exploration when more quantum hardware becomes available, but will also help train health research personnel in quantum computing. This integration of computational methods and transdisciplinary teams will accelerate the application of quantum computing to life science research and lay the foundation for the quantum revolution to come.

About Kevin Vigilante, Medical Director and Executive Vice President

Dr. Kevin Vigilante is a leader in Booz Allen’s healthcare industry, advising government healthcare clients in the Departments of Health and Human Services, Veterans Affairs, and the military healthcare system. He currently leads a portfolio of works at the Department of Veterans Affairs. Kevin is a physician who brings new ideas to health system planning and operational efficiency, biomedical informatics, life sciences and research management, public health, program evaluation and preparation. His work is published in leading academic journals and media, including The New York Times, on a wide range of topics, including innovation in research and computing, tax policy, and health care reform. , and care for populations underserved by HIV.

About Isabella Martinez, Principal Quantum Technologist

Isabella Bello Martinez is a quantum technologist at Booz Allen, specializing in strategic thinking for long-term quantum growth strategies and research into the applications of quantum technologies. She leads Booz Allen’s quantum team’s external outreach and delivery of analytical products for a variety of clients. Isabella helps clients imagine the impact of emerging technologies on their business, then helps them create the teams, policies and practices needed to make that vision a reality. An engineer by training, Isabella received her ScB from Brown University and her Masters from the University of Notre Dame.

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