Quantum Algorithms for Health Care – Digital Health

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

Advances in technology have always fueled advances in medicine. Robert Hooke’s detailed drawings of cells were based on his compound microscope, and the development of the COVID-19 vaccine depended on computer-driven genetic research. The advent of quantum technology will likely bring another significant revolution in medical science, impacting the art of the possible in life sciences and biological research.

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

A promising technology, still in its infancy

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

Since quantum computing is based on different mathematical principles than classical computers,probabilistic vs. 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 currently available prototype hardware or run on a simulation of a quantum computer on a classical machine. many organizationsincluded Oak Ridge National Laboratoriesthey have spent time and resources creating versatile software that can evolve along with the hardware.

Reimagining the workforce of the future

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

How to get started with Quantum for Health

It can be hard to imagine how advances in medicine or life sciences will be driven by prototype quantum machines. However, many quantum algorithms currently under development are hybrid in nature. That is, these algorithms are based both on current “classical” computers and on quantum computers, which makes it possible to take advantage of the quantum hardware prototype. Even in cases where the hybrid algorithm does not work as expected, the process of trying to create a quantum algorithm sometimes leads to the discovery of a new classical algorithm, called “quantum-inspired”, that 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 are inventing these new algorithms often lack domain knowledge in other fields, making it difficult to translate incremental algorithmic research into ready-to-use software. The importance of this crossing has not gone unnoticed; for example, the UK dedicated $8.4 billion in 2021 to experimentation in “Enhanced Quantum Computing Platform for Pharmaceutical Research and Development” and various pharmaceutical companies all have partnered with quantum companies to explore quantum applications in drug discovery.

Robust quantum hardware that will enable the full potential of quantum computing to be realized could be a decade away. However, we can start reaping the benefits of the quantum revolution today by leveraging quantum algorithms in hybrid computing systems that leverage quantum and classical computers in parallel. 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 areas that are ripe for further exploration when more quantum hardware becomes available, but will also help train the health research workforce 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 coming quantum revolution.

About Kevin Vigilante, Chief Medical Officer and Executive Vice President

Dr. Kevin Vigilante is a leader in Booz Allen’s healthcare business, advising government healthcare clients in the Departments of Health and Human Services, Veterans Affairs and the Military Health System. He currently runs a portfolio at the Department of Veterans Affairs. Kevin is a physician providing new insights for healthcare system planning and operational efficiency, biomedical informatics, life sciences and research management, public health, program evaluation, and preparedness. His work is published in leading academic journals and media outlets, including the New York Times, on a wide range of topics, including research and informatics innovation, fiscal policy and health care reform, and health care. of underserved populations with HIV.

About Isabella Martinez, Lead 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 applications of quantum technologies. She leads the external outreach of Booz Allen’s quantum team and the delivery of analytical products for a variety of clients. Isabella helps clients envision how emerging technologies will affect their businesses, and then helps them create the teams, policies, and practices to bring that vision to life. Isabella, an engineer by training, earned her ScB from Brown University and her MS from the University of Notre Dame.

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