The field of computational life sciences can and should invest in the development of quantum algorithms today to take advantage of the near-term improvements and long-term transformational potential of quantum technology.
Advancements in technology have always driven breakthroughs in medicine. Robert Hooke’s detailed drawings of cells relied on his compound microscope, and the development of the COVID-19 vaccine hinged on genetic research powered by computers. The arrival of quantum technology will likely bring about another significant revolution in medical sciences, impacting the art of the possible in life sciences and biological research.
Quantum computing is one of several sub-fields of quantum information sciences (QIS), which take advantage of quantum mechanics to improve existing and enable new, technologies. In particular, quantum computers are expected to allow certain large, complex systems to be modeled more accurately, more efficiently, or in some cases more quickly. They show great promise as tools for optimization or as engines for simulating very small things such as interactions between individual amino acids. For health, life, and medical 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 prototype 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. Succinctly put, quantum hardware available today consists of prototypes with less than 1000 quantum bits or qubits, the analog of the bits used in non-quantum (e.g., classical) computers. Tech giants, small startups, government labs, and universities alike are currently exploring how to make better hardware. Some research groups predict that they will surpass one million qubits as early as 2030. A quantum computer of that size will allow for what is known as “full-scale” quantum computing.
Since quantum computing is grounded in different mathematical principles than classical computers—probabilistic versus 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 the 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 which can evolve alongside the hardware.
Reimagining the Future Workforce
Hardware and software, however, are only part of what is needed to develop quantum computers custom-designed to solve our world’s most pressing problems. Researchers devoted to QIS often lack the domain knowledge that is necessary to apply quantum computation 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 barriers facing QIST as a whole, but it is one which can be solved with interdisciplinary teams.
How to Get Started with Quantum for Health
It can be difficult to envision how advancements in medicine or life sciences will be powered by prototype quantum machines. However, many quantum algorithms currently under development are hybrid in nature. That is, these algorithms rely on both today’s “classical” computers and quantum computers, allowing the prototype quantum hardware to be leveraged. Even in cases where the hybrid algorithm does not perform 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 both natural steps in the evolution toward 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 intersection has not gone unnoticed; for example, the United Kingdom devoted $8.4 billion in 2021 for experimentation in “Quantum Enhanced Computing Platform for Pharmaceutical R&D” and several pharmaceutical companies have all partnered with quantum businesses to explore quantum applications in drug discovery.
Robust quantum hardware that will enable the full realization of quantum computing potential could be a decade away. However, we can start to mine 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 areas that are ripe for further exploration when further quantum hardware is available, but will also help train the health research workforce in quantum computing. This integration of computing methods and transdisciplinary teams will accelerate the application of quantum computing to life science research and lay the foundation for the quantum revolution that is coming.
About Kevin Vigilante, Chief Medical Officer and EVP
Dr. Kevin Vigilante is a leader in Booz Allen’s health business, advising government healthcare clients at the Departments of Health and Human Services, Veterans Affairs, and the Military Health System. He currently leads a portfolio of work at the Department of Veteran’s Affairs. Kevin is a physician who offers new ideas for health system planning and operational efficiency, biomedical informatics, life sciences and research management, public health, program evaluation, and preparedness. His work is published in academic journals and top-tier media outlets including the New York Times on a broad range of topics, including research innovation and informatics, tax policy and healthcare reform, and care of underserved HIV populations.
About Isabella Martinez, Lead Quantum Technologist
Isabella Bello Martinez is a Quantum Technologist at Booz Allen who specializes in strategic thinking for long-term quantum growth strategies and quantum technologies application research. She leads external outreach for Booz Allen’s quantum team and the delivery of analytical products for a variety of clients. Isabella helps clients imagine how emerging technologies will impact their businesses, and then helps them create the teams, policies, and practices to make that vision a reality. An engineer by training, Isabella earned her ScB from Brown University and her MS from the University of Notre Dame.