After many years of tantalizing but unfulfilled promise, quantum computing’s potential – in healthcare and beyond – is heating up in a big way. This past year, a cloud-accessible quantum computer with 400-plus qubits was shown to be achievable, a power four times that imagined until just recently.
Now, with leading-edge health systems such as the Cleveland Clinic already deploying quantum systems to advance the speed and precision of their clinical and life sciences research initiatives, it’s time to plan for the real-world opportunities and significant challenges – data security not least among them – of this massively powerful new approach to computing.
At HIMSS23 next week, Frederik Flöther, lead quantum and deputy CEO at QuantumBasel, uptownBasel Infinity Corp, will offer a presentation describing the near-term prospects for quantum computing in healthcare and medicine.
Flöther has authored more than 40 peer-reviewed publications and white papers. Some recent titles include How can quantum technologies be applied in healthcare, medicine, and the life sciences? and The state of quantum computing applications in health and medicine.
At HIMSS23, speaking alongside Numan Laanait, senior director of engineering at Elevance Health, he’ll discuss how quantum is already changing how machine learning is being applied to healthcare data, with real-world case studies showing how it’s enabling new innovations across a variety of use cases.
We asked him about the differences between quantum and classical computing and the steps healthcare organizations need to take to prepare for the future.
Q. What is quantum computing, and how is it different from the 1s and 0s of standard computing?
A. Quantum computing is a fundamentally different form of information processing, entailing novel hardware, software, and applications. It leverages the principles of quantum mechanics and is the only known technology that can be exponentially faster than classical computing. However, quantum computers are not silver bullets and will not speed up every single calculation – they are only beneficial for certain kinds of problems. These quantum algorithm applications are often grouped into three categories:
Simulating nature – e.g. chemistry and physics
Processing data with complex structure – e.g. machine learning
Search and optimization
Thus, quantum computing represents an entirely novel way of approaching and even thinking about problems. Furthermore, quantum mechanics also enables other emerging technologies (such as quantum communication/security as well as quantum sensing) and has deep philosophical and ethical implications about the way we see the world.
Q. How close is it to being more widely deployed in healthcare, and what are its most promising use cases?
A. We do not yet have quantum solutions in production. There is much debate regarding the timeline, which is also highly use case-dependent; some see such solutions appearing within a year, others believe we are still a decade away. Nevertheless, a consensus has started to form that it is now just a matter of time.
A broad range of quantum computing use cases has already been explored in health and medicine. In the last few years, more than 40 proof-of-concept studies have appeared. The use cases include, for example, genomic sequence analysis, virtual screening in drug discovery, medical image classification, disease risk prediction, and adaptive radiotherapy.
Q. What could quantum computing mean for developing machine learning models?
A. Quantum computing may have a range of benefits. Speed is frequently cited as a key benefit. Still, there are other advantages that quantum computers can enable. These include, for instance, achieving better accuracies, requiring less training data, handling noisy data, dealing with high-dimensional data (many variables but relatively few samples), and enabling higher energy efficiencies.
Over the last years, machine learning has emerged as one of the most promising quantum computing use case areas. In fact, a symbiosis is forming. Quantum computing has shown promise in making the training of machine learning models more efficient and enabling higher accuracies. Conversely, classical machine learning has been applied to improve quantum computing methods.
Q. What are some of the cybersecurity implications of quantum computing?
A. Some quantum algorithms, specifically Shor’s algorithm (and to a lesser extent Grover’s algorithm), are able to provide significant speedups for solving mathematical problems that are central to current cryptographic methods. As a result, once quantum hardware and software improve to the point where these algorithms can be run for larger-size problems, many of the currently employed cryptographic protocols are rendered ineffective.
Moreover, the future confidentiality of today’s data is already threatened through “harvest now, decrypt later” attacks. Hence, it is imperative that organizations, particularly those dealing with sensitive data that need to be kept secure for a long time (as is common in the medical space), start developing roadmaps for the transition to quantum-safe cryptographic standards.
Q. What should health system IT leaders be doing now to prepare for a quantum future?
A. The following represent key steps that every health system leader should consider:
Engage in quantum consortia and ecosystems and collaborate with partners
Develop and enable a core team of quantum champions
Explore and prioritize quantum computing use cases
Implement proof-of-concept quantum computing applications
Develop a roadmap for transitioning to quantum-safe standards and begin enhancing crypto-agility.