Siemens Healthineers: Pioneering Healthcare with Quantum Computing
“I wanted to work on cutting edge research that could lead to benefits for patients” says Peter Mountney, a Senior Key Expert Scientist at Siemens Healthineers.
That was the key driver for joining Siemens Healthineers after a PhD and post doc work in machine learning-driven medical imaging and surgical robotics.
6 years ago, while working in the Siemens Healthineers’ Digital Technology & Innovation Center in Princeton, USA, quantum computing first came across Dr Mountney’s radar. The Center runs technology scouting activities to identify new and disruptive technologies. At that time, DWave, were making headlines with their annealing technology. But it was still too early for active engagement in quantum computing.
However, with the passage of a couple of years, Dr Mountney started thinking seriously about quantum computing and its potential to disrupt healthcare. The starting point was to understand what types and sizes of problems might be solvable if applying this technology.
“Initially, we identified a project and collaborated with University College London supported by InnovateUK and the UK National Quantum Technologies Programme. The project had key questions: 1) what could we do with today’s technology; and 2) what could we do with hardware that may be available in the future? We wanted to explore using the DWave machine, and concurrently have a more theoretical exploration of what is possible with gate based quantum computing. Funnily enough, between the conception of the project and its actual start, some of the theoretical work became practical to run on IBM and Rigetti chips with small experiments. That’s how fast the industry is moving!”
“We didn’t have one problem we were looking to solve. We were trying to broadly understand what quantum computing could be good for, and then apply it to healthcare. Given the nature of our data, it is enormous and multi dimensional, one initial problem we needed to solve was how to get the data onto the quantum machine in the first place? We came up with a classical/quantum system where we would use machine learning to extract the meaningful data, put it on a quantum computer."
Given the many applications of Artificial Intelligence and machine learning in healthcare Dr Mountney’s team has great interest in quantum machine learning. They have developed a systems for classifying medical images using neural networks that use a quantum pre-trained Restricted Boltzmann Machines and used the IBM machine to implement a simple kernel based classifier to differentiate healthy patients from dementia patients using simple feature sets extracted from MRI scans. One exciting direction is using quantum computers to train binary neural networks “we no longer need to use back propagation, instead we can search for the optimal combination of network weights and the optimal network architecture”.
In addition, the team has used NISQ algorithms to segment anatomy in medical images. Identifying cardiac vessels and contouring them in x-ray images is one of the steps in modelling how the patient’s blood is moving through the body which in turn indicates how healthy the patient is.
“We’ve taken our problems, made small versions of them and tried to find quantum solutions to fit those small problems. We aren’t at prime time yet, but we are finding applications within the business that could benefit from quantum solutions,” explains Dr Mountney.
What about the current shortfalls in quantum computing hardware?
“It is much easier to gain buy-in if we can create practical demonstrations. Showing someone a practical example always beats a theoretical explanation. Even if we aren’t running these applications on a ‘real’ quantum computer, we would rather run on a simulator and have some real results on a small problem. It’s much more compelling.”
Concepts and information presented are based on research and are not commercially available. Due to regulatory reasons, the future availability cannot be guaranteed.
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To visit the Siemens Healthineers website: https://www.siemens-healthineers.com/en-ie/