Quantum Algorithms for Cognitive Healthcare
Lead Research Organisation:
University College London
Department Name: Computer Science
Abstract
The healthcare industry is experiencing an explosion of data and at the same time the cost of healthcare is rising. Cognitive computing in healthcare is using big data in conjunction with advanced machine learning and supercomputers/cloud services to help doctors detect diseases earlier, improve therapeutic outcomes and ultimately reduce the cost of care. In this feasibility study we will investigate the use of quantum machine learning algorithms to improve cognitive computing in healthcare. Algorithms running on quantum processors have the potential to perform extremely fast calculations to solve problems that are computationally intractable with classical computers. This offer significant potential when extracting meaningful, decision level information from medical images.
Planned Impact
Social: Improved patient outcomes. One of the fundamental goals of cognitive computing in healthcare is improving patient's response to therapy and outcomes. Quantum algorithms will play an important role in saving lives and improving the quality of life of millions of people.
Economic: Current projections for healthcare spending are unsustainable. The goal of this project is to identify technologies with commercial potential to drive down the cost of healthcare. This includes reducing procedure times, enabling clinical staff to perform their jobs faster and improving the efficacy of procedures. Furthermore, improved patient outcome generally lead to patients returning to the workforce sooner and reducing the economic impact of ill health.
Environmental: 1) quantum computers have the potential to outperform classical computers with significantly reduce power consumption. Additionally, the power needed to operate these systems stays roughly the same as the number of qubits and processing power increases. Reducing power consumption is critical in creating sustainable growth that protects the environment. 2) Minimizing the environmental impact of this project: minimize travel where possible by using tele/video conferencing.
Economic: Current projections for healthcare spending are unsustainable. The goal of this project is to identify technologies with commercial potential to drive down the cost of healthcare. This includes reducing procedure times, enabling clinical staff to perform their jobs faster and improving the efficacy of procedures. Furthermore, improved patient outcome generally lead to patients returning to the workforce sooner and reducing the economic impact of ill health.
Environmental: 1) quantum computers have the potential to outperform classical computers with significantly reduce power consumption. Additionally, the power needed to operate these systems stays roughly the same as the number of qubits and processing power increases. Reducing power consumption is critical in creating sustainable growth that protects the environment. 2) Minimizing the environmental impact of this project: minimize travel where possible by using tele/video conferencing.
Organisations
Publications
Dervovic Danial
(2018)
Quantum linear systems algorithms: a primer
in arXiv e-prints
Piat S
(2018)
Image classification with quantum pre-training and auto-encoders
in International Journal of Quantum Information
| Description | This research identified areas for development of quantum algorithms for healthcare. Applications in the area of medical imaging were identified. Prototype algorithms were implemented on both quantum computers and simulators. Siemens submitted 4 patents based on this research. |
| Exploitation Route | These finding may form the basis for other wishing to develop quantum algorithms for healthcare particularly in the imaging domain. |
| Sectors | Digital/Communication/Information Technologies (including Software),Healthcare |
| Description | Hi, A patent has been awarded based on this work. |
| Sector | Healthcare |
| Impact Types | Societal |
| Title | DATA ENCODING AND CLASSIFICATION |
| Description | In a method and apparatus for training a computer system for use in classification of an image by processing image data representing the image, image data are compressed and then loaded into a programmable quantum annealing device that includes a Restricted Boltzmann Machine. The Restricted Boltzmann Machine is trained to act as a classifier of image data, thereby providing a trained Restricted Boltzmann Machine; and, the trained Restricted Boltzmann Machine is used to initialize a neural network for image classification thereby providing a trained computer system for use in classification of an image. |
| IP Reference | US2020005154 |
| Protection | Patent granted |
| Year Protection Granted | 2020 |
| Licensed | No |
| Impact | So far no notable impacts from patent |