Cleveland Clinic lead: Dan Blankenberg, PhD - IBM lead: Borja Peropadre, PhD
Classical computers, like the one you are using to read this text, rely on informational units known as bits. Bits can be in only one of two states: 0 or 1, also known as off/on or false/true. Conversely, quantum computers make use of quantum bits, or qubits. A qubit has the unique ability to place information into a state of superposition, a fundamental principle of quantum mechanics that allows the qubit to exist in both states.
Using the phenomena of quantum physics, new computing machines — like Cleveland Clinic’s Quantum System One — have been built to process information in new ways. This will lead to promising breakthroughs in a wide range of fields, including physics, chemistry, biology, machine learning, optimization, complex systems and risk analysis, anomaly detection and more.
DARPA Quantum Benchmarking Proposal
CCF Lead: Daniel Blankenberg
This is a federal award from the United States Defense Advanced Research Projects Agency (DARPA) to develop a quantum computing benchmarking framework for quantum computing architectures that can overcome any failures in hardware or software. It will lead to the development and analysis of various computational analysis problems to test and evaluate scenarios relevant to a variety of fields — including genomics, computational biology, protein conformational analysis, protein-drug interaction, radiation therapy planning, healthcare process optimization, computational biology and computational chemistry.
Investigating Quantum Kernels with EHR Data
CCF Lead: Ming Chung
IBM Lead: Omar Shehab
This project will explore the benefits of quantum computing to study the risk factors for cardiovascular complications following non-cardiac surgery. Major adverse cardiac events are a leading cause of non-cardiac morbidity and mortality around the time of surgery. By applying quantum machine learning techniques, we can enhance our understanding of the risk factors, improve patient outcomes and potentially reduce healthcare costs and utilization by facilitating clinical decision-making. The results of this project can also help determine if quantum computing-based machine learning algorithms can improve patient outcomes in other clinical settings.
Research Collaboration on Quantum Chemistry for Drug Discovery
CCF Lead: Jun Qin
IBM Lead: Gavin Jones
Cleveland Clinic and IBM will develop a quantum computing method to screen and optimize drugs targeted to specific proteins. This joint research project will utilize quantum computing and AI tools in a hybrid quantum/classical computing approach to investigate protein and drug interactions. The results of this project will help to advance our knowledge of drug development and potentially lead to creating more effective drugs for preventing and treating disease.
Quantum Algorithms for Feature Identification and Feature Selection
CCF Lead: Xiaojuan Li
IBM Lead: Omar Shehab
This project aims to leverage the power of quantum computing algorithms to optimize feature identification and feature selection in healthcare and life sciences (HCLS) data sets. Cleveland Clinic and IBM will identify optimal ways to represent HCLS datasets using fewer features. This will help to represent noisy datasets that are common bottlenecks for performance. By creating a better understanding of this problem in the context of quantum computing, we will help guide future research efforts, as well as hardware and software development. A deeper understanding of this field will also help with the improvement of classical AI algorithms.
Quantum Simulations of Biochemical Reactions
CCF Leads: Timothy Chan & Charis Eng
IBM Lead: Gavin Jones
This project aims to perform quantum simulations to study chemical reactions in important biological processes. Through simulations, we may develop a deeper understanding of biomedical reactions that can lead to human disease. Currently, performing these types of simulations is quite limited by existing computing technology. Cleveland Clinic and IBM plan to address this by developing new quantum simulation algorithms as the first of their kind that will be demonstrated on a quantum computer. One of the most significant benefits of this work is that it will allow researchers to stop relying on low-accuracy methods and instead make use of more precise quantum simulations that can potentially lead to new therapies.