o address the limitations in technological development and adoption of quantum-enabled technologies to solve translational biomedical problems, the National Center for Advancing Translational Sciences (NCATS), part of the National Institutes of Health (NIH), is announcing the NIH Quantum Computing Challenge, one of two parallel Prize Challenges for the NIH Quantum Biomedical Innovations and Technologies (Qu-BIT) Program, by inviting innovative design solutions to identify and propose novel applications of existing quantum computing approaches to apply toward use cases within clinical, translational, and biomedical problem areas. The three areas of interest for the current Challenge include:
1) Quantum Algorithms for Drug Discovery,
2) Quantum Algorithms for Clinical Risk Predictions, Diagnosis, and Therapeutics,
3) Quantum Algorithms for Biomedical Imaging and Genomic Data Analysis.
The Challenge will have two stages: Ideation and Planning followed by Quantum Algorithm Development, and Implementation on Quantum Hardware.
Stage 1: Ideation and Planning
In Stage 1, the focus is development of an algorithm of their interest by describing the problem and then designing the overall structure and function of the algorithm to address the stated problem as well as include all the relevant expertise needed to move to Stage 2 for algorithm development and testing biomedical use cases. Innovators are expected to clearly state how and why the proposed solution would provide significant advances over currently available tools.
Stage 2: Quantum Algorithm Development, and Implementation on Quantum Hardware
In Stage 2, there are two (2) Milestones:
Milestone 1: Innovators will perform algorithm testing, optimization, and algorithm verification on quantum simulators using datasets relevant to biomedical challenges mentioned above.
Milestone 2: Innovators will demonstrate deployment of their algorithms for the proposed uses cases on quantum hardware or quantum-classical hybrid system.
Background:
The recent advancements in quantum information sciences and engineering through the US National Quantum Initiative and international efforts have led to the development of second-generation quantum technologies (e.g., sensing, computing, networking, and communications) that harness the power of quantum physics and engineered quantum states that enable new modalities that provide disruptive capabilities in sensing and detecting and biological entities, as well as new computational capabilities. Of these, quantum computing is a rapidly emerging area offering new computational abilities for addressing certain complex biomedical applications.
Quantum computations are fundamentally different from classical computations, in that classical computations are limited by binary states, whereas quantum computations rely on multiple quantum states resulting in exponentially higher computing speeds. Using mathematical formulations of quantum mechanics such as entanglement and superposition, new quantum algorithms for improved speed and accuracy of current computations (e.g., simulation, optimization, and machine learning) are emerging rapidly. The rapidly growing quantum computing and quantum/classical algorithmic approaches have near-term transformative potential for certain biomedical use cases including molecular simulations, protein and DNA/RNA folding, drug discovery, medical image-based classification and diagnosis, biological sequence analysis, forecasting treatment effectiveness, etc. These items were highlighted as of interest in the recent roundtable between the NIH and Department of Energy (https://www.osti.gov/biblio/2228574).
Objectives of the Challenge:
The NIH Quantum Computing Challenge seeks to adopt, optimize, and deploy existing quantum algorithms or develop new quantum algorithms for biomedical problems, leading to a set of transformative solutions. Proposed quantum computing approaches are expected to provide a rigorous path towards quantum utility in biomedical fields by identifying specific biomedical problems amenable to quantum solutions. Compared to current methods, the proposed quantum approaches are expected to be superior (e.g., improved speed, accuracy, efficiency via exploitation of quantum mechanical phenomena) in solving specific computational problems and/or enhance the current computational workflows.
This Challenge aims to catalyze the identification of novel biomedical use cases that are amenable to quantum computing-based solutions, including quantum-classical hybrid solutions. By combining expertise and advancements made across different disciplines such as engineering, material sciences, biomedical sciences, and computational sciences we hope to uncover new cross-cutting technologies in this field. The three areas of interest for the current Challenge include: 1) Quantum Algorithms for Drug Discovery, 2) Quantum Algorithms for Clinical Risk Predictions, Diagnosis, and Therapeutics, 3) Quantum Algorithms for Biomedical Imaging and Genomic Data Analysis.
Specific examples of areas of interest include but are not limited to:
1. Quantum Algorithms for Drug Discovery:
- Chemical simulations, optimization methods, and quantum machine learning algorithms for drug discovery and design (e.g., predicting novel chemical structures with associated biological activities and reactive intermediates)
- Molecular simulations, optimization methods, and quantum machine learning algorithms for predicting or quantifying interactions between drug candidates and relevant biological targets.
- Quantum machine learning algorithms to enhance current computational analysis workflows for molecular design and drug discovery, high throughput screening and image processing.
- Quantum approaches for generation of libraries of synthetic data pertinent to drug discovery.
2. Quantum Algorithms for Clinical Risk Predictions, Diagnosis, and Therapeutics:
- Quantum algorithms for optimized feature selection to improve/accelerate current computational approaches or develop new approaches and develop quantum machine learning algorithms to generate predictive models.
- Example use cases include the following 1) for diagnostics where the training datasets are small and/or sparse, such as for rare diseases, 2) to identify people at risk for developing disease and/or predicting disease trajectory, disease patterns, 3) to inform selection of appropriate therapeutic strategies for patients including patient stratification, 4) clinical trials optimization.
3. Quantum Algorithms for Biomedical Imaging and Genomic Data Analysis:
- Quantum algorithms for enhanced image processing, image segmentation, reconstruction, registration, and classification of biomedical imaging data (e.g., MRI, CT scans).
- Quantum algorithms for genomic data analysis to speedup sequence searching, matching and alignment, and other multi-omic data analyses.
Expected technological outputs include and but not limited to the following: Quantum algorithms and quantum-classical hybrid approaches/workflows that are superior compared to the current state-of-the-art methods in solving for biomedical problems (e.g., improved accuracy, speed, scale, improved predictions, reducing energy use). These could be quantum algorithms for simulations, combinatorial optimizations, and quantum machine learning approaches (e.g., including classifiers, neural networks, kernel methods, approaches for data preprocessing and feature selection). The proposed use cases are expected to be tested/addressed using currently available NISQ QPUs and quantum simulators (realistic simulators of quantum hardware and/or classical supercomputing processors) to develop a path for demonstrating the use of quantum computing for improving current computational approaches and workflows.
Partners: NIH Office Data Science and Strategy (ODSS), Center for Information Technology (CIT), National Eye Institute (NEI)
NCATS reserves the right to cancel, suspend, and/or modify the Challenge, or any part of it, for any reason, at the NCATS’ sole discretion.
Awards:- $1,300,000
Deadline:- 22-02-2025
