NIH offers $7.1M for quantum tools to predict drug safety

The National Institutes of Health is offering $7.1 million through the QuSAFE challenge, a prize competition designed to spur innovation in drug safety testing beyond traditional methods. Unlike standard grants, this structure aims to attract a wider range of participants to develop quantum-powered tools for use with in vitro New Approach Methodologies, such as organ-on-a-chip platforms. The challenge seeks proof-of-concept systems capable of detecting biological signals currently beyond reach, improving preclinical models for drug development; NIH partners with NASA, NSF, and other institutes to achieve this goal. Resources for participants are available at https://www.nih.gov/challenges/qusafe.

QuSAFE Prize Challenge: $7.1M for Quantum Drug Safety Tools

The QuSAFE challenge will award up to $840,000 to a single grand prize winner, as part of a total $7.1 million allocated to incentivize development of quantum-powered drug safety tools. Unlike traditional grant funding, the competition structure is designed to attract a broader range of participants, including those outside of conventional research institutions. Stage 1 of the challenge offers $500,000 distributed among up to 25 winners, each receiving $20,000 for ideation and planning; subsequent stages increase funding substantially for prototype development and refinement.

NASA is partnering with the National Institutes of Health, the National Science Foundation, and other institutes on QuSAFE, demonstrating a cross-agency interest in applying quantum technology to biological and medical problems. This collaboration signals a widening scope for quantum sensing beyond its traditional applications in physics and computing.

The challenge specifically targets enhancement of in vitro New Approach Methodologies, or NAMs, platforms like organ-on-a-chip, with the goal of capturing biological signals currently undetectable by existing methods. According to information released by the NIH, “QuSAFE wants quantum sensing tools that help these platforms detect biological signals current tools can’t catch.” Researchers can enter the competition at Stage 1 with only a conceptual proposal, or bypass it and enter Stage 2 with existing hardware, offering flexibility for teams at various stages of development.

Stage 2 is divided into two milestones: the first awards $3M to up to 15 winners at $200,000 each for early prototypes, while the second distributes $3.6M among six runners-up receiving $460,000 each, culminating in the grand prize.

Examples of quantum detection methods sought include nanodiamond sensors measuring cell temperature and stress, quantum magnetometry for tracking electrical activity in tissue, and quantum-enhanced spectroscopy for detecting metabolic changes. Hybrid quantum-classical approaches are also welcomed, acknowledging the potential for combining existing technologies with emerging quantum capabilities.

The challenge focuses on three key areas: quantum-enabled measurement approaches for toxicity endpoints, application of these measurements within in vitro NAMs, and data analytics integration to improve the predictive power of preclinical models. Registration for the QuSAFE challenge opens on October 25th, with a technical assistance webinar scheduled for November 30th to clarify goals and assist team formation.

Winners may also gain opportunities to collaborate with NIH-supported NAMs programs and present their work at national and international conferences. Further details on NIH quantum technologies efforts are available at the QuBIT program site, https://ncats.nih.gov/research/research-activities/quantum, and broader federal quantum technology development efforts can be viewed at https://www.quantum.gov/.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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