A non-profit research organisation. We build open tools and open models for cell biology, and we publish what we find — including the results that go against us.
The Quantum Omics Foundation is a nonprofit organization dedicated to research and education at the intersection of classical/quantum computing and life sciences. We bring together computational scientists, biologists, and educators to explore how classical and quantum computing methods can deepen our understanding of complex biological systems.
Our work focuses on applying quantum computing to multi-omics data — integrating genomics, transcriptomics, proteomics, and metabolomics — to build richer, more actionable models of cellular biology. We share our findings openly — preprints, open-source software, internships, and educational curriculum.
We advance Quantum Omics as an open scientific discipline through joint research publications, open-source tool development, student internships, and educational outreach. Partnering with universities and research institutions worldwide, our goal is to make these computational methods accessible, reproducible, and useful for human health research.
Mission. Advance classical/quantum computing for cell modelling through open research, open-source tools, and rigorous education — empowering a global community of researchers and students to apply classical and quantum computing methods to multi-omics and human health.
Vision. A future in which quantum computing is a standard tool in the life-sciences toolkit — enabling richer models of cellular biology, faster drug discovery, and more personalised understanding of health and disease.
We apply quantum computing methods to model cellular dynamics from multi-omics data, building open, reproducible frameworks that the research community can study, extend, and build upon. Our work spans cell modelling, describing disease as a change to those models, and computational drug screening — all developed openly and released under permissive licences.
The core: physics-inspired classical and quantum digital twins of a living cell, built so the basic rules of biology hold by construction rather than being fitted from data, and wrapped in a multi-agent system. Wrapped in a layer of specialist AI agents, it becomes an end-to-end stack that the atlas and disease work both build on.
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Extending one generic cell model across the many kinds of cell in the human body, by describing each as the shared foundation plus a few added specialities — so cell types can be compared in the same terms.
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Studying disease as a specific change to how a healthy cell behaves, and researching how to evaluate candidate compounds against a whole-cell model rather than a single target.
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