Discover the drugs that your AI can’t
ORCA discusses a recent use case with Technical University of Denmark on quantum accelerated breakthroughs in peptide design.
20 mins presentation followed by live Q&A.
Recording sent to everyone who registers.
Industry use cases
ORCA Computing
A wider net for new molecules
On the QM9 benchmark, ORCA's quantum prior increased the number of valid and unique molecules while also producing substantially more novel candidates outside the training dataset.
+49%
more valid & unique molecules vs the classical baseline
Read the case study
In partnership with DTU
Discover the drugs that your AI can’t
Advancing rare disease drug discovery and personalised cancer vaccines — quantum-accelerated generative AI finds the candidates classical models miss, validated in vitro across all tested alleles.
~2x
more strong binders on
rare-disease alleles vs classical AI
Read the case study
01
The discovery bottleneck
Every discovery programme hits the same constraint: the early screening funnel is too narrow. Classical generative models tend to concentrate around known chemical space, and the search for novel structures, molecules and functional materials becomes increasingly expensive or simply not feasible with classical compute alone.
02
Why quantum?
Generative models are only as good as the prior they use, and classical priors are too simple. A photonic quantum accelerator samples from complex distributions and can explore more of the chemical space that matters, reaching beyond the limits of classical computing alone.
03
The value ORCA Computing delivers
ORCA provides a practical, hybrid solution to the computational hurdles faced in molecular exploration. ORCA's photonic quantum accelerator works alongside your current classical workflow — same models, same training, same Python frameworks. Only the diversity and quality of what comes out change.
Same models. Same pipelines. Better candidates.
There is no bespoke quantum stack for your team to learn, and no separate discovery pipeline to maintain. The accelerator substitutes the prior your generative model samples from — everything downstream stays as it is.
Are you ready to achieve commercial quantum advantage at industrial scale?
Discuss a use case and lets tackle your hardest problems.
