Quantum AI Platform
Access beyond-classical insights by training quantum models on your data with Red Cedar
Trainable at scale — bit-bit encoding, optimizer-free training, and sub-net initialization, drawn from our published work, scale quantum neural networks past the trainability barriers that stall standard approaches.
Explainable by design — move past black-box ML. Embed a trained model into a quantum circuit you can query to see which feature correlations drive a prediction.
Train once, deploy many ways — turn a trained classifier into a generator, or plug the same trained unitary into algorithms like Grover’s search and phase estimation for new applications, with no retraining.
See under the hood — built-in visualization to diagnose training bottlenecks as they happen.
How we scale Quantum AI
| Where Industry Approach Falls Short | Cascade Quantum Approach | |
| Data Loading Bottleneck | Dimensional reduction followed by angle/amplitude encoding reduces expressivity | Bit-bit encoding preserves expressivity with effective dimensional reduction |
| Barren Plateaus | Restrictions on model design lead to dequantization | Sub-net initialization can iteratively train arbitrarily large models |
| Local Minima | Few insights into nature of minima lead to premature conclusions of QML ineffectiveness | Visualization to understand minima and design loss functions to avoid them |
| Slow Inference | Expectation value based read-out can require unbounded shots | Bit-wise readout requires O(1) shots for inference |
| Slow Gradient Descent | Time for each optimization step scales with number of parameters | O(1) loss evaluations for exact coordinate updates during training |
Discover Applications
FOR RESEARCHERS & DEVELOPERS
Help Us Build Quantum Intelligence
The hardest problems in quantum machine learning won’t be solved by one team. If you work on QML algorithms or quantum programming in academia, industry or independently, apply to work with us.
What’s in it for you? Paid projects, revenue sharing, co-authored research, and a meaningful impact on our platform.
Joint Research
Co-author with us on scalable QML techniques, resource estimation, and encoding schemes. Research using our state-of-the-art platform and industry-leading techniques. Some of our best hires started as collaborators.
Build on Our Platform
Get early access to the Quantum AI Platform. Train models, run resource estimates, and stress-test our abstractions on your own problems, and shape the roadmap with your feedback.
Bring Your Hardware Access
Already have hardware access? Bring it. Our platform is hardware-agnostic, and we’ll help you turn that allocation into results worth publishing.
WE WORK BEST WITH PEOPLE WHO
Are willing to be bold. The interesting directions are the ones nobody can promise will pay off, and we’d rather attempt those than play it safe.
Build for others. The best outcome is to contribute to something that many other people will build upon. You want to push the field forward.
Research Themes
Blog
What will it mean to create a new kind of intelligence – one that relies on quantum superposition and entanglement – which can discover patterns beyond the reach of classical logic? What unique approach will an intelligent quantum system take to problem-solving? Which currently intractable challenges in science, technology, and human society might suddenly become accessible?