Do reach out to us at Coherent Computing coherentcomputing.com. if you’d like us to help evaluate whether your dataset is a good fit for quantum.

If you’re considering a quantum machine learning project, don’t go in blind: knowing the qubit requirement up front is essential to avoid wasted effort and ensure the project is truly worth pursuing.

These results aren’t just academic - we’ve developed a practical framework within Red Cedar, our QML platform, that you can apply directly to your own datasets.

But they are still well within the reach of the quantum computers that are expected to be viable in the next 5 years! These results support that some of the largest, most information-rich problems in biology and beyond may be prime candidates for quantum machine learning.

However, when applied to subsets of the Tahoe-100M dataset from Tahoe Therapeutics, a transcriptomic dataset with 100 million samples, the required qubits quickly exceed the practical limit for classical simulation.

Within this framework, we find that many medium-sized datasets require only about 20 qubits to encode, and so they aren’t great candidates for quantum learning.

It also allows us to predict how many logical qubits are needed for a quantum model to train to a desired accuracy on a dataset of interest. This is the first encoding framework that connects datasets directly to quantum hardware requirements.

In our paper “How many qubits does a machine learning problem require?” arxiv.org/pdf/2508.20992, we show that bit-bit encoding, a recently developed classical -> quantum encoding technique, makes it possible to encode datasets efficiently into quantum computers without compromising on expressivity.

At Coherent Computing, we’ve been thinking hard about these challenges, and we have a solution.

Or you went with amplitude encoding, and suddenly the cost of loading the data eats up your entire quantum budget unless you compromise on the loading quality. Or perhaps you turned to hybrid quantum-classical models, only to wonder whether the quantum part is even pulling its weight?

You’ve probably wrestled with how to encode it into a quantum computer. Maybe you tried angle encoding with data reuploading - only to realize that boosting expressivity isn’t straightforward.

Please come to Vancouver too!

If you missed the talk, the paper is at arxiv.org/abs/2501.02148

And yesterday I took a break from the meeting to head to Caltech to meet my PhD advisor Ravin Bhatt was was visiting

Come listen and get in touch if you want to discuss either of the topics!

- A Comprehensive Cross-Model Framework for Benchmarking the Performance of Quantum Hamiltonian Simulations, presented by Avimita Chatterjee
summit.aps.org/events/VIR-C...