Quantum Machine Learning

Solve machine learning problems with the unique neutral-atom computer from QuEra

Quantum machine learning is widely considered a promising application for near-term quantum computers, with potential in computer vision, natural language processing, and finding general patterns in large data sets.

Aquila's 256 qubits allow encoding a very large parameter space, and our system-wide coherence and fast entanglement propagation deliver dramatic performance increases over other quantum approaches.

A recent Webinar showcased results obtained by Deloitte Consulting when using QuEra's quantum machine learning workflow. Watch the recording here.


Obtain solutions to complex machine learning problems that cannot currently be solved with gate-based quantum computers.

Enjoy an increased robustness to noise.

Leverage quantum dynamics to implement powerful algorithms such as reservoir machine learning.

Example preliminary results for QML recognition of digits

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Learn more about machine learning with neutral atoms

Read this paper from a group of Harvard and IBM researchers


Watch a recent QML Webinar with Deloitte Consulting


Contact us to discuss your requirements

Our expert team is happy to discuss how we might be able to help