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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.
Our recent paper describing quantum machine learning results with 108 qubits, the largest QML experiment to date is here, and a Webinar recording describing the approach and results is here.
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.
Effective for classification as well as prediction tasks.
Read this paper describing the largest QML experiment to date.
Large-scale quantum reservoir learning with an analog quantum computer
Our expert team is happy to discuss how we might be able to help
Contact us today
Watch a recent Webinar explaining quantum reservoir computing and recent results.
Results with QuEra: New Quantum Machine Learning Results with Quantum Reservoir Computing
Watch a recent QML Webinar with Deloitte Consulting
Quantum Leaps in AI: Improved ML Classification with Neutral Atom Computers
Github code samples and tutorials
Access them here
