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.
Benefits
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.
Get Started
Learn more about machine learning with neutral atoms
Read this paper from a group of Harvard and IBM researchers
Quantum Reservoir Computing Using Arrays of Rydberg Atoms
Watch a recent QML Webinar with Deloitte Consulting
Quantum Leaps in AI: Improved ML Classification with Neutral Atom Computers
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