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.


  • 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 of digit identification using quantum machine learning with QuEra
Example preliminary results for QML recognition of digits

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Learn more about machine learning with neutral atoms
Read this interesting paper from a group of Harvard and IBM researchers: Quantum Reservoir Computing Using Arrays of Rydberg Atoms
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