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.

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.

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

Get Started

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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