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.
Read this interesting paper from a group of Harvard and IBM researchers
Quantum Reservoir Computing Using Arrays of Rydberg Atoms
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