  Quantum machine learning has gained considerable attention as quantum
technology advances, presenting a promising approach for efficiently learning
complex data patterns. Despite this promise, most contemporary quantum methods
require significant resources for variational parameter optimization and face
issues with vanishing gradients, leading to experiments that are either limited
in scale or lack potential for quantum advantage. To address this, we develop a
general-purpose, gradient-free, and scalable quantum reservoir learning
algorithm that harnesses the quantum dynamics of neutral-atom analog quantum
computers to process data. We experimentally implement the algorithm, achieving
competitive performance across various categories of machine learning tasks,
including binary and multi-class classification, as well as timeseries
prediction. Effective and improving learning is observed with increasing system
sizes of up to 108 qubits, demonstrating the largest quantum machine learning
experiment to date. We further observe comparative quantum kernel advantage in
learning tasks by constructing synthetic datasets based on the geometric
differences between generated quantum and classical data kernels. Our findings
demonstrate the potential of utilizing classically intractable quantum
correlations for effective machine learning. We expect these results to
stimulate further extensions to different quantum hardware and machine learning
paradigms, including early fault-tolerant hardware and generative machine
learning tasks.
