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About this paper
Yaswitha Gujju, Atsushi Matsuo, Rudy Raymond
Researchers from The University of Tokyo, IBM Quantum, and J.P. Morgan Chase & Co. conducted a comprehensive survey of quantum machine learning (QML) implementations on near-term quantum devices for supervised and unsupervised tasks in real-world applications. The survey analyzes current performance, highlights persistent hardware and algorithmic challenges, and identifies critical bottlenecks preventing a clear quantum advantage.
Key insights
- QML performance on near-term devices is transitioning into an empirical science, heavily influenced by hardware limitations and specific implementation choices.
- Efficient classical data encoding into quantum states remains a central bottleneck, often consuming significant coherence time and posing scalability challenges.
- Barren plateaus, characterized by exponentially vanishing gradients, are a widespread challenge hindering the trainability of variational quantum algorithms on current hardware.
Summary by alphaXiv.
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