Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
arXiv:2606.03517v1 Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes. In this work, we introduce a training framework that reduces this cost to logarithmic in the number of qubits, making gradient-based QNN optimisation feasible on near-term hardware at increasing scales. Our framewor
The continuous advancements in quantum computing hardware necessitate more efficient training methods for quantum neural networks to unlock their practical applications.
This breakthrough addresses a fundamental bottleneck in quantum machine learning, making on-hardware training of larger quantum neural networks feasible and accelerating the path to quantum advantage.
The ability to train QNNs on hardware with drastically reduced gradient estimation costs changes the landscape for quantum algorithm development and commercialization.
- · Quantum hardware manufacturers
- · Quantum software developers
- · AI/ML researchers in quantum computing
- · Healthcare sector (clinical data applications)
- · Classical machine learning for certain complex problems
- · Current quantum simulation-based training methods
On-hardware training of larger-scale quantum neural networks becomes practical, leading to more complex quantum AI applications.
Accelerated development and adoption of quantum AI solutions across various industries, starting with those that benefit from clinical data imputation.
The emergence of quantum AI as a competitive alternative to classical AI for specific high-value problems, driving further investment and research.
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Read at arXiv cs.LG