arXiv:2607.01080v1 Announce Type: new Abstract: We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the quantum kernel. This setting is motivated by NISQ-era tasks such as quantum control, state preparation and variational quantum algorithms. While quantum kernels can offer a `quantum advantage' via domain-specific inductive biases, na\"{i}vely using full, high-dimensional kernels increases model complexity and information gain, leading to higher cumulative regret and poo
The proliferation of NISQ-era quantum computing architectures necessitates advanced optimization techniques to maximize their utility and address computational challenges that classical methods cannot efficiently solve.
This research is crucial for advancing quantum machine learning, particularly in areas like quantum control and variational quantum algorithms, by balancing the performance gains from quantum kernels with the practicalities of model complexity.
The understanding of how to practically apply quantum kernels in bandit optimization is refined, suggesting more efficient paths to 'quantum advantage' without incurring excessive model complexity or regret.
- · Quantum computing researchers
- · Quantum hardware developers
- · AI/ML researchers leveraging quantum systems
- · Developers relying solely on brute-force quantum kernel approaches
Improved performance and broader applicability of quantum machine learning algorithms in specialized tasks.
Accelerated development of quantum-enhanced AI applications across various scientific and industrial domains.
Potential for quantum computers to achieve practical 'quantum advantage' in specific optimization and control problems sooner than anticipated.
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Read at arXiv cs.LG