arXiv:2606.28833v1 Announce Type: new Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must be sampled with a finite number of circuit executions. Recent work on quantum kernel classification has shown that allocating shots non-uniformly across kernel entries, weighted by their downstream task sensitivity, can reduce the shot budget required to reach a target accuracy. We extend this idea to Gaussian process (GP) regression, a setting whose downstream quantities (full-spectrum posterior variance, l
The development of quantum machine learning continues as researchers explore ways to optimize resource-constrained quantum hardware for practical applications, like Gaussian process regression.
This research addresses a fundamental limitation in quantum computing, the shot budget, potentially making quantum kernel methods more efficient and cost-effective for complex tasks.
The proposed method could reduce the computational overhead and accuracy limitations of quantum kernel estimation by intelligently allocating resources.
- · Quantum computing researchers
- · AI/ML developers
- · Companies investing in quantum machine learning
- · Classical regression methods (in specific quantum-advantage scenarios)
More efficient and accurate quantum machine learning models, especially for regression tasks, become feasible on near-term quantum hardware.
Increased adoption of quantum kernel methods in specialized applications where their unique advantages outweigh the computational cost.
Acceleration of quantum algorithm development and hardware improvements to further exploit these optimized kernel estimation techniques.
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Read at arXiv cs.LG