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This research presents a significant advancement in quantum machine learning by eliminating the need for post-selection, a method that previously limited the probability of successfully generating trained models. By integrating fixed-point amplitude amplification into a coherent circuit, the authors provide theoretical guarantees on output error and resource costs, enabling high-probability acquisition of models with certified quality. This work addresses a critical bottleneck in variational-free quantum training, making scalable quantum automated learning more practical and efficient.
Post-Selection-Free Quantum Automated Learning
arXiv:2610.08219v1 Announce Type: new Abstract: Quantum automated learning (QAL) trains quantum models by directly updating quantum states, offering an approach to quantum machine learning that avoids optimizing variational circuit parameters. However, post-selection constrains the probability of obtaining the final learned model. We develop a post-selection-free QAL algorithm that organizes a complete training path into a coherent quantum circuit and uses fixed-point amplitude amplification to increase the probability of obtaining the learned model. We provide theoretical guarantees on output error and learning loss, and identify conditions under which additional coherent resources reduce the cost of restarting failed trajectories. The construction gives QAL a coherent implementation with explicit learning guarantees and resource costs, enabling a model with certified learning quality and controlled output error to be obtained with high probability.
Abstract:Quantum automated learning (QAL) trains quantum models by directly updating quantum states, offering an approach to quantum machine learning that avoids optimizing variational circuit parameters. However, post-selection constrains the probability of obtaining the final learned model. We develop a post-selection-free QAL algorithm that organizes a complete training path into a coherent quantum circuit and uses fixed-point amplitude amplification to increase the probability of obtaining the learned model. We provide theoretical guarantees on output error and learning loss, and identify conditions under which additional coherent resources reduce the cost of restarting failed trajectories. The construction gives QAL a coherent implementation with explicit learning guarantees and resource costs, enabling a model with certified learning quality and controlled output error to be obtained with high probability.
arXiv:2610.08219 [quant-ph]
(or arXiv:2610.08219v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.08219 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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