The argument reduces any $N$-query circuit to the approximation of $e^{it\theta}$ by a trigonometric polynomial with degree bounded by $O(N)$ by a trigonometric polynomial with degree bounded by $O(N)$ together with Remez inequality to establish a matching lower bound for arbitrary query algorithms.
A. Liu, Adam Wesolowski, Jayne Thompson et al.· 0 citations
Weighted Quantum Signal Processing is introduced, an extension of QSP in which a weight function is assigned to the central rotation operator, which provides a deeper understanding of QSP and yields expressive and parameter-efficient neural architectures, highlighting the potential of WQSP as a scalable primitive for q...
Rohit Sarma Sarkar, Rupayan Bhattacharjee, E. F. Combarro et al.· 0 citations
We introduce Coherent Quantum Learning (CQL), a training framework for quantum learning models in which the model parameters are quantum degrees of freedom evolved under a Hamiltonian that encodes the loss function. Current quantum machine learning retains classical optimization: parameters are updated by a classical o...
Ignacio B. Acedo, Javier Gonzalez-Conde, Pablo Rodriguez-Grasa et al.· 0 citations
This work introduces quantum topological data encoding (QTDE), a general framework for encoding topological information into quantum states via topology-driven quantum evolution, and indicates several areas of application where the framework can be used to provide a more efficient and reliable data representation.
Adam Wesolowski, Dimitrios Thanos, Daniel Leykam et al.· 0 citations
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