Aug 2026· Computational Mathematics and Mathematical Physics· Vol 66, pp. 1579 - 1591· 0 citations· 41 references
TL;DR
The proposed machine learning method achieves high efficiency and fast execution times and accelerates the generation of target quantum processes compared to standard local search methods for qubit, qutrit, and qudit (five-level) systems.
Current research provides an overview of important QML algorithms, such as Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Variational Quantum Eigensolvers (VQE), Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantumclassical computing techniques, which have recently become more p...
J. Chen· Recent Research Reviews Jour...· 0 citations
One of the most important initial steps in quantum computing is high-fidelity quantum state preparation, because errors in it can affect the final computational result. Therefore, one of the major challenges is to design a quantum circuit that can achieve the desired target state accurately with a smaller number of gat...
Om Bhamare, Aryan Jadhav, Manas Shinde et al.· 0 citations
An experimental demonstration of qudit-based QNN using a trapped $\rm ^{40}Ca^+$ ion is reported, which highlights the potential of qudits to QNN architectures and provides a framework for implementing qudit-based QNNs across various quantum devices.
Yi-Bo Yuan, Zhuo-Yue Xu, Zhen-Yu Du et al.· National Science Review· 0 citations
The Noisy Intermediate-Scale Quantum (NISQ) era shows its practical applications through quantum computing which encounters essential problems with qubit stability and gate fidelity. This research introduces a complete quantum machine learning framework which uses IBM’s Qiskit toolkit to optimize quantum hardware perfo...
Sanjay Saroja Parameswaran, M. A. Rohit Surya, G. K et al.· 2026 International Conferenc...· 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 survey provides a comprehensive overview of the theoretical foundations, unified taxonomy, and key methodologies in QML, and discusses quantum data encoding techniques, variational quantum models, neural-inspired quantum architectures, and quantum data learning approaches, along with their associated challenges an...
Ashis Kumar Pati, Rajesh Vayyala, K. Mohanty et al.· IEEE Access· 0 citations
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