Sep 2026· National Science Review· 0 citations· 5 references
Physics
TL;DR
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.
Abstract
Quantum neural networks (QNNs), one of the fundamental algorithms in quantum machine learning, have been widely used in classification and identification tasks. However, the capabilities of QNNs are constrained by their size, which is determined by the dimension of the Hilbert space of the underlying quantum processor. Multi-level quantum digits (qudits) offer access to a higher-dimensional Hilbert space compared to two-level qubits, enabling the construction of more expressive QNNs. In this work, we report an experimental demonstration of qudit-based QNN using a trapped $\rm ^{40}Ca^+$ ion. We train the QNN using a hybrid quantum-classical implementation of backpropagation and achieve an experimental classification accuracy of 95.7% on a test image set. This demonstration highlights the potential of qudit-based processors to QNN architectures and provides a framework for implementing qudit-based QNNs across various quantum devices.
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
This work extends the notion of quantum neural network, using random sampling and classical data processing to enlarge the optimization space in a way that includes linear combinations of completely positive maps and leverages its enlarged optimization space to achieve increased expressivity and improved noise robustne...
Ben-Chi Zhao, Xuanqiang Zhao, Yi-Nan Li et al.· 0 citations
Training quantum neural networks (QNNs) on near-term hardware remains hampered by two compounding difficulties: the exponential vanishing of gradient variance known as the barren plateau, and the $\mathcal{O}(L \cdot 2^n)$ time and memory cost of differentiating through an $n$-qubit, $L$-layer circuit. We propose RLQ-G...
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
Together, these contributions show how learned representations, statistical control, and hardware constraints shape the exchange between quantum computing and machine learning.
Quantum machine learning could uncover statistical structure beyond the reach of classical models, but this requires quantum models large and expressive enough to be useful and cheap enough to read out. Most approaches optimize many quantum parameters and are thus limited by expensive training loops. Here we report a q...
Antonio Sannia, Roberto Menta, Pratik Sathe et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
