Published in arXiv, 2025
In this work, we establish the equivalence between Support Vector Machine (SVM) kernels and quantum Greens functions.
Recommended citation: Nan-Hong Kuo and Tsung-Wei Chiang and Renata Wong (2025). "SVM/SVR kernels as quantum propagators" arXiv. 2502.11153. https://arxiv.org/abs/2502.11153
Published in IEEE Transactions on NanoBioscience, 2024
Given an undirected, unweighted graph with n vertices and m edges, the maximum cut problem is to find a partition of the n vertices into disjoint subsets and such that the number of edges between them is as large as possible. Classically, it is an NP-complete problem, which has potential applications ranging from circuit layout design, statistical physics, computer vision, machine learning and network science to clustering. In this paper, we propose a biomolecular and a quantum algorithm to solve the maximum cut problem for any graph G.
Recommended citation: Weng-Long Chang, Renata Wong, Yu-Hao Chen, Wen-Yu Chung, Ju-Chin Chen, Athanasios V. Vasilakos (2024). "Bioinspired Quantum Oracle Circuits for Biomolecular Solutions of the Maximum Cut Problem" IEEE Transactions on NanoBioscience 23(3): 499-506, DOI: 10.1109/TNB.2024.3395420. https://ieeexplore.ieee.org/abstract/document/10510482
Published in arXiv, 2023
In this work, we demonstrate that the zero-fidelity, an approximation to the process fidelity, when combined with randomized benchmarking, becomes robust to state preparation and measurement (SPAM) errors.
Recommended citation: Yu-Hao Chen and Renata Wong and Hsi-Sheng Goan (2023). "Making the zeroth-order process fidelity independent of state preparation and measurement errors" arXiv. 2312.08590. https://arxiv.org/abs/2312.08590