Quantum AI Biomedical Research Lab

Quantum AI Biomedical Research Lab

Quantum and AI technology for medical applications

Publications

A quantum Bluestein algorithm for arbitrary size quantum Fourier transform

Published in arXiv, 2025

We propose a quantum analogue of Bluestein’s algorithm (QBA) that implements an exact N-point Quantum Fourier Transform (QFT) for arbitrary N. Our construction factors the N-dimensional QFT unitary into three diagonal quadratic-phase gates and two standard radix-2 QFT subcircuits of size M=2^m (with M≥2N−1). This achieves asymptotic gate complexity O((logN)^2) and uses O(logN) qubits, matching the performance of a power-of-two QFT on m qubits while avoiding the need to embed into a larger Hilbert space. We validate the correctness of the algorithm through a concrete implementation in Qiskit and classical simulation, confirming that QBA produces the exact N-point discrete Fourier transform on arbitrary-length inputs.

Recommended citation: Nan-Hong Kuo and Renata Wong (2025). "A quantum Bluestein algorithm for arbitrary size quantum Fourier transform" arXiv. 2512.15349. https://arxiv.org/abs/2512.15349

Bioinspired Quantum Oracle Circuits for Biomolecular Solutions of the Maximum Cut Problem

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

Making the zeroth-order process fidelity independent of state preparation and measurement errors

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