25 citations · 48 across the 14 of their papers we have counts for
16 papers
Custom Penalty QAOA via Walsh-Hadamard Transform
Xin Wei LEE, Siong Thye GOH, Hoong Chuin LAU
Inequality constraints in combinatorial optimization are conventionally handled by introducing slack variables and quadratic penalties, which increase the qubit count and distort t…
Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation
Xue Yang, Rigui Zhou, Dax Enshan Koh +5
Quantum Generative Adversarial Networks (QGANs) have emerged as representative generative models in the Noisy Intermediate-Scale Quantum (NISQ) era and have attracted increasing at…
CoQui: A Coordinate-Conditioned Quantum Implicit Generative Adversarial Network for End-to-End Image Generation
Xue Yang, Rigui Zhou, ShiZheng Jia +7
Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits. Existing amplitude-based approaches f…
Experimental Implementation of the Quantum Volunteer's Dilemma on NISQ Hardware: Noise Analysis and Digital-Twin Validation
Germán D. Díaz Agreda, Jhon Alejandro Andrade Hoyos, Carlos A. Durán Paredes +4
We present an experimental implementation of the multiplayer Quantum Volunteer's Dilemma on noisy intermediate-scale quantum (NISQ) hardware, executed on the ibm_kingston backend v…
Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings
Sebastian Cajas Ordóñez, Felipe Ocampo Osorio, Dax Enshan Koh +10
We provide evidence of quantum kernel advantage under noiseless simulation in binary insurance classification on MIMIC-CXR chest radiographs using quantum support vector machines (…
End-to-End QGAN-Based Image Synthesis via Neural Noise Encoding and Intensity Calibration
Xue Yang, Rigui Zhou, Shizheng Jia +5
Quantum Generative Adversarial Networks (QGANs) offer a promising path for learning data distributions on near-term quantum devices. However, existing QGANs for image synthesis avo…
Classical Shadows with Improved Median-of-Means Estimation
Winston Fu, Dax Enshan Koh, Siong Thye Goh +1
The classical shadows protocol, introduced by Huang et al. [Nat. Phys. 16, 1050 (2020)], makes use of the median-of-means (MoM) estimator to efficiently estimate the expectation va…
Quantum Volunteer's Dilemma
Dax Enshan Koh, Kaavya Kumar, Siong Thye Goh
The volunteer's dilemma is a well-known game in game theory that models the conflict players face when deciding whether to volunteer for a collective benefit, knowing that voluntee…
Double-bracket quantum algorithms for high-fidelity ground state preparation
Matteo Robbiati, Edoardo Pedicillo, Andrea Pasquale +13
Ground state preparation is a central application for quantum computers but remains challenging in practice. In this work, we quantitatively investigate the performance and gate co…
Solving Fractional Differential Equations on a Quantum Computer: A Variational Approach
Fong Yew Leong, Dax Enshan Koh, Jian Feng Kong +6
We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable…
Variational Quantum-Based Simulation of Waveguide Modes
Wei-Bin Ewe, Dax Enshan Koh, Siong Thye Goh +2
Variational quantum algorithms are one of the most promising methods that can be implemented on noisy intermediate-scale quantum (NISQ) machines to achieve a quantum advantage over…
QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver Surrogates
Tian Huang, Siong Thye Goh, Sabrish Gopalakrishnan +3
An increasingly popular method for solving a constrained combinatorial optimisation problem is to first convert it into a quadratic unconstrained binary optimisation (QUBO) problem…
A Hybrid Framework Using a QUBO Solver For Permutation-Based Combinatorial Optimization
Siong Thye Goh, Sabrish Gopalakrishnan, Jianyuan Bo +1
In this paper, we propose a hybrid framework to solve large-scale permutation-based combinatorial problems effectively using a high-performance quadratic unconstrained binary optim…
A Minimax Surrogate Loss Approach to Conditional Difference Estimation
Siong Thye Goh, Cynthia Rudin
We present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes. To overcome the problem th…
Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms
Siong Thye Goh, Lesia Semenova, Cynthia Rudin
We present sparse tree-based and list-based density estimation methods for binary/categorical data. Our density estimation models are higher dimensional analogies to variable bin w…
Box Drawings for Learning with Imbalanced Data
Siong Thye Goh, Cynthia Rudin
The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We…