11 citations · 14 across the 5 of their papers we have counts for
5 papers
HattriQ: Designing Integrated Gradients for Feature Attribution in Quantum Machine Learning
Nicholas S. DiBrita, Jason Han, Younghyun Cho +2
Quantum machine learning (QML) algorithms have demonstrated early promise across hardware platforms, but remain difficult to interpret due to the inherent opacity of quantum state…
Domain-Aware Probability Sampling for Hybrid Quantum Systems using Bayesian Optimization
Nicholas S. DiBrita, Jason Han, Krishna Bhatia +3
We study the problem of probability distribution matching and sampling on near-term quantum computers, aiming to construct parameterized circuits that generate samples from a targe…
EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data
Jason Han, Nicholas S. DiBrita, Younghyun Cho +2
Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods suffer from deep, variab…
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization
Hengrui Luo, Younghyun Cho, James W. Demmel +2
This paper presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualita…
Non-smooth Bayesian Optimization in Tuning Problems
Hengrui Luo, James W. Demmel, Younghyun Cho +2
Building surrogate models is one common approach when we attempt to learn unknown black-box functions. Bayesian optimization provides a framework which allows us to build surrogate…