6 papers · 1 filter
Quantum Super-resolution by Adaptive Non-local Observables
Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen +1
Super-resolution (SR) seeks to reconstruct high-resolution (HR) data from low-resolution (LR) observations. Classical deep learning methods have advanced SR substantially, but requ…
Neural Architecture Search for Quantum Autoencoders
Hibah Agha, Samuel Yen-Chi Chen, Huan-Hsin Tseng +1
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-laye…
It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensembles
Junghoon Justin Park, Huan-Hsin Tseng, Shinjae Yoo +2
Quantum reinforcement learning (QRL) promises compact function approximators with access to vast Hilbert spaces, but its practical progress is slowed by NISQ-era constraints such a…
Quantum Reinforcement Learning by Adaptive Non-local Observables
Hsin-Yi Lin, Samuel Yen-Chi Chen, Huan-Hsin Tseng +1
Hybrid quantum-classical frameworks leverage quantum computing for machine learning; however, variational quantum circuits (VQCs) are limited by the need for local measurements. We…
Adaptive Non-local Observable on Quantum Neural Networks
Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen +1
Conventional Variational Quantum Circuits (VQCs) for Quantum Machine Learning typically rely on a fixed Hermitian observable, often built from Pauli operators. Inspired by the Heis…
Transfer Learning Analysis of Variational Quantum Circuits
Huan-Hsin Tseng, Hsin-Yi Lin, Samuel Yen-Chi Chen +1
This work analyzes transfer learning of the Variational Quantum Circuit (VQC). Our framework begins with a pretrained VQC configured in one domain and calculates the transition of…