9 papers · 1 filter
Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods
Dao Duy Tung, Quoc Chuong Nguyen, Vu Tuan Hai +2
Selecting an effective encoding quantum circuit is a key challenge in quantum kernel methods because different feature maps can lead to different performance. Conventional methods…
Analytic correspondence between multipartite entanglement and quantum phase transitions
Huynh Le Dan Linh, Vu Tuan Hai, Le Bin Ho
We derive an analytic correspondence between multipartite concentratable entanglement (CE) and quantum phase transitions in one-dimensional quantum spin systems. We prove that CE s…
Benchmarking loss functions for trainable quantum feature maps
Nguyen Dinh Quyen, Vu Tuan Hai, Quoc Chuong Nguyen +2
Many quantum machine learning models employ quantum feature maps to encode classical data into quantum states. While fixed feature maps often lack sufficient expressivity for compl…
Measurement Geometry as a Resource for Certifying Network Nonlocality
Leon Adachi, Le Bin Ho
Quantum networks can exhibit nonclassical correlations that cannot be explained by classical models with independent sources. While the role of entanglement is well understood, the…
Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning
Dao Duy Tung, Nguyen Quoc Chuong, Vu Tuan Hai +2
In recent years, quantum kernel methods have shown promising applications on near-term quantum devices. However, selecting an appropriate encoding circuit for a given dataset requi…
Imaginary-time-enhanced feedback-based quantum algorithms for universal ground-state preparation
Thanh Nguyen Van Long, Lan Nguyen Tran, Le Bin Ho
Preparing ground states of strongly correlated quantum systems is a central goal in quantum simulation and optimization. The feedback-based quantum algorithm (FALQON) provides an a…