116 citations · 718 across the 27 of their papers we have counts for
27 papers
Photonic counterdiabatic quantum optimization algorithm
Pranav Chandarana, Koushik Paul, Mikel Garcia-de-Andoin +3
We propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization probl…
Active Learning in Physics: From 101, to Progress, and Perspective
Yongcheng Ding, José D. Martín-Guerrero, Yolanda Vives-Gilabert +1
Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled sa…
Superoscillating Quantum Control Induced By Sequential Selections
Yongcheng Ding, Yiming Pan, Xi Chen
Superoscillation is a counterintuitive phenomenon for its mathematical feature of ``faster-than-Fourier", which has allowed novel optical imaging beyond the diffraction limit. In t…
Digitized-Counterdiabatic Quantum Algorithm for Protein Folding
Pranav Chandarana, Narendra N. Hegade, Iraitz Montalban +2
We propose a hybrid classical-quantum digitized-counterdiabatic algorithm to tackle the protein folding problem on a tetrahedral lattice. Digitized-counterdiabatic quantum computin…
Time-Optimal Quantum Driving by Variational Circuit Learning
Tangyou Huang, Yongcheng Ding, Léonce Dupays +4
The simulation of quantum dynamics on a digital quantum computer with parameterized circuits has widespread applications in fundamental and applied physics and chemistry. In this c…
Meta-Learning Digitized-Counterdiabatic Quantum Optimization
Pranav Chandarana, Pablo S. Vieites, Narendra N. Hegade +3
Solving optimization tasks using variational quantum algorithms has emerged as a crucial application of the current noisy intermediate-scale quantum devices. However, these algorit…