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…
Machine-learning assisted quantum control in random environment
Tang-You Huang, Yue Ban, E. Ya. Sherman +1
Disorder in condensed matter and atomic physics is responsible for a great variety of fascinating quantum phenomena, which are still challenging for understanding, not to mention t…
Digitized-Counterdiabatic Quantum Optimization
Narendra N. Hegade, Xi Chen, Enrique Solano
We propose digitized-counterdiabatic quantum optimization (DCQO) to achieve polynomial enhancement over adiabatic quantum optimization for the general Ising spin-glass model, which…
Portfolio Optimization with Digitized-Counterdiabatic Quantum Algorithms
N. N. Hegade, P. Chandarana, K. Paul +3
We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to…
Quantum Stream Learning
Yongcheng Ding, Xi Chen, Rafael Magdalena-Benedicto +1
The exotic nature of quantum mechanics makes machine learning (ML) be different in the quantum realm compared to classical applications. ML can be used for knowledge discovery usin…
Active Learning for the Optimal Design of Multinomial Classification in Physics
Yongcheng Ding, José D. Martín-Guerrero, Yujing Song +2
Optimal design for model training is a critical topic in machine learning. Active Learning aims at obtaining improved models by querying samples with maximum uncertainty according…
Entangled Quantum Memristors
Shubham Kumar, Francisco A. Cárdenas-López, Narendra N. Hegade +4
We propose the interaction of two quantum memristors via capacitive and inductive coupling in feasible superconducting circuit architectures. In this composed system the input gets…
Digitized-counterdiabatic quantum approximate optimization algorithm
P. Chandarana, N. N. Hegade, K. Paul +4
The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization p…
Digitized Adiabatic Quantum Factorization
Narendra N. Hegade, Koushik Paul, Francisco Albarrán-Arriagada +2
Quantum integer factorization is a potential quantum computing solution that may revolutionize cryptography. Nevertheless, a scalable and efficient quantum algorithm for noisy inte…
Experimentally Realizing Efficient Quantum Control with Reinforcement Learning
Ming-Zhong Ai, Yongcheng Ding, Yue Ban +7
Robust and high-precision quantum control is crucial but challenging for scalable quantum computation and quantum information processing. Traditional adiabatic control suffers seve…
Breaking Adiabatic Quantum Control with Deep Learning
Yongcheng Ding, Yue Ban, José D. Martín-Guerrero +3
In the era of digital quantum computing, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep rei…
Retrieving Quantum Information with Active Learning
Yongcheng Ding, José D. Martín-Guerrero, Mikel Sanz +3
Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides a…
Implementation of a Hybrid Classical-Quantum Annealing Algorithm for Logistic Network Design
Yongcheng Ding, Xi Chen, Lucas Lamata +2
The logistic network design is an abstract optimization problem that, under the assumption of minimal cost, seeks the optimal configuration of the supply chain's infrastructures an…
Quantum Advantage in Cryptography with a Low-Connectivity Quantum Annealer
Feng Hu, Lucas Lamata, Chao Wang +3
The application in cryptography of quantum algorithms for prime factorization fostered the interest in quantum computing. However, quantum computers, and particularly quantum annea…
Towards Pricing Financial Derivatives with an IBM Quantum Computer
Ana Martin, Bruno Candelas, Ángel Rodríguez-Rozas +6
Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time-evolution of interest rates. Several stochastic d…
Towards Prediction of Financial Crashes with a D-Wave Quantum Computer
Yongcheng Ding, Javier Gonzalez-Conde, Lucas Lamata +7
Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can guarantee to find optimal solutions effici…
Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons
Tasio Gonzalez-Raya, Xiao-Hang Cheng, Iñigo L. Egusquiza +3
The Hodgkin-Huxley model describes the behavior of the cell membrane in neurons, treating each part of it as an electric circuit element, namely capacitors, memristors, and voltage…
Experimental Implementation of a Quantum Autoencoder via Quantum Adders
Yongcheng Ding, Lucas Lamata, Mikel Sanz +2
Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information. R…
Quantum computing cryptography: Finding cryptographic Boolean functions with quantum annealing by a 2000 qubit D-wave quantum computer
Feng Hu, Lucas Lamata, Mikel Sanz +4
As the building block in symmetric cryptography, designing Boolean functions satisfying multiple properties is an important problem in sequence ciphers, block ciphers, and hash fun…