9 citations · 14 across the 7 of their papers we have counts for
7 papers · 1 filter
Measurement-free Preparation of Surface-Code States with Digital-Analog Counterdiabatic Drivings
Shubham Kumar Balaganchi A. Bhargava, Shubham Kumar, Balaganchi A. Bhargava +6
Preparing stabilizer-code states with shallow circuits is an important primitive for near-term quantum error correction in superconducting circuits, where logical-state initializat…
Physics-Informed Neural Networks for Maximizing Quantum Fisher Information in Time-Dependent Many-Body Systems
Antonio Ferrer-Sánchez, Yolanda Vives-Gilabert, Yue Ban +2
Quantum Fisher Information (QFI) sets the ultimate precision limit for parameter estimation and is therefore a central quantity in quantum metrology. In time-dependent many-body sy…
Analog Quantum Feature Selection with Neutral-Atom Quantum Processors
Jose J. Orquin-Marques, Carlos Flores-Garrigos, Alejandro Gomez Cadavid +5
We present a quantum-native approach to quantum feature selection (QFS) based on analog quantum simulation with neutral atom arrays, adaptable to a variety of academic and industri…
Quenched Quantum Feature Maps
Anton Simen, Carlos Flores-Garrigos, Murilo Henrique De Oliveira +9
We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns at the quantum-advantage level for academ…
Digital-analog quantum convolutional neural networks for image classification
Anton Simen, Carlos Flores-Garrigos, Narendra N. Hegade +6
We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemmin…
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation
Antonio Ferrer-Sánchez, Carlos Flores-Garrigos, Carlos Hernani-Morales +7
We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum…
Machine Learning for maximizing the memristivity of single and coupled quantum memristors
Carlos Hernani-Morales, Gabriel Alvarado, Francisco Albarrán-Arriagada +3
We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show that maximizing the memristivity leads to large…