8 citations · 10 across the 14 of their papers we have counts for
14 papers
Hardware-Aware Fermion-to-Qubit Mappings for Simulating the 2D Hubbard Model on Heavy-Hexagon Quantum Processors
Alessio Esposito, Andrea Giachero, Zoltàn Zimboràs +1
Quantum simulation of strongly correlated fermionic systems is among the most promising near- term applications of quantum computing, but its practical efficiency depends criticall…
Observable Estimation in the Absence of Classical Verification
Samantha V. Barron, Bradley Mitchell, Vinay Tripathi +45
The predictive success of quantum mechanics underpins many areas of modern science, even as the exact simulation of large, interacting quantum systems remains beyond the reach of c…
Repetition-code-based readout error detection and correction across hardware platforms and generations
Csaba Czabán, Orsolya Kálmán, Sergey N. Filippov +1
Readout errors are one of the dominant sources of noise in current quantum processors, limiting both expectation-value estimation and sampling-based applications. Since they affect…
Classical simulation of free-fermionic dynamics and quantum chemistry with magic input
Changhun Oh, Michał Oszmaniec, Oliver Reardon-Smith +1
Establishing the precise computational boundary between classically tractable fermionic systems and those capable of genuine quantum advantage is a central challenge in quantum sim…
General framework for anticoncentration and linear cross-entropy benchmarking in photonic quantum advantage experiments
Zoltán Kolarovszki, Ágoston Kaposi, Zoltán Zimborás +1
Photonic architectures are one of the leading platforms for demonstrating quantum computational advantage, with Boson Sampling and Gaussian Boson Sampling as the primary schemes. Y…
Generative modeling with Gaussian Boson Sampling: classically trainable Bosonic Born Machines
Zoltán Kolarovszki, Bence Bakó, Michał Oszmaniec +2
Quantum generative modeling has emerged as a promising application of quantum computers, aiming to model complex probability distributions beyond the reach of classical methods. In…