7 citations · 12 across the 9 of their papers we have counts for
10 papers
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…
Universality of Classically Trainable, Quantum-Deployed Boson-Sampling Generative Models
Andrii Kurkin, Ulysse Chabaud, Zoltán Kolarovszki +3
Recent work on the instantaneous quantum polynomial-time (IQP) quantum-circuit Born machine (QCBM) highlights a promising paradigm for generative modeling: train classically, deplo…
Fermionic Born Machines: Classical training of quantum generative models based on Fermion Sampling
Bence Bakó, Zoltán Kolarovszki, Zoltán Zimborás
Quantum generative learning is a promising application of quantum computers, but faces several trainability challenges, including the difficulty in experimental gradient estimation…
On the learning abilities of photonic continuous-variable Born machines
Zoltán Kolarovszki, Dániel T. R. Nagy, Zoltán Zimborás
This paper investigates photonic continuous-variable Born machines (CVBMs), which utilize photonic quantum states as resources for continuous probability distributions. Implementin…
Suppressing photon detection errors in nondeterministic state preparation
Csaba Czabán, Zoltán Kolarovszki, Márton Karácsony +1
Photonic quantum computing has recently emerged as a promising candidate for fault-tolerant quantum computing by photonic qubits. These protocols make use of nondeterministic gates…