2 citations · 2 across the 5 of their papers we have counts for
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Eigenstate Preparation Through Near-Optimal Eigenprobability Filtering
Po-Wei Huang, Bence Bakó, Bálint Koczor
Quantum simulation is expected to be a main application of quantum computers with realistic utility in quantum chemistry, materials science and beyond. However, preparing excited o…
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…
Exponential distillation of dominant eigenproperties
Bence Bakó, Tenzan Araki, Bálint Koczor
Estimating observable expectation values in eigenstates of quantum systems has a broad range of applications and is an area where early fault-tolerant quantum computers may provide…