5 papers
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, MichaŠOszmaniec +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…
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…
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…