3 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
On Certifying Source Sampling Hardness in Quantum Generative Modeling
Chen-Yu Liu, Leonardo Placidi, Enrico Rinaldi
Quantum generative models are often motivated by circuit families whose output distributions are believed to be classically hard to sample from. When such models are trained on ord…
Generative IQP Circuit Learning with Physics-Informed Latent Initialization
Chen-Yu Liu, Leonardo Placidi, Marco Ballarin +1
Quantum generative learning based on instantaneous quantum polynomial-time (IQP) circuits can benefit from efficient classical training strategies. A recent latent adaptation frame…
The Impact of Qubit Connectivity on Quantum Advantage in Noisy IQP Circuits
Leonardo Placidi, Enrico Rinaldi, Keisuke Fujii +1
Instantaneous Quantum Polynomial-time (IQP) circuits are a candidate for demonstrating near-term quantum advantage, as their sampling task is believed to be classically hard in the…
Deep Learning Approaches to Quantum Error Mitigation
Leonardo Placidi, Ifan Williams, Enrico Rinaldi +4
We present a systematic investigation of deep learning methods applied to quantum error mitigation of noisy output probability distributions from measured quantum circuits. We comp…
MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era
Leonardo Placidi, Ryuichiro Hataya, Toshio Mori +4
We introduce the first large-scale dataset, MNISQ, for both the Quantum and the Classical Machine Learning community during the Noisy Intermediate-Scale Quantum era. MNISQ consists…
VQE-generated quantum circuit dataset for machine learning
Akimoto Nakayama, Kosuke Mitarai, Leonardo Placidi +2
Quantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical prob…