6 papers
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…
Toward Generative Quantum Utility via Correlation-Complexity Map
Chen-Yu Liu, Leonardo Placidi, Eric Brunner +1
We study a practical question in generative quantum machine learning: given a classical dataset, can we determine, before training, whether it is well suited to a quantum generativ…
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…
You Only Measure Once: On Designing Single-Shot Quantum Machine Learning Models
Chen-Yu Liu, Leonardo Placidi, Kuan-Cheng Chen +2
Quantum machine learning (QML) models conventionally rely on repeated measurements (shots) of observables to obtain reliable predictions. This dependence on large shot budgets lead…
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…