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Found 10 papers, 4 papers with code
Transfer learning with generative models for object detection on limited datasets
We validate our approach on object detection tasks, specifically focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting.
The role of data embedding in equivariant quantum convolutional neural networks
In this work, we investigate the role of classical-to-quantum embedding on the performance of equivariant quantum convolutional neural networks (EQCNNs) for the classification of images.
Quantum-Noise-driven Generative Diffusion Models
Generative models realized with machine learning techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data.
Machine-learning based noise characterization and correction on neutral atoms NISQ devices
Here, two approaches are proposed to characterize and correct noise parameters on neutral atoms NISQ devices.
Deep learning enhanced noise spectroscopy of a spin qubit environment
The undesired interaction of a quantum system with its environment generally leads to a coherence decay of superposition states in time.
Experimental quantum pattern recognition in IBMQ and diamond NVs
Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea.
Noise fingerprints in quantum computers: Machine learning software tools
In this paper we present the high-level functionalities of a quantum-classical machine learning software, whose purpose is to learn the main features (the fingerprint) of quantum noise sources affecting a quantum device, as a quantum computer.
Learning the noise fingerprint of quantum devices
Noise sources unavoidably affect any quantum technological device.
Machine learning classification of non-Markovian noise disturbing quantum dynamics
In this paper machine learning and artificial neural network models are proposed for the classification of external noise sources affecting a given quantum dynamics.
Classification of cancer pathology reports: a large-scale comparative study
We report about the application of state-of-the-art deep learning techniques to the automatic and interpretable assignment of ICD-O3 topography and morphology codes to free-text cancer reports.
