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Found 12 papers, 3 papers with code
Hierarchy of the echo state property in quantum reservoir computing
The echo state property (ESP) represents a fundamental concept in the reservoir computing (RC) framework that ensures output-only training of reservoir networks by being agnostic to the initial states and far past inputs.
Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
These results open up new possibilities for incorporating the prior data knowledge into the efficient design of QML models, leading to practical quantum advantages.
Variational Denoising for Variational Quantum Eigensolver
We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE by learning from noisy VQE outputs.
Quantum-Classical Hybrid Information Processing via a Single Quantum System
Current technologies in quantum-based communications bring a new integration of quantum data with classical data for hybrid processing.
Quantum Noise-Induced Reservoir Computing
We demonstrate this ability in several typical benchmarks and investigate the information processing capacity to clarify the framework's processing mechanism and memory profile.
Learning Temporal Quantum Tomography
Quantifying and verifying the control level in preparing a quantum state are central challenges in building quantum devices.
Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces
This feature map provides opportunities to incorporate quantum advantages into machine learning algorithms to be performed on near-term intermediate-scale quantum computers.
Higher-Order Quantum Reservoir Computing
Quantum reservoir computing (QRC) is an emerging paradigm for harnessing the natural dynamics of quantum systems as computational resources that can be used for temporal machine learning tasks.
Evaluating the phase dynamics of coupled oscillators via time-variant topological features
By characterizing the phase dynamics in coupled oscillators, we gain insights into the fundamental phenomena of complex systems.
Topological Persistence Machine of Phase Transitions
Topological data analysis is an emerging framework for characterizing the shape of data and has recently achieved success in detecting structural transitions in material science, such as the glass--liquid transition.
Scale-variant topological information for characterizing complex networks
Real-world networks are difficult to characterize because of the variation of topological scales, the non-dyadic complex interactions and the fluctuations.
Social and Information Networks Algebraic Topology Physics and Society
Topological time-series analysis with delay-variant embedding
This method reveals multiple-time-scale patterns in a time series by allowing observation of variations in topological features, with time delay serving as an extra dimension in topological-feature space.
Data Analysis, Statistics and Probability
