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Found 16 papers, 2 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.
Virtual reservoir acceleration for CPU and GPU: Case study for coupled spin-torque oscillator reservoir
We provide high-speed implementations for simulating reservoirs described by $N$-coupled spin-torque oscillators.
Self-organized criticality for dendritic readiness potential
Self-organized criticality is a principle explaining avalanche-like phenomena obeying power-laws in integrate-and-fire type dynamical systems.
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.
Composite FORCE learning of chaotic echo state networks for time-series prediction
Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output only by training output connection weights.
Physical Deep Learning with Biologically Plausible Training Method
In addition, we can emulate and accelerate the computation for this training on a simple and scalable physical system.
Transient Chaos in BERT
A Lite BERT (ALBERT) is literally characterized as a lightweight version of BERT, in which the number of BERT parameters is reduced by repeatedly applying the same neural network called Transformer's encoder layer.
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.
Physical reservoir computing -- An introductory perspective
Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years.
Optimal short-term memory before the edge of chaos in driven random recurrent networks
The ability of discrete-time nonlinear recurrent neural networks to store time-varying small input signals is investigated by mean-field theory.
A Unifying Framework for Information Processing in Stochastically Driven Dynamical Systems
First, we establish a connection between the IPC for time-invariant systems and PC expansion, which is a type of polynomial expansion using orthogonal functions of input history as bases.
Use of recurrent infomax to improve the memory capability of input-driven recurrent neural networks
The inherent transient dynamics of recurrent neural networks (RNNs) have been exploited as a computational resource in input-driven RNNs.
Harnessing disordered quantum dynamics for machine learning
Quantum computer has an amazing potential of fast information processing.
