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Quantum Autoencoders for Learning Quantum Channel Codes
This work investigates the application of quantum machine learning techniques for classical and quantum communication across different qubit channel models.
High-Quality Protein Force Fields with Noisy Quantum Processors
Using a linear depth ansatz with active-space reduction, we estimate a quantum circuit with a circuit depth of few thousands can be used to simulate these dipeptides.
Quantum Physics Chemical Physics
Exploring Transfer Function Nonlinearity in Echo State Networks
ESN is a simple neural network architecture in which a fixed recurrent network is driven with an input signal, and the output is generated by a readout layer from the measurements of the network states.
Product Reservoir Computing: Time-Series Computation with Multiplicative Neurons
Echo state networks (ESN), a type of reservoir computing (RC) architecture, are efficient and accurate artificial neural systems for time series processing and learning.
