Chronological feed of everything captured from Maria Schuld.
The paper proposes a quantum perceptron model that simulates the step-activation function of classical perceptrons using the quantum phase estimation algorithm. It processes inputs of size n with O(n) resource requirements, enabling efficient scaling. This foundational unit supports development of trainable quantum neural networks in quantum machine learning.
Quantum machine learning explores enhancing classical ML algorithms using quantum computing, from efficient execution of costly subroutines to reformulating stochastic methods in quantum terms. The field addresses tasks like image/speech recognition and optimization relevant to IT. This paper provides an accessible systematic review of approaches, technical details, and prospects for a quantum learning theory.
Quantum Neural Networks (QNNs) aim to fuse neural computing's nonlinear, dissipative dynamics with quantum computing's linear, unitary evolution, but current proposals fall short. The paper systematically reviews QNN research, defines key requirements, and finds no model fully leverages both quantum advantages and neural properties. It proposes Open Quantum Neural Networks via dissipative quantum computing as a promising path forward.
Proposes quantum neural networks (QNNs) using qubits instead of binary neurons to leverage quantum computing. Models QNN dynamics via stochastic quantum walks on global firing state graphs, replicating classical associative memory. Biased discrete Hadamard walks from biological neuron updates fail unitarity, but stochastic walks succeed with modest quantum speed-up in select regimes.