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Found 5 papers, 1 papers with code
Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i. e., generalizing).
The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware
To our knowledge, this is the first work to show a Spiking Neural Network (SNN) implementation of the backpropagation algorithm that is fully on-chip, without a computer in the loop.
Ranked #31 on
Image Classification
on MNIST
(Accuracy metric)
Long-time simulations with high fidelity on quantum hardware
Specifically, we simulate an XY-model spin chain on the Rigetti and IBM quantum computers, maintaining a fidelity of at least 0. 9 for over 600 time steps.
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer's ability to learn a unitary process (the quantum analog of a function) with quantum training data.
Variational Fast Forwarding for Quantum Simulation Beyond the Coherence Time
Finally, we implement VFF on Rigetti's quantum computer to show simulation beyond the coherence time.
Quantum Physics
