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Found 12 papers, 1 papers with code
Privacy-preserving quantum federated learning via gradient hiding
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum nodes.
Blind quantum machine learning with quantum bipartite correlator
Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes.
Expressive variational quantum circuits provide inherent privacy in federated learning
However, standard neural network-based federated learning models have been shown to be susceptible to data leakage from the gradients shared with the server.
Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification
However, as data sizes grow, traditional methods for constructing and retraining decision trees become increasingly slow, scaling polynomially with the number of training examples.
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
Numerical evidence against advantage with quantum fidelity kernels on classical data
We show that the general-purpose hyperparameter tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well-approximated by a classical kernel, removing the possibility of quantum advantage.
MaSS: Multi-attribute Selective Suppression
The recent rapid advances in machine learning technologies largely depend on the vast richness of data available today, in terms of both the quantity and the rich content contained within.
Quantum Machine Learning for Finance
In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term.
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation
Current state-of-the-art models for video action recognition are mostly based on expensive 3D ConvNets.
Ranked #89 on
Action Recognition
on Something-Something V2
(using extra training data)
A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest.
Efficient Fusion of Sparse and Complementary Convolutions
We propose a new method to create compact convolutional neural networks (CNNs) by exploiting sparse convolutions.
Sparse-Complementary Convolution for Efficient Model Utilization on CNNs
By simply replacing the convolution of a CNN with our sparse-complementary convolution, at the same FLOPs and parameters, we can improve top-1 accuracy on ImageNet by 0. 33% and 0. 18% for ResNet-101 and ResNet-152, respectively.
