Search Results for author: Marco Pistoia

Found 11 papers, 1 papers with code

Expressive variational quantum circuits provide inherent privacy in federated learning

no code implementations22 Sep 2023 Niraj Kumar, Jamie Heredge, Changhao Li, Shaltiel Eloul, Shree Hari Sureshbabu, Marco Pistoia

However, standard neural network-based federated learning models have been shown to be susceptible to data leakage from the gradients shared with the server.

Federated Learning Quantum Machine Learning

Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification

no code implementations18 Sep 2023 Niraj Kumar, Romina Yalovetzky, Changhao Li, Pierre Minssen, Marco Pistoia

However, as data sizes grow, traditional methods for constructing and retraining decision trees become increasingly slow, scaling polynomially with the number of training examples.

Binary Classification

Numerical evidence against advantage with quantum fidelity kernels on classical data

no code implementations29 Nov 2022 Lucas Slattery, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Sami Khairy, Stefan M. Wild

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.

Inductive Bias Quantum Machine Learning

MaSS: Multi-attribute Selective Suppression

no code implementations18 Oct 2022 Chun-Fu Chen, Shaohan Hu, Zhonghao Shi, Prateek Gulati, Bill Moriarty, Marco Pistoia, Vincenzo Piuri, Pierangela Samarati

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

no code implementations9 Sep 2021 Marco Pistoia, Syed Farhan Ahmad, Akshay Ajagekar, Alexander Buts, Shouvanik Chakrabarti, Dylan Herman, Shaohan Hu, Andrew Jena, Pierre Minssen, Pradeep Niroula, Arthur Rattew, Yue Sun, Romina Yalovetzky

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.

BIG-bench Machine Learning Quantum Machine Learning

A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver

no code implementations21 Oct 2019 Arthur G. Rattew, Shaohan Hu, Marco Pistoia, Richard Chen, Steve Wood

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

no code implementations7 Aug 2018 Chun-Fu Chen, Quanfu Fan, Marco Pistoia, Gwo Giun Lee

We propose a new method to create compact convolutional neural networks (CNNs) by exploiting sparse convolutions.

General Classification object-detection +1

Sparse-Complementary Convolution for Efficient Model Utilization on CNNs

no code implementations ICLR 2018 Chun-Fu (Richard) Chen, Jinwook Oh, Quanfu Fan, Marco Pistoia, Gwo Giun (Chris) Lee

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.

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