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Found 6 papers, 1 papers with code
QArchSearch: A Scalable Quantum Architecture Search Package
The current era of quantum computing has yielded several algorithms that promise high computational efficiency.
Learning To Optimize Quantum Neural Network Without Gradients
This extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer.
BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms
In this paper, we propose an alternative strategy which initializes the parameters of a unitary gate by drawing from a beta distribution.
CONFAIR: Configurable and Interpretable Algorithmic Fairness
The rapid growth of data in the recent years has led to the development of complex learning algorithms that are often used to make decisions in real world.
Coping with Mistreatment in Fair Algorithms
In this paper, we study the algorithmic fairness in a supervised learning setting and examine the effect of optimizing a classifier for the Equal Opportunity metric.
Accelerating COVID-19 research with graph mining and transformer-based learning
In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19.
