4 papers · 1 filter
Model selection in hybrid quantum neural networks with applications to quantum transformer architectures
Harsh Wadhwa, Rahul Bhowmick, Naipunnya Raj +3
Quantum machine learning models generally lack principled design guidelines, often requiring full resource-intensive training across numerous choices of encodings, quantum circuit…
Meta-learning of Gibbs states for many-body Hamiltonians with applications to Quantum Boltzmann Machines
Ruchira V Bhat, Rahul Bhowmick, Avinash Singh +1
The preparation of quantum Gibbs states is a fundamental challenge in quantum computing, essential for applications ranging from modeling open quantum systems to quantum machine le…
Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation
Naipunnya Raj, Rajiv Sangle, Avinash Singh +1
In this work, we introduce the Quantum Generative Adversarial Autoencoder (QGAA), a quantum model for generation of quantum data. The QGAA consists of two components: (a) Quantum A…
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware
Rahul Bhowmick, Harsh Wadhwa, Avinash Singh +3
Quantum computers offer a promising route to tackling problems that are classically intractable such as in prime-factorization, solving large-scale linear algebra and simulating co…