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quant-ph2025
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…
quant-ph2025
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…
quant-ph2025
Reducing QPU Calls in Variational Quantum Algorithms via Classical Offloading of Gradient Computation
Rahul Bhowmick, Harsh Wadhwa, Avinash Singh +3
Variational quantum algorithms (VQAs) are a promising approach for leveraging near-term quantum computers, but trainability and quantum-resource costs remain major challenges. Here…