5 papers
AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization
Harshkumar Oza, Aritra Sarkar, Syed Naqi Abbas +4
As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system…
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…
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…
Fast Gates of Detuned Cat Qubit
A. Schlabes, R. Bhowmick, M. H. Ansari
Cat qubits have emerged as a promising candidate for quantum computation due to their higher error-correction thresholds and low resource overheads. In existing literature, the det…