1 citations · 2 across the 7 of their papers we have counts for
7 papers
Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing
Yash Vardhan Tomar, Dheeraj Peddireddy
Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors, particularly superconducting devices whose sparse fixed coupling makes…
SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning
Yash Vardhan Tomar, Dheeraj Peddireddy
Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next experiment is itself a computation. Bayesia…
Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks
Debanjan Konar, Dheeraj Peddireddy, Vaneet Aggarwal +1
Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization. However, the standard optimizati…
Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization
Dheeraj Peddireddy, Utkarsh Priyam, Vaneet Aggarwal
Variational Quantum algorithms, especially Quantum Approximate Optimization and Variational Quantum Eigensolver (VQE) have established their potential to provide computational adva…
Classical Simulation of Variational Quantum Classifiers using Tensor Rings
Dheeraj Peddireddy, Vipul Bansal, Vaneet Aggarwal
In recent times, Variational Quantum Circuits (VQC) have been widely adopted to different tasks in machine learning such as Combinatorial Optimization and Supervised Learning. With…
An FEA surrogate model with Boundary Oriented Graph Embedding approach
Xingyu Fu, Fengfeng Zhou, Dheeraj Peddireddy +3
In this work, we present a Boundary Oriented Graph Embedding (BOGE) approach for the Graph Neural Network (GNN) to serve as a general surrogate model for regressing physical fields…
Regret and Belief Complexity Trade-off in Gaussian Process Bandits via Information Thresholding
Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal +2
Bayesian optimization is a framework for global search via maximum a posteriori updates rather than simulated annealing, and has gained prominence for decision-making under uncerta…