Výsledky vyhledávání - "Peddireddy, Dheeraj"

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Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks

Autor: Konar, Debanjan, Peddireddy, Dheeraj, Aggarwal, Vaneet, Panigrahi, Bijaya K.

Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization. However, the standard optimization techniques used for training the contracted trainable weights of ea

Externí odkaz: http://arxiv.org/abs/2310.01515

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Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization

Autor: Peddireddy, Dheeraj, Priyam, Utkarsh, Aggarwal, Vaneet

Variational Quantum algorithms, especially Quantum Approximate Optimization and Variational Quantum Eigensolver (VQE) have established their potential to provide computational advantage in the realm of combinatorial optimization. However, these algor

Externí odkaz: http://arxiv.org/abs/2307.03884

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Classical Simulation of Variational Quantum Classifiers using Tensor Rings

Autor: Peddireddy, Dheeraj, Bansal, Vipul, Aggarwal, Vaneet

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 the growing interest, it is pertinent to study the boundaries of the

Externí odkaz: http://arxiv.org/abs/2201.08878

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An FEA surrogate model with Boundary Oriented Graph Embedding approach

Autor: Fu, Xingyu, Zhou, Fengfeng, Peddireddy, Dheeraj, Kang, Zhengyang, Jun, Martin Byung-Guk, Aggarwal, Vaneet

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 and solving boundary value problems. Providing shortcuts for both bou

Externí odkaz: http://arxiv.org/abs/2108.13509

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Regret and Belief Complexity Trade-off in Gaussian Process Bandits via Information Thresholding

Autor: Bedi, Amrit Singh, Peddireddy, Dheeraj, Aggarwal, Vaneet, Sadler, Brian M., Koppel, Alec

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 uncertainty. In this work, we cast Bayesian optimization as a multi-armed ban

Externí odkaz: http://arxiv.org/abs/2003.10550

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