51 citations · 155 across the 8 of their papers we have counts for
10 papers
Quantum Computation of Reactions on Surfaces Using Local Embedding
Tanvi P. Gujarati, Mario Motta, Triet Nguyen Friedhoff +9
Modeling electronic systems is an important application for quantum computers. In the context of materials science, an important open problem is the computational description of ch…
Free-Will Arbitrary Time Consensus Protocols with Diffusive Coupling
Quoc Van Tran, Minh Hoang Trinh, Nam Hoai Nguyen +1
In this technical note, we first clarify a technical issue in the convergence proof of a free-will arbitrary time (FwAT) consensus law proposed recently in Pal et al. IEEE Trans. C…
Quantum Embedding Search for Quantum Machine Learning
Nam Nguyen, Kwang-Chen Chen
This paper introduces a novel quantum embedding search algorithm (QES, pronounced as "quest"), enabling search for optimal quantum embedding design for a specific dataset of intere…
A Quantum Annealing Approach for Dynamic Multi-Depot Capacitated Vehicle Routing Problem
Ramkumar Harikrishnakumar, Saideep Nannapaneni, Nam H. Nguyen +2
Quantum annealing (QA) is a quantum computing algorithm that works on the principle of Adiabatic Quantum Computation (AQC), and it has shown significant computational advantages in…
Experimental evaluation of quantum Bayesian networks on IBM QX hardware
Sima E. Borujeni, Nam H. Nguyen, Saideep Nannapaneni +2
Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Netwo…
Quantum circuit representation of Bayesian networks
Sima E. Borujeni, Saideep Nannapaneni, Nam H. Nguyen +2
Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems to perform various types of analysis such as probabilistic prediction, risk ana…
Experimental pairwise entanglement estimation for an N-qubit system :A machine learning approach for programming quantum hardware
N. L. Thompson, N. H. Nguyen, E. C. Behrman +1
Designing and implementing algorithms for medium and large scale quantum computers is not easy. In previous work we have suggested, and developed, the idea of using machine learnin…
Benchmarking neural networks for quantum computation
N. H. Nguyen, E. C. Behrman, M. A. Moustafa +1
The power of quantum computers is still somewhat speculative. While they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve ha…
Quantum Learning with Noise and Decoherence: A Robust Quantum Neural Network
Nam H. Nguyen, Elizabeth C. Behrman, James E. Steck
Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of…
Quantum neural computation of entanglement is robust to noise and decoherence
E. C. Behrman, N. H. Nguyen, J. E. Steck +1
In previous work, we have proposed an entanglement indicator for a general multiqubit state, which can be "learned" by a quantum system, acting as a neural network. The indicator c…