51 citations · 154 across the 18 of their papers we have counts for
20 papers · 1 filter
Quantum Neural Network Training of a Repeater Node
Diego Fuentealba, Jack Dahn, James Steck +1
The construction of robust and scalable quantum gates is a uniquely hard problem in the field of quantum computing. Real-world quantum computers suffer from many forms of noise, ch…
A quantum GAN for entanglement detection and image classification
James E. Steck, Elizabeth C. Behrman
Machine learning can be used as a systematic method to non-algorithmically program quantum computers. Quantum machine learning enables us to perform computations without breaking d…
Programming Quantum Hardware via Levenberg Marquardt Machine Learning
James E. Steck, Nathan L. Thompson, Elizabeth C. Behrman
Significant challenges remain with the development of macroscopic quantum computing, hardware problems of noise, decoherence, and scaling, software problems of error correction, an…
A non-algorithmic approach to "programming" quantum computers via machine learning
Nathan Thompson, James Steck, Elizabeth Behrman
Major obstacles remain to the implementation of macroscopic quantum computing: hardware problems of noise, decoherence, and scaling; software problems of error correction; and, mos…
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…