74 citations · 228 across the 19 of their papers we have counts for
22 papers
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…
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
Debanjan Konar, Siddhartha Bhattacharyya, Bijaya K. Panigrahi +1
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bi-level quantum bits often describe qu…
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…
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…