10 citations · 17 across the 3 of their papers we have counts for
5 papers
Systematic comparison of deep belief network training using quantum annealing vs. classical techniques
Joshua Job, Steve Adachi
In this work we revisit and expand on a 2015 study that used a D-Wave quantum annealer as a sampling engine to assist in the training of a Deep Neural Network. The original 2015 re…
Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer
João Caldeira, Joshua Job, Steven H. Adachi +2
We present the application of Restricted Boltzmann Machines (RBMs) to the task of astronomical image classification using a quantum annealer built by D-Wave Systems. Morphological…
Quantum adiabatic machine learning with zooming
Alexander Zlokapa, Alex Mott, Joshua Job +3
Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific applicat…
Test-driving 1000 qubits
Joshua Job, Daniel Lidar
Quantum computing is no longer a nascent field. Programmable quantum annealing devices with more that 1000 qubits are commercially available. How does one know that a putative quan…
Teleportation-based Fault-tolerant Quantum Computation in Multi-qubit Large Block Codes
Todd A. Brun, Yi-Cong Zheng, Kung-Chuan Hsu +2
A major goal for fault-tolerant quantum computation (FTQC) is to reduce the overhead needed for error correction. One approach is to use block codes that encode multiple qubits, wh…