no code implementations • 21 Jun 2022 • David Von Dollen, Sheir Yarkoni, Daniel Weimer, Florian Neukart, Thomas Bäck
We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization.
no code implementations • 8 Apr 2021 • David Von Dollen, Florian Neukart, Daniel Weimer, Thomas Bäck
Within machine learning model evaluation regimes, feature selection is a technique to reduce model complexity and improve model performance in regards to generalization, model fit, and accuracy of prediction.
no code implementations • 19 Jun 2020 • Sheir Yarkoni, Andrii Kleshchonok, Yury Dzerin, Florian Neukart, Marc Hilbert
We accomplish this by discretizing the TS and converting the reconstruction to a set cover problem, allowing us to perform a one-versus-all method of reconstruction.
1 code implementation • 8 Apr 2020 • Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Rami Barends, Sergio Boixo, Michael Broughton, Bob B. Buckley, David A. Buell, Brian Burkett, Nicholas Bushnell, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Sean Demura, Andrew Dunsworth, Daniel Eppens, Edward Farhi, Austin Fowler, Brooks Foxen, Craig Gidney, Marissa Giustina, Rob Graff, Steve Habegger, Matthew P. Harrigan, Alan Ho, Sabrina Hong, Trent Huang, L. B. Ioffe, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Cody Jones, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Seon Kim, Paul V. Klimov, Alexander N. Korotkov, Fedor Kostritsa, David Landhuis, Pavel Laptev, Mike Lindmark, Martin Leib, Erik Lucero, Orion Martin, John M. Martinis, Jarrod R. McClean, Matt McEwen, Anthony Megrant, Xiao Mi, Masoud Mohseni, Wojciech Mruczkiewicz, Josh Mutus, Ofer Naaman, Matthew Neeley, Charles Neill, Florian Neukart, Hartmut Neven, Murphy Yuezhen Niu, Thomas E. O'Brien, Bryan O'Gorman, Eric Ostby, Andre Petukhov, Harald Putterman, Chris Quintana, Pedram Roushan, Nicholas C. Rubin, Daniel Sank, Kevin J. Satzinger, Andrea Skolik, Vadim Smelyanskiy, Doug Strain, Michael Streif, Kevin J. Sung, Marco Szalay, Amit Vainsencher, Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Leo Zhou
For problems defined on our hardware graph we obtain an approximation ratio that is independent of problem size and observe, for the first time, that performance increases with circuit depth.
Quantum Physics
no code implementations • 13 Nov 2018 • Michael Streif, Florian Neukart, Martin Leib
Additionally we investigate the scaling in terms of needed physical qubits on a quantum annealer with limited connectivity.
Quantum Physics
no code implementations • 6 Sep 2017 • Martin Hofmann, Florian Neukart, Thomas Bäck
Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future.
1 code implementation • 4 Aug 2017 • Florian Neukart, Gabriele Compostella, Christian Seidel, David Von Dollen, Sheir Yarkoni, Bob Parney
Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems.
Quantum Physics Data Structures and Algorithms