6 papers · 1 filter
Why is topology hard to learn?
D. O. Oriekhov, Stan Bergkamp, Guliuxin Jin +5
Much attention has been devoted to the use of machine learning to approximate physical concepts. Yet, due to challenges in interpretability of machine learning techniques, the ques…
Every Benchmark All at Once
Ana Silva, Eliska Greplova
As quantum technology matures, the efficient benchmarking of quantum devices remains a key challenge. Although sample-efficient, information-theoretic benchmarking techniques have…
High-fidelity single-spin shuttling in silicon
Maxim De Smet, Yuta Matsumoto, Anne-Marije J. Zwerver +12
The computational power and fault-tolerance of future large-scale quantum processors derive in large part from the connectivity between the qubits. One approach to increase connect…
Hands-on Introduction to Randomized Benchmarking
Ana Silva, Eliska Greplova
Randomized benchmarking techniques have been an essential tool for assessing the performance of contemporary quantum devices. The goal of this tutorial is to provide a pedagogical,…
Quantum resources of quantum and classical variational methods
Thomas Spriggs, Arash Ahmadi, Bokai Chen +1
Variational techniques have long been at the heart of atomic, solid-state, and many-body physics. They have recently extended to quantum and classical machine learning, providing a…
Dynamical localization in 2D topological quantum random walks
D. O. Oriekhov, Guliuxin Jin, Eliska Greplova
We study the dynamical localization of discrete time evolution of topological split-step quantum random walk (QRW) on a single-site defect starting from a uniform distribution. Usi…