233 citations · 516 across the 26 of their papers we have counts for
6 papers · 1 filter
Fragmentation is Efficiently Learnable by Quantum Neural Networks
Mikhail Mints, Eric R. Anschuetz
In certain classes of physical quantum systems, the exponentially large state space "fragments" into many low-dimensional, dynamically disconnected subspaces. We introduce a learni…
Average-case quantum complexity from glassiness
Alexander Zlokapa, Bobak T. Kiani, Eric R. Anschuetz
Glassiness -- a phenomenon in physics characterized by a rough free-energy landscape -- implies hardness for stable classical algorithms. For example, it can obstruct constant-time…
Spin Glass Transitions Obstruct Decoded Quantum Interferometry
Eric R. Anschuetz, David Gamarnik, Jonathan Z. Lu
Quantum algorithms are believed to offer advantages in solving certain hard discrete optimization problems, yet identifying when such advantages persist in explicit distributions o…
k-Contextuality as a Heuristic for Memory Separations in Learning
Mariesa H. Teo, Willers Yang, James Sud +3
Classical machine learning models struggle with learning and prediction tasks on data sets exhibiting long-range correlations. Previously, the existence of a long-range correlation…
Optimizing Sparse SYK
Matthew Ding, Robbie King, Bobak T. Kiani +1
Finding the ground state of strongly-interacting fermionic systems is often the prerequisite for fully understanding both quantum chemistry and condensed matter systems. The Sachde…
Quantum Glassiness From Efficient Learning
Eric R. Anschuetz
We show a relation between quantum learning theory and algorithmic hardness. We use the existence of efficient, local learning algorithms for energy estimation -- such as the class…