5 citations · 5 across the 1 of their papers we have counts for
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SupercheQ: Quantum Advantage for Distributed Databases
E. R. Anschuetz, P. Gokhale, B. Tonekaboni +18
We introduce Supercheq, a family of quantum protocols that achieves asymptotic advantage over classical protocols for checking the equivalence of files, a task also known as finger…
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…
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…
Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
Eric R. Anschuetz, Xun Gao
Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability;…
Q-CHOP: Quantum constrained Hamiltonian optimization
Michael A. Perlin, Ruslan Shaydulin, Benjamin P. Hall +7
Combinatorial optimization problems that arise in science and industry typically have constraints. Yet the presence of constraints makes them challenging to tackle using both class…
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…