202 citations · 2.5k across the 68 of their papers we have counts for
12 papers · 1 filter
An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory
Niklas Pirnay, Vincent Ulitzsch, Frederik Wilde +2
Combinatorial optimization - a field of research addressing problems that feature strongly in a wealth of scientific and industrial contexts - has been identified as one of the cor…
Finite temperature tensor network algorithm for frustrated two-dimensional quantum materials
Philipp Schmoll, Christian Balz, Bella Lake +2
Aimed at a more realistic classical description of natural quantum systems, we present a two-dimensional tensor network algorithm to study finite temperature properties of frustrat…
Exponentially tighter bounds on limitations of quantum error mitigation
Yihui Quek, Daniel Stilck França, Sumeet Khatri +2
Quantum error mitigation has been proposed as a means to combat unwanted and unavoidable errors in near-term quantum computing without the heavy resource overheads required by faul…
Stochastic noise can be helpful for variational quantum algorithms
Junyu Liu, Frederik Wilde, Antonio Anna Mele +3
Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided for example by means of stochas…
Perturbative gadgets for gate-based quantum computing: Non-recursive constructions without subspace restrictions
Simon Cichy, Paul K. Faehrmann, Sumeet Khatri +1
Perturbative gadgets are a tool to encode part of a Hamiltonian, usually the low-energy subspace, into a different Hamiltonian with favorable properties, for instance, reduced loca…
A quantum inspired approach to learning dynamical laws from data -- block-sparsity and gauge-mediated weight sharing
J. Fuksa, M. Götte, I. Roth +1
Recent years have witnessed an increased interest in recovering dynamical laws of complex systems in a largely data-driven fashion under meaningful hypotheses. In this work, we pro…