22 citations · 51 across the 13 of their papers we have counts for
9 papers · 1 filter
A measurement-driven quantum algorithm for SAT: Performance guarantees via spectral gaps and measurement parallelization
Franz J. Schreiber, Maximilian J. Kramer, Alexander Nietner +1
The Boolean satisfiability problem (SAT) is of central importance in both theory and practice. Yet, most provable guarantees for quantum algorithms rely exclusively on Grover-type…
Interactive proofs for verifying (quantum) learning and testing
Matthias C. Caro, Jens Eisert, Marcel Hinsche +3
We consider the problem of testing and learning from data in the presence of resource constraints, such as limited memory or weak data access, which place limitations on the effici…
Hamiltonian truncation tensor networks for quantum field theories
Philipp Schmoll, Jan Naumann, Alexander Nietner +2
Understanding the equilibrium properties and out of equilibrium dynamics of quantum field theories are key aspects of fundamental problems in theoretical particle physics and cosmo…
Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms
Alexander Nietner
Kearns' statistical query (SQ) oracle (STOC'93) lends a unifying perspective for most classical machine learning algorithms. This ceases to be true in quantum learning, where many…
Classical Verification of Quantum Learning
Matthias C. Caro, Marcel Hinsche, Marios Ioannou +2
Quantum data access and quantum processing can make certain classically intractable learning tasks feasible. However, quantum capabilities will only be available to a select few in…
Free Fermion Distributions Are Hard to Learn
Alexander Nietner
Free fermions are some of the best studied quantum systems. However, little is known about the complexity of learning free-fermion distributions. In this work we establish the hard…
On the average-case complexity of learning output distributions of quantum circuits
Alexander Nietner, Marios Ioannou, Ryan Sweke +4
In this work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model. This learning model is widely…
Learnability of the output distributions of local quantum circuits
Marcel Hinsche, Marios Ioannou, Alexander Nietner +6
There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two differe…
Efficient variational contraction of two-dimensional tensor networks with a non-trivial unit cell
A. Nietner, B. Vanhecke, F. Verstraete +2
Tensor network states provide an efficient class of states that faithfully capture strongly correlated quantum models and systems in classical statistical mechanics. While tensor n…