233 citations · 531 across the 32 of their papers we have counts for
6 papers · 1 filter
Efficient classical algorithms for simulating symmetric quantum systems
Eric R. Anschuetz, Andreas Bauer, Bobak T. Kiani +1
In light of recently proposed quantum algorithms that incorporate symmetries in the hope of quantum advantage, we show that with symmetries that are restrictive enough, classical a…
Analog quantum variational embedding classifier
Rui Yang, Samuel Bosch, Bobak Kiani +2
Quantum machine learning has the potential to provide powerful algorithms for artificial intelligence. The pursuit of quantum advantage in quantum machine learning is an active are…
Joint Embedding Self-Supervised Learning in the Kernel Regime
Bobak T. Kiani, Randall Balestriero, Yubei Chen +2
The fundamental goal of self-supervised learning (SSL) is to produce useful representations of data without access to any labels for classifying the data. Modern methods in SSL, wh…
Beyond Barren Plateaus: Quantum Variational Algorithms Are Swamped With Traps
Eric R. Anschuetz, Bobak T. Kiani
One of the most important properties of classical neural networks is how surprisingly trainable they are, though their training algorithms typically rely on optimizing complicated,…
projUNN: efficient method for training deep networks with unitary matrices
Bobak Kiani, Randall Balestriero, Yann LeCun +1
In learning with recurrent or very deep feed-forward networks, employing unitary matrices in each layer can be very effective at maintaining long-range stability. However, restrict…
Block-encoding dense and full-rank kernels using hierarchical matrices: applications in quantum numerical linear algebra
Quynh T. Nguyen, Bobak T. Kiani, Seth Lloyd
Many quantum algorithms for numerical linear algebra assume black-box access to a block-encoding of the matrix of interest, which is a strong assumption when the matrix is not spar…