53 citations · 136 across the 8 of their papers we have counts for
9 papers
A Framework for Stochastic Differentiable Programming
Guillaume Verdon, Leo Tyrpak, Owen Lockwood +5
We introduce Parametrized Stochastic Circuits (PSCs), a gate-based intermediate representation for programmable stochastic dynamics in which typed local stochastic kernels with tun…
Non-Markov quantum belief propagation
Jack Ceroni, Ian MacCormack, Guillaume Verdon
We provide a rigorous proof of the approximate convergence of sliding-window quantum belief-propagation as outlined heuristically in the work of Bilgin and Poulin (Ref. [1]), in th…
Towards a scalable discrete quantum generative adversarial neural network
Smit Chaudhary, Patrick Huembeli, Ian MacCormack +3
We introduce a fully quantum generative adversarial network intended for use with binary data. The architecture incorporates several features found in other classical and quantum m…
Simulating Large PEPs Tensor Networks on Small Quantum Devices
Ian MacCormack, Alexey Galda, Adam L. Lyon
We systematically map low-bond-dimension PEPs tensor networks to quantum circuits. By measuring and reusing qubits, we demonstrate that a simulation of an square-latti…
Demonstration of quantum advantage by a joint detection receiver for optical communications using quantum belief propagation on a trapped-ion device
Conor Delaney, Kaushik P. Seshadreesan, Ian MacCormack +3
Demonstrations of quantum advantage have largely focused on computational speedups and on quantum simulation of many-body physics, limited by fidelity and capability of current dev…
Branching Quantum Convolutional Neural Networks
Ian MacCormack, Conor Delaney, Alexey Galda +2
Neural network-based algorithms have garnered considerable attention in condensed matter physics for their ability to learn complex patterns from very high dimensional data sets to…