Speaker: Dr Nathan McMahon 
Affiliation: University of Erlangen-Nuremberg


The cluster-Ising model is an example of a quantum model with a symmetry protected topological (SPT) phase. For this model, the efficiency of performing phase recognition has recently been improved over measuring string order parameter (SOP) by the use of a particular quantum convolutional neural network (QCNN), which was motivated by renormalisation theory.

Unlike most neural networks, the function of the QCNN used here is relatively straightforward to explain. First, each layer of the QCNN performs a process analogous to both renormalisation and quantum error correction. Then second, the remainder of the circuit simply determines if we are in the ground state of a stabiliser Hamiltonian. If the energy is sufficiently low, we consider the input state to be in the target phase.

This QCNN also has a second feature, it is exactly equivalent to a constant depth quantum circuit + post-processing. Beyond just providing a cheaper circuit, this also points to the generalisation of phase recognising QCNNs beyond the cluster-Ising model. Combining these with the fidelity view of quantum phases, I will discuss the potential of QCNNs as a quantum information theory construction of renormalisation.

About Physics Seminars

The weekly Physics Seminar series focuses on a broad range of physics research within SMP, along with frequent presentations from visiting researchers. Seminars are usually scheduled for 1.00pm on Tuesdays.

The talks are typically more specialised than a colloquium but are often attended by staff and PhD students across a broad range of areas. Speakers are thus encouraged to include introductory material in the talks.

All SMP researchers and HDR students are encouraged to speak. Please email Glen Harris to register your interest.

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Previous recorded physics seminars 


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