Talks

In recent years, non-onsite symmetry representations on the
lattice have enabled new insights in both anomalous and non-invertible
symmetries.  In (1+1)D, these can be represented as matrix product
operators (MPO), a type of tensor network that captures the correlated
action on neighbouring sites.  In this talk, I will present the
underlying mathematical structures that govern these symmetries, and
show that these MPOs provide the lattice representation theory of
generalised symmetries in (1+1)D.  Time permitting, I will discuss some
applications of these ideas, in particular how they can be leveraged to
improve computational performance in state of the art in tensor network
algorithms.

Based on arXiv:2509.03600, arXiv:2408.06334

In this talk, I will argue that the universal structure of the corner algebra offers a new perspective on quantum gravity, in which the representation theory of this algebra plays a role as fundamental as that of the Poincaré algebra in quantum field theory. I will discuss the representation theory of the (quantum) universal and extended corner symmetries in a simplified model of spherically symmetric spacetimes, where the corner reduces to a point and the algebra correspondingly simplifies. Furthermore, I will propose a purely algebraic procedure for gluing regions in the quantum theory, compute the entanglement entropy, and derive the entropy–area law for a class of specific coherent states.

Achieving superpolynomial speedups for optimization has long been a central goal for quantum algorithms. I will discuss Decoded Quantum Interferometry (DQI), a quantum algorithm descended from Regev's reduction, that uses the quantum Fourier transform to reduce optimization problems to decoding problems. For approximating optimal polynomial fits over finite fields, DQI achieves a superpolynomial speedup over known classical algorithms. The speedup arises because the problem’s algebraic structure is reflected in the decoding problem, which can be solved efficiently. Whether DQI can also attain quantum advantage for algebraically unstructured optimization problems such as max-k-XORSAT remains an open question. One reason for optimism is that the sparsity of the clause structure in max-k-XORSAT is reflected in the dual decoding problem, which is for LDPC codes. I will describe some current lines of attack on this open question, as well as generalizations of DQI for preparing Gibbs states of Hamiltonians. This talk will target a broad audience without assuming deep background in quantum algorithms.

Decoded Quantum Interferometry (DQI) has been recently proposed as a new quantum algorithm for optimization. Hamiltonian Decoded Quantum Interferometry (HDQI), an extension of DQI, adapts this paradigm to Hamiltonian optimization and Gibbs state preparation. In this work, I will introduce HDQI for general Pauli Hamiltonians and discuss its application to the approximation of Gibbs states.