Excursions at the Interface of Topological Phases of Matter and Quantum Error Correction

Topological quantum error-correcting codes are a family of stabilizer codes that are built using a lattice of qubits covering some manifold. The stabilizers of the code are local with respect to the underlying lattice, and logical information is encoded in the non-local degrees of freedom. The locality of stabilizers in these codes makes them especially suitable for experiments. From the condensed matter perspective, their code space corresponds to the ground state subspace of a local Hamiltonian belonging to a non-trivial topological phase of matter. The stabilizers of the code correspond to the Hamiltonian terms, and errors can be thought of as excitations above the ground state subspace. Conversely, one can use fixed point Hamiltonian of a topological phase of matter to define a topological quantum error-correcting code.This close connection has motivated numerous studies which utilize insights from one view- point to address questions in the other. This thesis further explores the possibilities in this di- rection. In the first two chapters, we present novel schemes to implement logical gates, which are motivated by viewing topological quantum error-correcting codes as topological phases of matter. In the third chapter, we show how the quantum error correction perspective could be used to realize robust topological entanglement phases in monitored random quantum circuits. And in the last chapter, we explore the possibility of extending this connection beyond topological quan- tum error-correcting codes. In particular, we introduce an order parameter for detecting k-local non-trivial states, which can be thought of as a generalization of topological states that includes codewords of any quantum error-correcting code

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition