Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

A graph-state based synthesis framework for Clifford isometries

View through CrossRef
We tackle the problem of Clifford isometry compilation, i.e, how to synthesize a Clifford isometry into an executable quantum circuit. We propose a simple framework for synthesis that only exploits the elementary properties of the Clifford group and one equation of the symplectic group. We highlight the versatility of our framework by showing that several normal forms of the literature are natural corollaries. We recover the state of the art two-qubit gate depth necessary for the execution of a Clifford circuit on an LNN architecture, concomitantly with another work. We also propose practical synthesis algorithms for Clifford isometries with a focus on Clifford operators, graph states and codiagonalization of Pauli rotations. Benchmarks show that in all three cases we improve the 2-qubit gate count and depth of random instances compared to the state-of-the-art methods. We also improve the execution of practical quantum chemistry experiments.
Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
Title: A graph-state based synthesis framework for Clifford isometries
Description:
We tackle the problem of Clifford isometry compilation, i.
e, how to synthesize a Clifford isometry into an executable quantum circuit.
We propose a simple framework for synthesis that only exploits the elementary properties of the Clifford group and one equation of the symplectic group.
We highlight the versatility of our framework by showing that several normal forms of the literature are natural corollaries.
We recover the state of the art two-qubit gate depth necessary for the execution of a Clifford circuit on an LNN architecture, concomitantly with another work.
We also propose practical synthesis algorithms for Clifford isometries with a focus on Clifford operators, graph states and codiagonalization of Pauli rotations.
Benchmarks show that in all three cases we improve the 2-qubit gate count and depth of random instances compared to the state-of-the-art methods.
We also improve the execution of practical quantum chemistry experiments.

Related Results

Graph convolutional neural networks for 3D data analysis
Graph convolutional neural networks for 3D data analysis
(English) Deep Learning allows the extraction of complex features directly from raw input data, eliminating the need for hand-crafted features from the classical Machine Learning p...
Etude infinitésimale et asymptotique de certains flots stochastiques relativistes
Etude infinitésimale et asymptotique de certains flots stochastiques relativistes
Nous étudions certains processus de Lévy à valeurs dans les groupes d'isométries respectifs des espace-temps de Minkowski, de De Sitter et de Anti-De-Sitter. Le groupe d'isométries...
Anne Clifford
Anne Clifford
Lady Anne Clifford (b. 30 January 1590–d. 22 March 1676) spent a considerable portion of her life embroiled in lawsuits attempting to recover her father’s extensive land holdings a...
Bootstrapping a Biodiversity Knowledge Graph
Bootstrapping a Biodiversity Knowledge Graph
The "biodiversity knowledge graph" is a nice metaphor for connecting biodiversity data sources, but can we actually build it? Do we have sufficient linked data available? Given tha...
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract Accurately predicting drug sensitivity and understanding what is driving it are major challenges in drug discovery. Graphs are a natural framework for captu...
Efficient Unitary Designs with a System-Size Independent Number of Non-Clifford Gates
Efficient Unitary Designs with a System-Size Independent Number of Non-Clifford Gates
AbstractMany quantum information protocols require the implementation of random unitaries. Because it takes exponential resources to produce Haar-random unitaries drawn from the fu...
A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations
A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations
The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent gr...

Back to Top