English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Scaling the size of monolithic quantum computer systems is a difficult task.As the number of qubits within a device increases, a number of factorscontribute to decreases in yield and performance. To meet this challenge,distributed architectures composed of many networked quantum computers havebeen proposed as a viable path to scalability. Such systems will needalgorithms and compilers that are tailored to their distributed architectures.In this work we introduce the Quantum Divide and Conquer Algorithm (QDCA), ahybrid variational approach to mapping large combinatorial optimizationproblems onto distributed quantum architectures. This is achieved through thecombined use of graph partitioning and quantum circuit cutting. The QDCA, anexample of application-compiler co-design, alters the structure of thevariational ansatz to tame the exponential compilation overhead incurred byquantum circuit cutting.  The result of this cross-layer co-design is a highly flexible algorithm whichcan be tuned to the amount of classical or quantum computational resources thatare available, and can be applied to both near- and long-term distributedquantum architectures. We simulate the QDCA on instances of the MaximumIndependent Set problem and find that it is able to outperform similarclassical algorithms. We also evaluate an 8-qubit QDCA ansatz on asuperconducting quantum computer and show that circuit cutting can help tomitigate the effects of noise. Our work demonstrates how many small-scalequantum computers can work together to solve problems $85\%$ larger than theirown qubit count, motivating the development and potential of large-scaledistributed quantum computing.

Divide and Conquer for Combinatorial Optimization and Distributed Quantum Computation