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/

Zendy Plus

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

Zendy Tools

Zendy Open

Finding the ground state of the Ising spin-glass is an important andchallenging problem (NP-hard, in fact) in condensed matter physics. However,its applications spread far beyond physic due to its deep relation to variouscombinatorial optimization problems, such as travelling salesman or proteinfolding. Sophisticated and promising new methods for solving Ising instancesrely on quantum resources. In particular, quantum annealing is a quantumcomputation paradigm, that is especially well suited for QuadraticUnconstrained Binary Optimization (QUBO). Nevertheless, commercially availablequantum annealers (i.e., D-Wave) are prone to various errors, and their abilityto find low energetic states (corresponding to solutions of superior quality)is limited. This naturally calls for a post-processing procedure to correcterrors (capable of lowering the energy found by the annealer). As aproof-of-concept, this work combines the recent ideas revolving around theDIRAC architecture with the Chimera topology and applies them in a real-worldsetting as an error-correcting scheme for quantum annealers. Our preliminaryresults show how to correct states output by quantum annealers usingreinforcement learning. Such an approach exhibits excellent scalability, as itcan be trained on small instances and deployed for large ones. However, itsperformance on the chimera graph is still inferior to a typical algorithm onecould incorporate in this context, e.g., simulated annealing.

Post-Error Correction for Quantum Annealing Processor using Reinforcement Learning