Reproduce “Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Amana Liaqat, Ahmed Darwish, Stephen DiAdamo, Dan Holme, Kieran McDowall, Emre Sahin, Naeimeh Mohseni, Giorgio Cortiana and 1 more
The authors present a distributed quantum-assisted workflow for sparse Birkhoff decomposition of network demands. It partitions residual graphs into edge-bounded subproblems, samples candidate connections with QAOA, stitches and repairs them, then updates weights classically. On the 1,354-bus PEGASE grid, packed hardware ran about 3× faster, with higher final residual error; the results do not establish quantum advantage.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.