Just lately, useful quantum computer systems grew to become obtainable for the analysis group. They allow researchers to research the applying of quantum computing on numerous pc imaginative and prescient duties.

A latest research seems into combinatorial graph matching, a elementary drawback of visible computing.

{Photograph} of a chip constructed by D-Wave Techniques Inc., designed to function as a 128-qubit superconducting adiabatic quantum optimization processor, mounted in a pattern holder. Picture credit score: D-Wave Techniques Inc., License: Artistic Commons Attribution 3.0 through Wikiwand

The researchers present how a quadratic project drawback, an NP-hard drawback, which is an important a part of matching issues, may be effectively solved with quantum annealing for small drawback cases. It opens the best way for a number of drawback varieties in 3D pc imaginative and prescient.

The numerical verification in simulations and on an actual adiabatic quantum pc was carried out. It’s proven that the proposed method successfully will increase the success price of fixing combinatorial optimization issues with permutation matrix constraints.

Matching issues on 3D shapes and pictures are difficult as they’re incessantly formulated as combinatorial quadratic project issues (QAPs) with permutation matrix constraints, that are NP-hard. On this work, we tackle such issues with rising quantum computing know-how and suggest a number of reformulations of QAPs as unconstrained issues appropriate for environment friendly execution on quantum {hardware}. We examine a number of methods to inject permutation matrix constraints in a quadratic unconstrained binary optimization drawback which may be mapped to quantum {hardware}. We give attention to acquiring a adequate spectral hole, which additional will increase the chance to measure optimum options and legitimate permutation matrices in a single run. We carry out our experiments on the quantum pc D-Wave 2000Q (2^11 qubits, adiabatic). Regardless of the noticed discrepancy between simulated adiabatic quantum computing and execution on actual quantum {hardware}, our reformulation of permutation matrix constraints will increase the robustness of the numerical computations over different penalty approaches in our experiments. The proposed algorithm has the potential to scale to greater dimensions on future quantum computing architectures, which opens up a number of new instructions for fixing matching issues in 3D pc imaginative and prescient and graphics.

Analysis paper: Seelbach Benkner, M., Golyanik, V., Theobalt, C., and Moeller, M., “Adiabatic Quantum Graph Matching with Permutation Matrix Constraints”, 2021. Hyperlink: https://arxiv.org/abs/2107.04032

Hyperlink to the undertaking web page: https://gvv.mpi-inf.mpg.de/projects/QGM/




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