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This research addresses the locality bottleneck in shallow-depth Quantum Approximate Optimization Algorithm (QAOA) circuits by introducing transport-augmented mixers with optimized shortcut couplings. The approach effectively collapses interaction-graph diameters, rendering QAOA performance size-invariant for sparse MaxCut instances without altering the cost Hamiltonian. Benchmarks demonstrate significant gains in approximation ratios across bipartite families and random trees compared to existing methods like ma-QAOA.
Surse care acoperăarXiv, Quantum Zeitgeist, Quantum Computing Report, Proton Blog
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Dealing with locality in QAOA

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2606.14447v2 Announce Type: replace Abstract: Shallow-depth QAOA on sparse, high-diameter MaxCut instances faces a locality bottleneck: at depth \(p\), local observables can depend only on a bounded neighborhood of the circuit interaction graph. We propose a transport-augmented QAOA that keeps the MaxCut cost Hamiltonian unchanged but enriches the mixer with optimized, unweighted shortcut couplings (scheduled \(XX+YY\)) to collapse the effective interaction-graph diameter. Using exact finite-depth support recursions, we relate optimal shortcut placement to bounded-diameter graph augmentation, and show in benchmarks that (unlike ma-QAOA) performance becomes effectively size-invariant once the diameter is reduced. For bipartite families (base diameter 4), reducing the interaction path to \(d=1\) raises the ensemble-averaged approximation ratio from 0.7378 (ma-QAOA) to 0.9767 at \(p=1\) (\(\sigma=0.0251\), nine system sizes); on random trees (base diameter 10), at \(p=2\) it improves from 0.9226 to 0.9997 (\(\sigma=0.0001\)).

Abstract:Shallow-depth QAOA on sparse, high-diameter MaxCut instances faces a locality bottleneck: at depth \(p\), local observables can depend only on a bounded neighborhood of the circuit interaction graph. We propose a transport-augmented QAOA that keeps the MaxCut cost Hamiltonian unchanged but enriches the mixer with optimized, unweighted shortcut couplings (scheduled \(XX+YY\)) to collapse the effective interaction-graph diameter. Using exact finite-depth support recursions, we relate optimal shortcut placement to bounded-diameter graph augmentation, and show in benchmarks that (unlike ma-QAOA) performance becomes effectively size-invariant once the diameter is reduced. For bipartite families (base diameter 4), reducing the interaction path to \(d=1\) raises the ensemble-averaged approximation ratio from 0.7378 (ma-QAOA) to 0.9767 at \(p=1\) (\(\sigma=0.0251\), nine system sizes); on random trees (base diameter 10), at \(p=2\) it improves from 0.9226 to 0.9997 (\(\sigma=0.0001\)).

arXiv:2606.14447 [quant-ph]

(or arXiv:2606.14447v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2606.14447 Focus to learn more arXiv-issued DOI via DataCite

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