Analytical reweighting rules for QAOA parameters on hypergraphs improve performance by adjusting mixing terms beyond previous graph-based methods.
Fixed-angle conjectures for the quantum approximate optimization algorithm on regular maxcut graphs
7 Pith papers cite this work, alongside 67 external citations. Polarity classification is still indexing.
7
Pith papers citing it
67
external citations · OpenAlex
citation-role summary
background 2
citation-polarity summary
fields
quant-ph 7roles
background 2polarities
background 2representative citing papers
Exhaustively parametrised feasibility-respecting quantum circuits can reach every feasible solution to problems like TSP with certainty using fixed parameters by leveraging group actions and generating sequences.
Iterative-QAOA solves pangenome assembly instances on current quantum hardware by using a fixed-ramp QAOA schedule with warm-start updates and a new HUBO encoding that cuts variables from O(N^{2}) to O(N log N).
Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.
Adaptive min-vertex-cut qubit freezing plus bias folding makes DC-QAOA partition every graph (to n=10k) while matching quality on separable instances and beating QAOA2 at equal coverage.
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
citing papers explorer
-
QAOA Parameter Transfer for Hypergraphs
Analytical reweighting rules for QAOA parameters on hypergraphs improve performance by adjusting mixing terms beyond previous graph-based methods.
-
Exhaustive and feasible parametrisation with applications to the travelling salesperson problem
Exhaustively parametrised feasibility-respecting quantum circuits can reach every feasible solution to problems like TSP with certainty using fixed parameters by leveraging group actions and generating sequences.
-
Nonvariational quantum optimisation approaches to pangenome-guided sequence assembly
Iterative-QAOA solves pangenome assembly instances on current quantum hardware by using a fixed-ramp QAOA schedule with warm-start updates and a new HUBO encoding that cuts variables from O(N^{2}) to O(N log N).
-
Iceberg Beyond the Tip: Co-Compilation of a Quantum Error Detection Code and a Quantum Algorithm
Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.
-
Adaptive Qubit Freezing Enables Robust Graph Partitioning for Divide-and-Conquer QAOA
Adaptive min-vertex-cut qubit freezing plus bias folding makes DC-QAOA partition every graph (to n=10k) while matching quality on separable instances and beating QAOA2 at equal coverage.
-
Sampling (noisy) quantum circuits through randomized rounding
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
-
Going off Pattern? QAOA Parameter Heuristics and Potentials of Parsimony
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.