kA-QAOA matches MA-QAOA approximation ratios on 3-uniform hypergraphs while using significantly fewer function evaluations.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
quant-ph 4verdicts
UNVERDICTED 4roles
method 1polarities
use method 1representative citing papers
A nonlinear custom penalty without slack variables plus CVaR sampling improves optimality gaps and consistency on knapsack instances for quantum constrained optimization.
Structured state preparation in QCQMC improves energy accuracy over pure variational methods across molecular, condensed-matter, nuclear, and graph problems.
A tunable mixing parameter p in random quantum circuits controls the transition from classically simulable to expressive quantum reservoir dynamics via entanglement and nonstabilizer content.
citing papers explorer
-
Structured Parameterization and Non-Stabilizerness in Hypergraph QAOA
kA-QAOA matches MA-QAOA approximation ratios on 3-uniform hypergraphs while using significantly fewer function evaluations.
-
CVaR-Assisted Custom Penalty Function for Constrained Optimization
A nonlinear custom penalty without slack variables plus CVaR sampling improves optimality gaps and consistency on knapsack instances for quantum constrained optimization.
-
A unified quantum computing quantum Monte Carlo framework through structured state preparation
Structured state preparation in QCQMC improves energy accuracy over pure variational methods across molecular, condensed-matter, nuclear, and graph problems.
-
Optimal quantum reservoir learning in proximity to universality
A tunable mixing parameter p in random quantum circuits controls the transition from classically simulable to expressive quantum reservoir dynamics via entanglement and nonstabilizer content.