Max-Cut
Emerging41papers using it
2023first seen
The Max-Cut problem is an optimization problem that involves partitioning a graph's vertices into two sets to maximize the number of edges between the sets, and it is used to evaluate the performance of quantum adiabatic algorithms in detecting multiple solutions.
Papers using Max-Cut (41)
- Benchmarking Variational Quantum Algorithms for Combinatorial Optimization in PracticeQAOA-GPT: Efficient Generation of Adaptive and Regular Quantum
Approximate Optimization Algorithm CircuitsNon-Variational Quantum Random Access Optimization with Alternating Operator AnsatzShadow measurements for feedback-based quantum optimizationLeveraging Landau-Zener-St\"uckelberg interference for accelerating diabatic quantum annealingInvestigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement LearningEQE-QAOA: An Equivalence-Preserving Qubit Efficient Framework for Combinatorial OptimizationBeyond Single Trajectories: Optimal Control and Jordan-Lie Algebra in Hybrid Quantum Walks for Combinatorial OptimizationIterative warm-start optimization with quantum imaginary time evolutionA Lyapunov Framework for Quantum Algorithm Design in Combinatorial Optimization with Approximation Ratio GuaranteesMeta-Learning for Quantum Optimization via Quantum Sequence ModelAn Information-Minimal Geometry for Qubit-Efficient OptimizationSampled-Based Guided Quantum Walk: Non-variational quantum algorithm for combinatorial optimizationA Depth-Independent Linear Chain Ansatz for Large-Scale Quantum Approximate OptimizationOn the role of overparametrization in Quantum Approximate OptimizationQuantum-Enhanced Optimization by Warm StartsTowards solving large QUBO problems using quantum algorithms: improving the LogQ schemeComparing performance of variational quantum algorithm simulations on HPC systemsScaling Portfolio Diversification with Quantum Circuit Cutting TechniquesLearning to Learn with Quantum Optimization via Quantum Neural NetworksSolving General QUBOs with Warm-Start QAOA via a Reduction to Max-CutA mixed-integer program for circuit execution time minimization with
precedence constraintsPerformance guarantees of light-cone variational quantum algorithms for the maximum cut problemWarm Start Adaptive-Bias Quantum Approximate Optimization AlgorithmDAPO-QAOA: An algorithm for solving combinatorial optimization problems
by dynamically constructing phase operatorsThe Questionable Influence of Entanglement in Quantum Optimisation AlgorithmsVariational Quantum Algorithms for Combinatorial OptimizationHierarchical Multigrid Ansatz for Variational Quantum AlgorithmsRecursive Quantum Relaxation for Combinatorial Optimization ProblemsClassical optimization with imaginary time block encoding on quantum computers: The MaxCut problemTight Lieb-Robinson Bound for approximation ratio in Quantum AnnealingPerformant near-term quantum combinatorial optimizationTowards Robust Benchmarking of Quantum Optimization AlgorithmsSymmetry-informed transferability of optimal parameters in the Quantum Approximate Optimization AlgorithmSequential Hamiltonian Assembly: Enhancing the training of combinatorial
optimization problems on quantum computersPhantom Edges in the Problem Hamiltonian: A Method for Increasing
Performance and Graph Visibility for QAOAAccuracy and Performance Evaluation of Quantum, Classical and Hybrid
Solvers for the Max-Cut ProblemInvestigating layer-selective transfer learning of QAOA parameters for Max-Cut problemSimulation of a feedback-based algorithm for quantum optimization for a
realistic neutral atom system with an optimized small-angle controlled-phase
gateOn the dynamical Lie algebras of quantum approximate optimization algorithmsImaginary Hamiltonian variational ansatz for combinatorial optimization problems