Ising machines outperform every tested Potts machine on Max-k-Cut problems, with the performance gap widening from k=3 to k=4.
Mixed citations
Optimization by Simulated Annealing,
Mixed citation behavior. Most common role is background (67%).
18
Pith papers citing it
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Background
67% of classified citations
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background 5
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representative citing papers
EOS-Bench creates thousands of satellite scheduling test cases spanning small to large scales and evaluates multiple solver types across five performance metrics.
AI coding agents evolve simple ground-state protocols into improved versions for VQE, DMRG, and AFQMC on spin models and molecules by using executable energy scores under fixed compute budgets.
Four continuous relaxations turn non-differentiable coverage and revisit calculations into a fully differentiable pipeline that optimizes satellite orbits via gradients and outperforms metaheuristics.
Gaussian particles in a linearized Bures-Wasserstein space perform consensus optimization for variational inference and outperform deterministic gradient methods on low-dimensional non-log-concave targets.
Simulated annealing with seasonal sliced Wasserstein distance selects climate year subsets that are 2.5-3.5 times more representative than ENTSO-E practice and achieve 4-5 times effective sample size.
Introduces a beam-search heuristic for random subset sum that uses meshing to obtain inverse-quadratic expected error decay in linearithmic time.
Q-SFD, a QUBO formulation for simultaneous fragment docking with an added inter-fragment distance term, approximately doubles top-1 recovery of reconstruction-feasible pose pairs and places at least one feasible pair in the top-5 for over 90% of benchmark cases without losing pose accuracy.
A quantum MCMC algorithm leveraging the MBL phase and its thermal-to-localized transition to tune acceptance rates and sample thermal distributions on programmable quantum simulators for combinatorial optimization.
A quantum circuit computes the Gowers U2 norm using 3n qubits and O(n^2) gates to accelerate genetic search for bent Boolean functions, providing exponential advantage over classical O(2^{2n}) evaluation for n greater than 25.
HYPERHEURIST uses simulated annealing to refine functionally validated LLM-generated RTL designs, producing more stable PPA optimization than single-pass LLM generation across eight benchmarks.
Physical neural computing platforms using diverse materials offer complementary strengths for efficient on-device AI, with no single substrate excelling in all dimensions.
QAOA-based QuSO achieves end-to-end speedup over classical baselines for power grid unit commitment with up to 14 qubits using 16 layers in high-load scenarios via efficient classical pre-computation.
A framework combining linear mixed-effects models for player ratings and prices with multi-criteria optimization and auction simulation for football transfers, illustrated on 2018-19 Premier League data.
Graph contraction reduces TSP instances to smaller sub-problems solvable by quantum annealers, shown via Path Integral Monte Carlo simulation and D-Wave hardware.
A digital twin framework integrates agent-based decision support and metaheuristic optimization to dynamically model and optimize EV charging infrastructure, policies, and renewables in a Hanoi university campus setting.
GPU implementation of global optimization for logic model identification from time-course data achieves 33-1866% speedups over CPU baselines on two example regulatory networks.
RKO with tailored decoders yields competitive or superior solutions to commercial MIP solvers on constrained portfolio optimization and time-dependent TSP benchmarks.
citing papers explorer
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Comparative Study of Potts Machine Dynamics and Performance for Max-k-Cut
Ising machines outperform every tested Potts machine on Max-k-Cut problems, with the performance gap widening from k=3 to k=4.
-
EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling
EOS-Bench creates thousands of satellite scheduling test cases spanning small to large scales and evaluates multiple solver types across five performance metrics.
-
Optimizing ground state preparation protocols with autoresearch
AI coding agents evolve simple ground-state protocols into improved versions for VQE, DMRG, and AFQMC on spin models and molecules by using executable energy scores under fixed compute budgets.
-
Differentiable Satellite Constellation Configuration via Relaxed Coverage and Revisit Objectives
Four continuous relaxations turn non-differentiable coverage and revisit calculations into a fully differentiable pipeline that optimizes satellite orbits via gradients and outperforms metaheuristics.
-
Variational inference via Gaussian interacting particles in the Bures-Wasserstein geometry
Gaussian particles in a linearized Bures-Wasserstein space perform consensus optimization for variational inference and outperform deterministic gradient methods on low-dimensional non-log-concave targets.
-
Bridging the climate to energy data gap: simulated annealing for representative climate year selection
Simulated annealing with seasonal sliced Wasserstein distance selects climate year subsets that are 2.5-3.5 times more representative than ENTSO-E practice and achieve 4-5 times effective sample size.
-
Inverse Quadratic Decay in Random Subset Sum
Introduces a beam-search heuristic for random subset sum that uses meshing to obtain inverse-quadratic expected error decay in linearithmic time.
-
Simultaneous Fragment Docking for Geometrically Linkable Pose Pairs
Q-SFD, a QUBO formulation for simultaneous fragment docking with an added inter-fragment distance term, approximately doubles top-1 recovery of reconstruction-feasible pose pairs and places at least one feasible pair in the top-5 for over 90% of benchmark cases without losing pose accuracy.
-
Quantum Markov chain Monte Carlo method with programmable quantum simulators
A quantum MCMC algorithm leveraging the MBL phase and its thermal-to-localized transition to tune acceptance rates and sample thermal distributions on programmable quantum simulators for combinatorial optimization.
-
Quantum-Accelerated Gowers $U_2$ Norm for Bent Boolean Functions
A quantum circuit computes the Gowers U2 norm using 3n qubits and O(n^2) gates to accelerate genetic search for bent Boolean functions, providing exponential advantage over classical O(2^{2n}) evaluation for n greater than 25.
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HYPERHEURIST: A Simulated Annealing-Based Control Framework for LLM-Driven Code Generation in Optimized Hardware Design
HYPERHEURIST uses simulated annealing to refine functionally validated LLM-generated RTL designs, producing more stable PPA optimization than single-pass LLM generation across eight benchmarks.
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Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing
Physical neural computing platforms using diverse materials offer complementary strengths for efficient on-device AI, with no single substrate excelling in all dimensions.
-
End-to-End Speedup for Quantum Simulation-Based Optimization in Power Grid Management
QAOA-based QuSO achieves end-to-end speedup over classical baselines for power grid unit commitment with up to 14 qubits using 16 layers in high-load scenarios via efficient classical pre-computation.
-
Optimising football transfer strategy under budget constraints: A weighted multi-criteria approach
A framework combining linear mixed-effects models for player ratings and prices with multi-criteria optimization and auction simulation for football transfers, illustrated on 2018-19 Premier League data.
-
A Hybrid Classical-Quantum Annealing Algorithm for the TSP
Graph contraction reduces TSP instances to smaller sub-problems solvable by quantum annealers, shown via Path Integral Monte Carlo simulation and D-Wave hardware.
-
A Digital Twin Framework for Decision-Support and Optimization of EV Charging Infrastructure in Localized Urban Systems
A digital twin framework integrates agent-based decision support and metaheuristic optimization to dynamically model and optimize EV charging infrastructure, policies, and renewables in a Hanoi university campus setting.
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GPU-accelerated Modeling of Biological Regulatory Networks
GPU implementation of global optimization for logic model identification from time-course data achieves 33-1866% speedups over CPU baselines on two example regulatory networks.
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Applying a Random-Key Optimizer on Mixed Integer Programs
RKO with tailored decoders yields competitive or superior solutions to commercial MIP solvers on constrained portfolio optimization and time-dependent TSP benchmarks.