Ising machines outperform every tested Potts machine on Max-k-Cut problems, with the performance gap widening from k=3 to k=4.
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Optimization by Simulated Annealing
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- method distance term changes the solution distribution. Before reconstruction, one-hot valid- ity and anchor completeness were explicitly verified so that invalid decoded states were not interpreted as geometric solutions. 3.6 Solvers, baselines, and resource logging Primary solver (simulated annealing). The main Q-SFD benchmark used classical simulated annealing (SA) [32] with mul- tiple reads; the top-5 solutions were retained for each case. Retaining a pool of near-optimal states allows downstream r
- background space of combinatorial optimization problems (COPs) can grow faster than exponentially in the number of objects, making exhaustive search impractical [ 6]. As a result, several algorithms and hardware accelerators have been developed over the years which aim to provide high-quality solutions with minimal consumption of resources such as time and energy [7, 8]. Ising machines (IMs) [9, 10, 11, 8] are hardware accelera- tors designed to find low-energy states of theIsing model of statistical mecha
- background Classical simulations and hybrid classical-quantum algorithms can be a useful approach to overcome the physical limitations of current Noisy Intermediate-Scale Quantum (NISQ) [38] devices. Our technique to solve the TSP is based on the classical sim- ulation of Quantum Annealing (QA) [ 29] via the Path Integral Monte Carlo (PIMC) method [34]. In particular, Martoňák et al. [35] proposed a PIMC quantum annealing scheme based on a highly constrained Ising-like representation of the TSP. While thei
- background straints into molecular binding and then letting the sys- tem settle [24]. In engineered hardware, analogous solver behavior appears in systems whose dynamics minimize an implicit cost function, such as spin glasses or elastic networks that relax to reduce frustration or stress. The connection between annealing in statistical physics and combinatorial optimization was formalized by Kirkpatrick et al. [25]. Conceptually, these systems admit a scalar "energy" (or Hamiltonian) that acts as a Lyapun
- background softx.2022.101109. [100] X. Wang, C. Han, R. Leus, Scheduling multiple agile earth observation satellites with multiple observations, Advances in Space Research (2025). doi: 10.1016/j.asr.2025.10. 042. [101] S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, Optimization by simulated annealing, Science 220 (1983) 671-680. doi: 10.1126/science.220.4598.671. [102] G. Wu, H. Wang, W. Pedrycz, et al., Satellite observation scheduling with a novel adaptive simulated annealing algorithm and a dynamic task cl
- background Journal of the Physical Society of Japan, 5(6): 435-439, 1950.doi:10.1143/JPSJ.5.435. [46] Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Michael Sipser. Quantum computation by adiabatic evolution, 2000. [47] S. Kirkpatrick, C. D. Gelatt Jr., and M. P. Vecchi. Optimization by simulated annealing.Science, 220(4598): 671-680, 1983.doi:10.1126/science.220.4598.671. [48] D. R. Hartree. The wave mechanics of an atom with a non-coulomb central field. part ii. some results and discussion.Mathematica
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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.
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.
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.
-
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.
-
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.
-
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.
-
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.
- Quantum-Accelerated Gowers $U_2$ Norm for Bent Boolean Functions
- Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing