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Quantum Annealing for Realistic Traffic Flow Optimization: Clustering and Data-Driven QUBO
Ren\'ata Rusn\'akov\'a, Martin Chovanec, Juraj Gazda
https://arxiv.org/abs/2510.06053

Quantum Annealing for Realistic Traffic Flow Optimization: Clustering and Data-Driven QUBO
Managing city traffic is a complex NP-hard problem where traditional methods often fail to scale. We present a data-driven approach that reformulates traffic optimization as a Quadratic Unconstrained Binary Optimization, capturing both congestion reduction and travel-time efficiency. The model integrates simulated realistic mobility data, multiple routing alternatives, and analytically derived penalty constraints. To address large networks, we apply Leiden clustering to preserve critical conges…

Computing Envy-Free up to Any Good (EFX) Allocations via Local Search
Simina Br\^anzei
https://arxiv.org/abs/2510.05429 https://arxiv.org/pdf/2510.05429

Computing Envy-Free up to Any Good (EFX) Allocations via Local Search
We present a simple local search algorithm for computing EFX (envy-free up to any good) allocations of $m$ indivisible goods among $n$ agents with additive valuations. EFX is a compelling fairness notion, and whether such allocations always exist remains a major open question in fair division. Our algorithm employs simulated annealing with the total number of EFX violations as an objective function together with a single-transfer neighborhood structure to move through the space of allocations…

Stability of graphene hyperbolic pseudospheres under harsh conditions
T. P. C. Klaver, R. Gabbrielli, V. Tynianska, A. Iorio, D. Legut
https://arxiv.org/abs/2510.04699 https://

Stability of graphene hyperbolic pseudospheres under harsh conditions
We demonstrate the high stability of simulated graphene hyperbolic pseudospheres under large externally imposed deformations and high temperature annealing. Hyperbolic pseudospheres are produced in a two-step Molecular Dynamics simulation process. First, carbon atoms are forced down a thin three-dimensional volume of a chosen shape. During this extrusion process the carbon atoms form a precursor to graphene that is unrealistically less stable than graphite or diamond. Then the unstable carbon s…

A Continuous Energy Ising Machine Leveraging Difference-of-Convex Programming
Debraj Banerjee, Santanu Mahapatra, Kunal Narayan Chaudhury
https://arxiv.org/abs/2509.01928 https:…

A Continuous Energy Ising Machine Leveraging Difference-of-Convex Programming
Many combinatorial optimization problems can be reformulated as the task of finding the ground state of a physical system, such as the Ising model. Most existing Ising solvers are inspired by simulated annealing. Although annealing techniques offer scalability, they lack convergence guarantees and are sensitive to the cooling schedule. We propose to solve the Ising problem by relaxing the binary spins to continuous variables and introducing a potential function (attractor) that steers the solut…

Freeze and Conquer: Reusable Ansatz for Solving the Traveling Salesman Problem
Fabrizio Fagiolo, Nicolo' Vescera
https://arxiv.org/abs/2508.21730 https://

Freeze and Conquer: Reusable Ansatz for Solving the Traveling Salesman Problem
In this paper we present a variational algorithm for the Traveling Salesman Problem (TSP) that combines (i) a compact encoding of permutations, which reduces the qubit requirement too, (ii) an optimize-freeze-reuse strategy: where the circuit topology (``Ansatz'') is first optimized on a training instance by Simulated Annealing (SA), then ``frozen'' and re-used on novel instances, limited to a rapid re-optimization of only the circuit parameters. This pipeline eliminates costly structural resea…

Solving quantum-inspired dynamics on quantum and classical annealers
Philipp Hanussek, Jakub Paw{\l}owski, Zakaria Mzaouali, Bart{\l}omiej Gardas
https://arxiv.org/abs/2509.03952

Solving quantum-inspired dynamics on quantum and classical annealers
We propose a practical benchmarking suite inspired by physical dynamics to challenge both quantum and classical computers. Using a parallel in time encoding, we convert the real-time propagator of an $n$-qubit, possibly non-Hermitian, Hamiltonian into a quadratic-unconstrained binary optimisation (QUBO) problem. The resulting QUBO instances are executed on D-Wave quantum annealers as well as using two classical solvers, Simulated Annealing and VeloxQ, a state-of-the-art classical heuristic solv…

Variables Ordering Optimization in Boolean Characteristic Set Method Using Simulated Annealing and Machine Learning-based Time Prediction
Minzhong Luo, Yudong Sun, Yin Long
https://arxiv.org/abs/2509.14754

Variables Ordering Optimization in Boolean Characteristic Set Method Using Simulated Annealing and Machine Learning-based Time Prediction
Solving systems of Boolean equations is a fundamental task in symbolic computation and algebraic cryptanalysis, with wide-ranging applications in cryptography, coding theory, and formal verification. Among existing approaches, the Boolean Characteristic Set (BCS) method[1] has emerged as one of the most efficient algorithms for tackling such problems. However, its performance is highly sensitive to the ordering of variables, with solving times varying drastically under different orderings for f…

Quantum and Simulated Annealing-Based Iterative Algorithms for QUBO Relaxations of the Sparsest $k$-Subgraph Problem
Omkar Bihani, Roman Ku\v{z}el, Janez Povh, Dunja Pucher
https://arxiv.org/abs/2509.08544

Quantum and Simulated Annealing-Based Iterative Algorithms for QUBO Relaxations of the Sparsest $k$-Subgraph Problem
In this paper, we introduce three QUBO (Quadratic Unconstrained Binary Optimization) relaxations for the sparsest $k$-subgraph (SkS) problem: a quadratic penalty relaxation, a Lagrangian relaxation, and an augmented Lagrangian relaxation. The effectiveness of these approaches strongly depends on the choice of penalty parameters. We establish theoretical results characterizing the parameter values for which the QUBO relaxations are exact. For practical implementation, we propose three iterative …

Replaced article(s) found for cond-mat.dis-nn. https://arxiv.org/list/cond-mat.dis-nn/new
[1/1]:
- Problem hardness of diluted Ising models: Population Annealing versus Simulated Annealing
Fernando Mart\'inez-Garc\'ia, Diego Porras

Quantum-Guided Cluster Algorithms for Combinatorial Optimization
Peter J. Eder, Aron Kerschbaumer, Jernej Rudi Fin\v{z}gar, Raimel A. Medina, Martin J. A. Schuetz, Helmut G. Katzgraber, Sarah Braun, Christian B. Mendl
https://arxiv.org/abs/2508.10656

Quantum-Guided Cluster Algorithms for Combinatorial Optimization
Finding the ground state of Ising spin glasses is notoriously difficult due to disorder and frustration. Often, this challenge is framed as a combinatorial optimization problem, for which a common strategy employs simulated annealing, a Monte Carlo (MC)-based algorithm that updates spins one at a time. Yet, these localized updates can cause the system to become trapped in local minima. Cluster algorithms (CAs) were developed to address this limitation and have demonstrated considerable success …