{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Provides a collection of classical and hybrid optimization algorithms (simulated annealing, tabu search, steepest descent, and others) that solve binary quadratic models locally on CPU.","skillfed_tags":["optimization","quantum-inspired","local-solver"],"use_cases":["Prototype optimization algorithms locally before submitting to D-Wave quantum hardware or cloud services.","Establish classical baseline performance for comparison against quantum or hybrid approaches.","Solve small to medium-scale QUBO and Ising problems using heuristics like simulated annealing or tabu search.","Exact solution of planar Ising models or low-treewidth problems using specialized polynomial-time solvers.","Approximate Boltzmann sampling and thermal equilibrium exploration via simulated annealing with custom temperature schedules."],"what_it_does":"dwave-samplers is a collection of classical and quantum-inspired optimization solvers designed to work with binary quadratic models (BQMs) and QUBO problems. It provides seven different algorithms\u2014Planar, Random, Simulated Annealing, Simulated Quantum Annealing, Steepest Descent, Tabu, and Tree Decomposition\u2014each suited to different problem structures and use cases. The package runs entirely on your local CPU, making it useful for prototyping, baseline comparisons, and problems where remote quantum hardware is unavailable.\n\nThe solvers are built on top of dimod for problem representation and integrate with the D-Wave Ocean SDK ecosystem. They range from exact solvers (Planar, Tree Decomposition) for specialized problem structures to heuristic methods (Simulated Annealing, Tabu) for general optimization. Each sampler accepts a BQM or QUBO, applies its algorithm, and returns a SampleSet with solutions ranked by energy. The package depends on numpy and networkx for numerical and graph operations.","worth_installing":"Yes. The package is actively maintained, permissively licensed, has no known vulnerabilities, and provides a practical toolkit for local optimization of binary quadratic models. Install it if you work with BQMs, need classical baselines, or want to prototype before using D-Wave's quantum services. The medium install friction is typical for compiled packages and not a barrier."},"id":"dwave-samplers","links":{"html":"https://skillfed.io/packages/dwave-samplers","md":"https://skillfed.io/packages/dwave-samplers.md","pypi":"https://pypi.org/project/dwave-samplers/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-18","license_spdx":null,"license_treatment":"permissive","name":"dwave-samplers","python_support":"supports_current","summary":"Ocean-compatible collection of solvers/samplers."},"popularity":{"monthly_downloads":117453,"position":12161,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.8.0"}