dwave-samplers
Ocean-compatible collection of solvers/samplers.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; compiled wheels available for macOS, Windows, and Linux on x86_64 and ARM64 architectures.
- Medium install friction due to compiled wheels across multiple Python versions and platforms.
- Package is actively maintained with recent release activity and no known vulnerabilities.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 (permissive) allows commercial and derivative use with minimal restrictions; you must retain license notices and document modifications.
last release 2026-06-18 (57 days) · last repo commit 2026-08-04 · 20 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 117,453 downloads/mo, #12,161 on PyPI
Alternatives
Verify before relying
pip install dwave-samplers
from dwave.samplers import SimulatedAnnealingSampler
import dimod
sampler = SimulatedAnnealingSampler()
bqm = dimod.generators.gnp_random_bqm(100, 0.5, 'BINARY')
sampleset = sampler.sample(bqm)- Performance characteristics and scalability limits for large problem instances
- Comparison of solution quality across different samplers for specific problem classes
- Memory requirements and computational overhead relative to problem size
What it is and 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—Planar, Random, Simulated Annealing, Simulated Quantum Annealing, Steepest Descent, Tabu, and Tree Decomposition—each 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
dwave-samplers on PyPI
Before you install
Medium install friction due to compiled wheels across multiple Python versions and platforms. Package is actively maintained with recent release activity and no known vulnerabilities.
Requires Python 3.10 or later; compiled wheels available for macOS, Windows, and Linux on x86_64 and ARM64 architectures.
License in practice
Apache License 2.0 (permissive) allows commercial and derivative use with minimal restrictions; you must retain license notices and document modifications.
Quickstart
pip install dwave-samplers
from dwave.samplers import SimulatedAnnealingSampler
import dimod
sampler = SimulatedAnnealingSampler()
bqm = dimod.generators.gnp_random_bqm(100, 0.5, 'BINARY')
sampleset = sampler.sample(bqm)Verify before relying
- Performance characteristics and scalability limits for large problem instances
- Comparison of solution quality across different samplers for specific problem classes
- Memory requirements and computational overhead relative to problem size
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesdimodnetworkxnumpy |
| Maintenance | Actively maintained 57 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 117,453 / month, #12,161 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPython |
Evidence: dwave_samplers-1.8.0-cp310-cp310-macosx_10_9_x86_64.whl; dwave_samplers-1.8.0-cp310-cp310-macosx_11_0_arm64.whl; dwave_samplers-1.8.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; dwave_samplers-1.8.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; dwave_samplers-1.8.0-cp310-cp310-win_amd64.whl; dwave_samplers-1.8.0-cp311-cp311-macosx_10_9_x86_64.whl; dwave_samplers-1.8.0-cp311-cp311-macosx_11_0_arm64.whl; dwave_samplers-1.8.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; dwave_samplers-1.8.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; dwave_samplers-1.8.0-cp311-cp311-win_amd64.whl; dwave_samplers-1.8.0-cp312-abi3-macosx_10_13_x86_64.whl; dwave_samplers-1.8.0-cp312-abi3-macosx_11_0_arm64.whl; dwave_samplers-1.8.0-cp312-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; dwave_samplers-1.8.0-cp312-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; dwave_samplers-1.8.0-cp312-abi3-win_amd64.whl; dwave_samplers-1.8.0-cp314-cp314t-macosx_10_15_x86_64.whl; dwave_samplers-1.8.0-cp314-cp314t-macosx_11_0_arm64.whl; dwave_samplers-1.8.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; dwave_samplers-1.8.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; dwave_samplers-1.8.0-cp314-cp314t-win_amd64.whl
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See also dimod · dwave-graphs · dwave-optimization · dwave-cloud-client · dwave-networkx · quadprog · osqp · qpsolvers · directsearch · pyDOE3