You can run a first D-Wave optimization experiment without owning or installing a quantum computer. Create a Leap account and project, authenticate the Ocean SDK with that project’s API token, express a small problem as a binary quadratic model (BQM) or QUBO, and submit it to a Leap hybrid solver. Leap returns assignments and energies; you then check the lowest-energy candidate against the original constraints and objective.
What you need for a first Leap run
Leap is D-Wave’s cloud service for submitting problems to quantum processing units (QPUs) and quantum-classical hybrid solvers. Your Python program sends a model over the service; it does not connect to locally installed quantum hardware. The first successful run requires an account, a selected Leap project, an API token authorized for that project, and an Ocean SDK environment.
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- A Leap account and project: create or sign in to an account and select the project that will own the submission. Access, quotas, and available solvers are project-dependent.
- An API token: arbitrary problem submission requires a token. Trial projects may be restricted to specified demonstrations rather than unrestricted problem submission.
- Python or a compatible development environment: D-Wave supports local IDEs and cloud development environments that implement the Development Containers specification; its documentation recommends GitHub Codespaces as one option.
- The Ocean SDK: install the packages that provide BQM classes, samplers, composites, and the Leap client configuration.
- A small objective model: a BQM/QUBO is the most approachable starting point. Problems with richer constraints or variable types require another model and its matching sampler.
Keep the token out of source control, notebooks shared with other people, screenshots, and error reports. Store it through Ocean’s configuration workflow or the environment-variable method documented for the D-Wave Solver API (SAPI).
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D-Wave describes the process in two steps: formulate the problem as an objective function, then find good solutions by sampling. Ocean packages those steps into a model-and-sampler workflow:
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- Formulate: translate decisions, costs, and penalties into a supported objective model.
- Select a solver: choose a classical sampler, a Leap hybrid solver, or a QPU sampler.
- Sample: submit the model and receive candidate variable assignments, energies, and diagnostic fields.
- Validate: decode the assignment into business terms, check every original constraint, and compare its objective value with a suitable classical baseline.
The energy reported by a sampler is the value of the encoded mathematical objective, not automatically the value or validity of your real-world solution. A penalty may make an invalid state expensive, but your application must still test feasibility explicitly.
Choose the right first model and sampler
A BQM contains binary or spin variables and linear and quadratic terms. A QUBO is the binary form of that model, commonly written as a quadratic expression in variables restricted to 0 or 1. You use coefficients to represent the real objective and, when necessary, penalties for violating constraints.
| Problem shape | Typical Ocean model | Beginner submission route | Important consideration |
|---|---|---|---|
| Unconstrained binary decisions with linear and pairwise interactions | BQM or QUBO | LeapHybridSampler or the version-specific LeapHybridBQMSampler |
Encode all desired trade-offs in the objective; validate feasibility afterward. |
| Explicit constraints and possibly binary, integer, or other supported variables | Constrained quadratic model (CQM) | The Leap hybrid CQM sampler that is available to your project | Use a sampler that accepts CQM; a BQM sampler will reject the model. |
| Discrete variables with more than two values | Discrete quadratic model (DQM) | The matching Leap hybrid DQM sampler | Confirm the solver’s current variable and interaction requirements. |
| Higher-order or non-quadratic terms | Nonlinear model, where supported | The corresponding hybrid nonlinear sampler | Availability and limits vary; inspect the current solver list before submission. |
| Experiment that specifically requires QPU access | BQM/QUBO mapped to hardware topology | DWaveSampler wrapped by EmbeddingComposite |
Logical variables must be minor-embedded into the physical QPU graph. |
For a first experiment, a hybrid BQM submission is usually the least surprising path. Leap manages a cloud workflow that combines classical processing with quantum resources, so you can focus on modeling and result interpretation instead of hardware topology. Direct QPU use is appropriate when you need QPU-specific control or are studying embedding and sampling behavior.
Install Ocean and configure Leap authentication
- Create the environment. Use a virtual environment, a development container, or a supported cloud IDE. Keeping Ocean isolated avoids conflicts with unrelated Python packages.
- Run the interactive setup. In a terminal, run
dwave setup. The documented workflow can install the Ocean SDK, offer optional contributor tools, authorize Leap, and create the local configuration used by samplers. - Sign in and choose a project. Follow the authorization flow, then select the Leap project associated with the token you intend to use. A token belongs to a project; a valid token for a different project is not a substitute.
- Verify access before submitting a large job. Inspect the configured profile and the solvers visible to that project. Solver names, availability, quotas, and service policies can change, so use the current Leap dashboard and documentation rather than hard-coding an old solver identity.
- Protect the credential. Use Ocean’s configuration or the SAPI-supported environment-variable mechanism in deployment. Never commit the token to Git, place it in a notebook cell that will be published, or print the complete configuration in logs.
The referenced Ocean documentation identifies version 9.5.0. Treat that number as a dated reference, not a guarantee that it is the version installed in your environment. If an import or solver name differs, check the package reference and the solver list associated with your project.
Run a minimal BQM on a Leap hybrid solver
The following tiny model uses a helper that encodes the Boolean relation y1 = x1 AND x2. It is useful as a connectivity and authentication test because it is small enough to inspect manually.
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from dimod.generators import and_gate
from dwave.system import LeapHybridSampler
# A tiny BQM encoding y1 = x1 AND x2
bqm = and_gate("x1", "x2", "y1")
sampler = LeapHybridSampler()
sampleset = sampler.sample(bqm)
print(sampleset)Run the file from the same environment in which dwave setup created or selected your configuration. The returned binary sampleset has one column for each variable and an energy column. A typical first inspection is:
best = sampleset.first
print("assignment:", dict(best.sample))
print("energy:", best.energy)
print("occurrences:", best.num_occurrences)sampleset.first is ordered by the sampler’s reported energy, so it is a convenient candidate—not a proof that your application objective has been met. For the AND relation, decode the three bits and confirm that the output bit equals the logical AND of the two input bits.
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Submit your own small QUBO
A QUBO is commonly represented as a mapping from a variable or variable pair to a coefficient. The diagonal terms represent linear costs; off-diagonal terms represent pairwise interactions. The sampler minimizes the resulting energy. If your original problem is a maximization, convert it consistently—for example, by negating the objective before adding any penalties.
Here is a self-contained pattern using a dictionary-based QUBO. The coefficients are illustrative; replace them with values derived from your problem.
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from dwave.system import LeapHybridSampler
# Minimize: 2*x0 + 1*x1 - 3*x0*x1
Q = {
("x0", "x0"): 2.0,
("x1", "x1"): 1.0,
("x0", "x1"): -3.0,
}
sampler = LeapHybridSampler()
sampleset = sampler.sample_qubo(Q)
for row in sampleset.data(fields=["sample", "energy", "num_occurrences"]):
print(row)
best = sampleset.first.sample
print("best assignment:", dict(best))Before trusting the result, write down the original objective separately from the encoded QUBO. Recalculate the original objective from the returned assignment, check all hard constraints, and record any penalty violations. This catches sign errors, missing variables, incorrect penalty weights, and decoding mistakes that an energy value alone cannot reveal.
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Direct QPU submission adds a hardware-mapping step. Your logical QUBO graph rarely matches the QPU’s physical connectivity exactly, so an embedding composite maps logical variables to chains of physical qubits and manages the associated interactions.
from dwave.system import DWaveSampler, EmbeddingComposite
Q = {
("x0", "x0"): 2.0,
("x1", "x1"): 1.0,
("x0", "x1"): -3.0,
}
sampler = EmbeddingComposite(DWaveSampler())
sampleset = sampler.sample_qubo(Q, num_reads=5000)
print(sampleset)DWaveSampler selects a QPU according to your configured or explicitly selected solver. EmbeddingComposite performs the minor embedding from the logical problem to that QPU’s topology. The num_reads=5000 value is an example from the documented pattern, not a universal requirement or a promise of a particular runtime or solution quality.
Inspect QPU results for more than the lowest energy. Embedding-related fields and diagnostics can indicate broken chains or other mapping issues. Experiment with one change at a time—such as read count or an embedding-related setting—and keep the model, solver identity, and configuration with your result so the run can be reproduced while that solver remains available.
Hybrid versus direct QPU: a practical decision
| Decision point | Leap hybrid BQM route | Direct QPU route |
|---|---|---|
| Best first use | Learning the model/sample workflow or solving a small-to-medium supported model without hardware-mapping work | Studying QPU behavior, embedding, annealing parameters, or a workload that specifically calls for QPU access |
| Model support | Use the hybrid sampler matching BQM, CQM, DQM, or nonlinear input | Typically submit a QUBO/BQM accepted by the selected QPU sampler |
| Constraints | Some hybrid model types represent constraints natively; confirm the selected solver | Constraints generally must be converted into objective penalties before QPU submission |
| Embedding | Handled within the cloud hybrid workflow | Required for a general logical graph; use an embedding composite or an explicit embedding |
| Hardware controls | Less direct control over individual QPU parameters | More direct access to QPU sampling controls, subject to solver and API limits |
| Diagnostics | Inspect energies, samples, counts, and model-specific fields | Also inspect embedding, chain, and QPU timing diagnostics |
| Baseline development | Easy to compare with a local classical sampler while building the model | Still compare with a classical baseline; a QPU result is not automatically optimal |
| Availability and quota | Depends on the project and current hybrid solver policies | Depends on QPU access, project permissions, current solver availability, and QPU scheduling |
Validate a returned solution correctly
Use a validation function that knows the original problem, not just the encoded coefficients. A robust validation pass should:
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- Convert the sample into ordinary domain values, such as selected routes, machines, or items.
- Check every hard constraint independently, including capacity, assignment, coverage, and integrality rules.
- Recompute the original objective in its original units and direction.
- Separate penalty energy from business cost so you can tell whether a low-energy state is actually feasible.
- Compare the candidate with a classical sampler or exact method on small instances. Agreement is a useful modeling check even when the production problem will be larger.
- Inspect duplicate samples and occurrence counts; a frequently returned state can be useful evidence, but frequency is not a proof of optimality.
For constrained models, a feasible state with a slightly higher encoded energy may be preferable to an infeasible state with the lowest raw energy. The acceptance rule belongs to your application, so implement it explicitly rather than relying on row order alone.
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Authentication or authorization failure
Confirm that the active configuration points to the intended Leap project, that the token has not expired or been revoked, and that the token was not copied with extra whitespace. Re-run the Ocean authorization flow if necessary. Tokens are project-specific, so switching projects can make an otherwise valid token appear unauthorized.
No solver is available
Open the current Leap dashboard and inspect the solvers exposed to the project. Solver identities, maintenance windows, access rules, quotas, and training-plan policies change. Do not assume that a solver name from an old code sample remains available or has the same limits.
The model is rejected
Check the sampler’s accepted model type and requirements. A BQM sampler cannot accept a CQM, DQM, or nonlinear model merely because all of them are optimization problems. Also verify variable labels, coefficient types, constraint forms, and any size or time limits documented for that solver.
The import or class name is missing
Print the installed Ocean and dwave-system versions, then consult the current package reference. The BQM hybrid class name may differ by release, and a project may expose a different set of solver classes than an older tutorial.
The result is unexpected or infeasible
Run the same model with a classical sampler during development. Recalculate the original objective, inspect penalties and scaling, and verify that variable names decode to the intended domain fields. For QPU runs, inspect embedding and chain diagnostics. A poor result may be a modeling error, an inadequate penalty, an embedding problem, or ordinary heuristic variation.
The job times out or exceeds a project limit
Check the current project quota and solver limits, reduce the test instance, and submit asynchronously for production workloads rather than blocking an interactive process indefinitely. Preserve the problem identifier and implement polling, timeout handling, retries where safe, and clear failure reporting.
Turn the experiment into a dependable application
A script that prints a sampleset is a useful first test but not yet an integration. A production flow should separate model construction, submission, result retrieval, and validation.
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- Build and record the model: retain the objective, penalty weights, variable map, model type, and software version used for the submission.
- Select a solver at runtime: discover or configure an accessible solver rather than assuming a permanent solver identity.
- Submit asynchronously when appropriate: persist the submission identifier, poll for completion, and handle service errors, timeouts, and cancellation according to current SAPI guidance.
- Validate and rank results: filter infeasible samples, recompute the domain objective, and retain diagnostics alongside the selected assignment.
- Maintain a classical baseline: test small and representative instances locally so regressions in encoding or decoding are visible before cloud usage grows.
Keep credentials and result data under separate controls. A token should authorize service access; it should not be embedded in application data, model files, or logs that are shared with analysts.
A first-run checklist
- Leap account created and the correct project selected.
- Project token configured through Ocean setup or the documented SAPI environment mechanism.
- Ocean SDK installed in the environment that runs the script.
- Current project dashboard checked for accessible hybrid and QPU solvers.
- Problem expressed as the model type accepted by the chosen sampler.
- Hybrid BQM example run successfully before attempting direct QPU access.
- Lowest-energy sample decoded and checked against every original constraint.
- Original objective recomputed and compared with a classical baseline on small cases.
- For QPU work, embedding and chain diagnostics recorded with the result.
- For production, asynchronous handling, polling, timeouts, retries, and credential protection implemented.
The shortest reliable path is therefore: configure a Leap project, run a tiny BQM through the available hybrid sampler, validate the assignment in your own problem language, and only then move to a direct QPU submission when embedding or QPU-specific controls are part of the experiment.
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