Backends
Available Backends
To check available backends we will create a StrangeworksOptimizer object and call the backends method. We can print the names of the available backends with the following code:
from strangeworks_optimization import StrangeworksOptimizer
so = StrangeworksOptimizer()
backends = so.backends()
for backend in backends:
print(backend.name)
Here's are some available backend samplers supported through Strangeworks Optimization:
| Solver | Description | Backends (see catalog for complete list) |
|---|---|---|
| Aquila | Aquila is QuEra's 256-qubit neutral-atom quantum computer. It operates as an analog Hamiltonian simulator on a user-configurable architecture, executing programmable coherent quantum dynamics on up to 256 neutral-atom qubits | braket.aquila |
| D-Wave | D-Wave's quantum annealing hardware relies on metal loops of niobium that have tiny electrical currents running through them. | dwave.Advantage_system4.1, dwave.Advantage_system6.4, dwave.Advantage2_4, dwave.hybrid_binary_quadratic_model_version2p, dwave.hybrid_discrete_quadratic_model_version1p, dwave.hybrid_constrained_quadratic_model_version1p, dwave.SimulatedAnnealingSampler, dwave.RandomSampler |
| Fujitsu | Fujitsu's Digital Annealer provides an alternative to quantum computing technology, which is at present both very expensive and difficult to run. | fujitsu.DA3 |
| Gurobi | Gurobi is an industry-leading solver for mathematical optimization | gurobi.qubo, gurobi.mps |
| Hitachi | CMOS annealing machine is a non-Neumann architecture computer that Hitachi has developed by utilizing the structure of SRAM, a storage device, to perform optimization processing with the Ising model. | hitachi.cmos_annealer |
| InfinityQ | InfinityQ offers the quantum inspired TitanQ solver | infinityq.titanq |
| JiJ | JiJ is part of the Strangeworks Syndicate and is working on developing quantum annealing devices | jij.SA, jij.SQA, jij.LeapHybridCQM |
| Lightsolver | Lightsolver is a quantum annealing solver that uses a hybrid quantum-classical approach to solve optimization problems | lightsolver.lasermind |
| NEC | NEC is developing quantum annealers using superconducting parametron qubits with a greater number of all-to-all connected qubits. | nec.vector_annealer |
| Quantagonia | Quantagonia offers a Hybrid Quantum Platform for solving complex computational problems | quantagonia.qubo, quantagonia.mps |
| Toshiba | Toshiba's Simulated Bifurcation Machine (SBM) is a quantum-inspired optimization algorithm that can be run on classical hardware | toshibo.qubo, toshibo.qplib, toshiba.pubo |
Note: This list is not exhaustive. For a complete list of backends, see the catalog or reach out via Slack here.
Solver Parameters
Particular solvers may have specific parameters that can be set.
For example, the D-Wave sampler has a num_reads parameter that can be set to specify the number of samples to generate.
The Gurobi sampler has a time_limit parameter that can be set to specify the maximum time to run the solver and so on.
To set the parameters for a particular solver, we can pass options to the StrangeworksOptimizer during initialization.
from strangeworks_optimization import StrangeworksOptimizer
...
so = StrangeworksOptimizer(model=model,
solver=solver,
options=options)
sw_job = so.run()
Each sampler has its own Parameter Model available for import from strangeworks_optimization_models.parameter_models. See the Optimization Providers section for more details on individual solvers and their parameters.