Prerequisites
Install
Set your SDK key as an environment variable, or create a .env file in your project root:
# .env
DYNEX_SDK_KEY=your_sdk_key_here
DYNEX_GRPC_ENDPOINT=quantum-router-engine-grpc.hz.dynex.co:3000
Install python-dotenv to auto-load .env files: pip install python-dotenv
Your first annealing job
The following example creates a simple Binary Quadratic Model and samples it on the Dynex neuromorphic GPU network:
import dynex
import dimod
from dynex import DynexConfig, ComputeBackend
# Build a simple BQM: minimize x0 + x1 with interaction penalty
bqm = dimod.BinaryQuadraticModel(
{0: -1.0, 1: -1.0},
{(0, 1): 2.0},
0.0,
'BINARY'
)
# Configure to use Dynex neuromorphic GPU chips
config = DynexConfig(compute_backend=ComputeBackend.GPU)
# Wrap model and create sampler
model = dynex.BQM(bqm)
sampler = dynex.DynexSampler(model, config=config, description="My first Dynex job")
# Sample
sampleset = sampler.sample(num_reads=1000, annealing_time=200)
# Inspect results
best = sampleset.first
print(f"Best sample: {best.sample}")
print(f"Best energy: {best.energy}")
ComputeBackend.GPU is the primary Dynex backend — your job runs on Dynex’s own neuromorphic GPU chips distributed globally. For offline testing without credentials, use ComputeBackend.LOCAL with the local solver binary.
Your first quantum circuit
Run a PennyLane circuit on Dynex using the DynexCircuit class:
import pennylane as qml
from dynex import DynexConfig, ComputeBackend, DynexCircuit
# Define a simple 2-qubit circuit
def bell_circuit(params):
qml.Hadamard(wires=0)
qml.CNOT(wires=[0, 1])
return qml.state()
# Configure QPU backend
config = DynexConfig(
compute_backend=ComputeBackend.QPU,
qpu_model='apollo_rc1'
)
dynex_circuit = DynexCircuit(config=config)
result = dynex_circuit.execute(
bell_circuit,
params=[],
wires=2,
method='measure'
)
print("Circuit result:", result)
Next steps