Kipu Quantum Hub

from qhub.quantum.sdk import HubQiskitProvider  # uv add qhub-quantum
from qiskit import QuantumCircuit, transpile

# A Bell state: two qubits that always give the same result.
circuit = QuantumCircuit(2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure_all()

# Log in with "qhubctl login", or set KQH_PERSONAL_ACCESS_TOKEN.
provider = HubQiskitProvider()

# Every backend: https://hub.kipu-quantum.com/quantum-backends
backend = provider.get_backend("kipu.sim.qsim")

circuit = transpile(circuit, backend)
job = backend.run(circuit, shots=100)

print(job.result().get_counts())

from qhub.service.client import HubServiceClient  # pip install qhub-service

client = HubServiceClient(
    service_endpoint=SERVICE_ENDPOINT,
    access_key_id=ACCESS_KEY_ID,
    secret_access_key=SECRET_ACCESS_KEY,
)

hubo = {
    "()": 0.0,
    "(0,)": -1.0,
    "(1,)": -1.0,
    "(0, 1)": 2.0,
    "(0, 1, 2)": -1.5,
}

execution = client.run(request={
    "problem": hubo,
    "problem_type": "binary",
    "shots": 100,
    "num_iterations": 3,
})
execution.wait_for_final_state()

response = execution.result()
print(response.result["cost"], response.result["mapped_solution"])

import json, numpy as np
from qhub.api.platform.client import HubPlatformClient
from qhub.service.client import HubServiceClient

# 1. Upload your tabular data to a Hub data pool (the file must be data.json).
pools = HubPlatformClient(api_key=QHUB_ACCESS_TOKEN, organization_id=ORG_ID).data_pools
dataset = {
    "training_tabular_data": X_train.to_dict(),  # <= 15 features, >= 20 rows
    "training_target_data": y_train.to_dict(),
    "test_tabular_data": X_test.to_dict(), "test_target_data": y_test.to_dict(),
}
src = pools.create_data_pool(name="Rimay input")
pools.add_data_pool_file(id=src.id, file=("data.json", json.dumps(dataset).encode()))
dst = pools.create_data_pool(name="Rimay output")

# 2. Extract quantum features on the free IBM Aer simulator.
client = HubServiceClient(access_key_id=ACCESS_KEY_ID, secret_access_key=SECRET_KEY,
                          service_endpoint=SERVICE_ENDPOINT)
client.run(request={
    "input_data_pool": {"id": src.id, "ref": "DATAPOOL"},
    "output_data_pool": {"id": dst.id, "ref": "DATAPOOL"},
    "num_shots": 500, "num_runs": 1,
}).result()

# 3. Single-body expectations + two-body correlations, ready for any classifier.
model = XGBClassifier().fit(np.load("1_Xq_train_0.npy"), y_train)