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IBM Quantum Hardware

Tested with: Quanta SDK v0.8.1

What You'll Learn

Submit circuits to real IBM quantum hardware, poll job results, and compare simulator vs hardware output.

Prerequisites

Step 1 — Set Up Credentials

Create a .env file in your project root:

IBM_API_KEY=your_api_key_here

Or set the environment variable:

export IBM_API_KEY=your_api_key_here

Security: .env is automatically gitignored by Quanta SDK.

Step 2 — List Available Backends

# ── Quanta ── (requires IBM_API_KEY)
# from quanta.backends.ibm_rest import IBMRestBackend
# backends = IBMRestBackend.available_backends()
# for b in backends:
#     print(f"{b['name']}: {b['num_qubits']} qubits, status={b['status']}")
print("IBM backend listing requires API key — see docs")

Step 3 — Submit a Job

# ── Quanta IBM hardware submission ──
# from quanta import circuit, H, CX, measure, run
# from quanta.backends.ibm_rest import IBMRestBackend
#
# @circuit(qubits=2)
# def bell(q):
#     H(q[0])
#     CX(q[0], q[1])
#     return measure(q)
#
# backend = IBMRestBackend(backend_name="ibm_torino")
# result = run(bell, backend=backend, shots=4096)
# print(result.summary())
print("IBM job submission requires API key and queue time")

Step 4 — Multi-Backend Support

Quanta supports 4 backends:

Backend Class Use Case
Local (default) Development, testing, education
IBM Quantum IBMRestBackend Superconducting qubits (Heron r3)
IonQ IonQBackend Trapped-ion (high fidelity)
Google GoogleBackend Sycamore processor 
# All backends use the same run() API:
# result = run(circuit, backend=backend, shots=N)
#
# IBM:
# backend = IBMRestBackend(backend_name="ibm_torino")
#
# IonQ:
# backend = IonQBackend(target="simulator")  # or "qpu"
#
# Google:
# backend = GoogleBackend(processor_id="weber")
print("Multi-backend: IBM, IonQ, Google — same run() API")

Step 5 — Compiler Pipeline

Before running on hardware, circuits are automatically compiled:

from quanta.compiler.pipeline import CompilerPipeline
from quanta.dag.dag_circuit import DAGCircuit
from quanta import circuit, H, CX, measure

@circuit(qubits=2)
def bell(q):
    H(q[0])
    CX(q[0], q[1])
    return measure(q)

dag = DAGCircuit.from_builder(bell.build())
pipeline = CompilerPipeline()
compiled = pipeline.run(dag)
print(f"Compiled: {compiled.gate_count()} gates, depth {compiled.depth()}")
print(pipeline.summary())

IBM Quantum Tips

  1. Use ibm_sherbrooke for 127-qubit experiments
  2. Use ibm_torino for latest Heron r3 (156 qubits)
  3. Start small: Test with 2-5 qubits first
  4. Queue times: Expect 1-30 minutes depending on system load
  5. Transpilation: Quanta automatically translates to {CX, RZ, SX, X}

What's Next

→ 06 — Quantum Machine Learning: Deep-dive into QuantumClassifier