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To run a circuit on a real cloud quantum computer, create the circuit in a provider SDK, authenticate, select an operational quantum processing unit (QPU), compile the circuit to that device’s instruction set and qubit connectivity, submit a job, wait for completion, and analyze the returned samples or expectation values. Test the code on a simulator or fake-device model first; hardware jobs are queued, probabilistic, and noisy.
The cloud-quantum workflow
A cloud QPU is not an extension of your local Python process. Your program builds and submits a job to a provider service. The provider schedules that job, executes it on a simulator or physical processor, and returns a result object. The reusable workflow is:
- Map the problem: express the algorithm as gates, measurements, and (when needed) observables.
- Optimize for the target: translate the abstract circuit into the device’s supported gates and connectivity.
- Execute: submit the compiled circuit with a chosen shot count or estimation settings.
- Analyze: retrieve the completed result, quantify sampling uncertainty, and account for hardware noise.
These stages matter even for a two-qubit demonstration. A circuit that is valid in a local SDK may contain gates unavailable on the selected QPU or require interactions between qubits that are not physically connected.
Choose a simulator or a real QPU
Start with a simulator or fake provider
Run the circuit locally before consuming hardware queue time. IBM’s documented workflow includes a local fake_provider mode for small circuits. It lets you check circuit construction, compilation, job submission code, and result handling against a device-like interface. A fake backend is useful for software validation, but it does not reproduce the changing calibration and noise of a live QPU. Larger workloads in the documented example require a real device.
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A simulator is also the right place to verify expected probabilities, measurement labels, and classical post-processing. Once those checks pass, submit the same logically correct circuit to hardware after compiling it for the selected target.
Use a real device when hardware behavior is the question
A physical QPU introduces queue time, calibration drift, readout errors, gate errors, and statistical variation. Run enough shots to estimate the distribution you care about, then compare the hardware result with the simulator baseline. Do not interpret one short run as a deterministic answer.
Run a circuit with IBM Quantum and Qiskit
The following path follows IBM’s current documented pattern. Package interfaces change, so check the installed documentation if your versions differ from the example versions.
1. Install the client packages
IBM’s current example uses Qiskit approximately version 2.5.2 and Qiskit IBM Runtime approximately version 0.47.0. Install the IBM Runtime client with:
pip install "qiskit[all]~=2.5.2" "qiskit-ibm-runtime~=0.47.0"If you are maintaining an existing project, pin the versions that you test and review the provider’s release notes before upgrading. Runtime primitives and result objects can change between major versions.
2. Create an IBM Quantum account and API credentials
Sign in to IBM Quantum Platform. Where you are eligible, the Open Plan provides a way to begin without a paid plan. In the platform, generate an API key and identify the service instance (also called the instance or CRN in IBM’s terminology). Treat the key like a password: keep it out of source control and inject it through a secret manager or environment variable.
Save the account once on the machine that will submit jobs. A version-tolerant pattern is:
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import os
from qiskit_ibm_runtime import QiskitRuntimeService
QiskitRuntimeService.save_account(
channel="ibm_quantum",
token=os.environ["IBM_QUANTUM_TOKEN"],
instance=os.environ["IBM_QUANTUM_INSTANCE"],
overwrite=True,
)Then create a service object in later runs:
from qiskit_ibm_runtime import QiskitRuntimeService
service = QiskitRuntimeService(channel="ibm_quantum")The exact accepted keyword set can depend on the Runtime release and account type. If authentication fails, confirm that the token, channel, and instance belong to the same account and that the local package is the version you intended to install.
3. Build a measured circuit
This Bell-state example uses two qubits. A Hadamard on qubit 0 creates a superposition, and a controlled-X from qubit 0 to qubit 1 entangles the pair. Measurements then produce correlated bitstrings in the ideal case.
from qiskit import QuantumCircuit
circuit = QuantumCircuit(2, 2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure([0, 1], [0, 1])For an algorithm with an observable rather than direct bitstring output, keep the circuit’s quantum operations separate from the observable definition. That allows an Estimator primitive to calculate expectation values instead of returning a shot-by-shot sample distribution.
4. Select an operational backend
Do not hard-code a device merely because it exists in the account list. Select a backend that is currently operational and suitable for the circuit’s size and connectivity. IBM’s example asks the service for the least-busy operational non-simulator backend:
backend = service.least_busy(
simulator=False,
operational=True,
)
print(backend.name)“Least busy” is a scheduling convenience, not a quality guarantee. Before a production run, inspect the device’s supported instructions, qubit count, coupling map, calibration information, and any project or access restrictions.
5. Transpile to the device’s ISA
Hardware execution requires an ISA (instruction-set architecture) circuit: every gate must be supported by the target and every two-qubit operation must respect its connectivity. IBM’s documentation states that only ISA circuits can run on IBM hardware through IBM Quantum Compute Service.
Use a preset pass manager to perform this mapping and optimization:
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from qiskit.transpiler import generate_preset_pass_manager
pass_manager = generate_preset_pass_manager(
backend=backend,
optimization_level=1,
)
isa_circuit = pass_manager.run(circuit)The optimization level trades compilation effort and circuit rewriting against the resulting gate count. Higher optimization can reduce depth, but it may take longer and can produce a different circuit layout. Always inspect the transpiled circuit when depth, two-qubit count, or qubit placement affects your experiment.
6. Choose Sampler or Estimator
- Sampler: use it when you need measurement samples or bitstring frequencies. The result represents repeated shots of the circuit.
- Estimator: use it when the question is the expectation value of one or more observables. It returns estimated means and associated uncertainty rather than a simple count dictionary.
These primitives express different questions. A Sampler run can tell you how often each measured bitstring appeared; an Estimator run can tell you the average value of an observable such as a Pauli operator.
7. Submit the job
Instantiate the primitive for the selected backend and submit the ISA circuit. Current Runtime releases expose versioned primitive classes, so use the class documented for your installed release. The structure is:
from qiskit_ibm_runtime import SamplerV2 as Sampler
sampler = Sampler(mode=backend)
job = sampler.run([isa_circuit], shots=5000)
print(job.job_id())The shots=5000 value is IBM’s example setting, not a universal requirement. More shots generally reduce sampling error but increase execution work. Choose the count based on the precision you need, the provider’s limits, and the experiment’s queue and usage constraints.
8. Wait for completion and inspect the result
A submitted job may remain queued before execution. Keep the job identifier so you can poll or resume retrieval in a later process:
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print(result)For a Sampler result, access the measurement data through the result object’s documented data field and convert it to counts or probabilities. A typical current-release pattern is:
pub_result = result[0]
counts = pub_result.data.meas.get_counts()
print(counts)Result-field names can vary with the primitive version and the classical register name. If this accessor does not match your installed release, inspect the returned object and use that release’s result schema rather than changing the circuit blindly.
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9. Analyze uncertainty and noise
Quantum measurements are random. Even an ideal Bell circuit produces a distribution, not one guaranteed bitstring on every shot. Physical hardware adds gate and readout errors, so compare measured frequencies with the simulator expectation and report the shot count.
Estimator results include expectation values and standard deviations. Use those values when deciding whether an observed difference is larger than ordinary sampling variation. IBM also documents error-suppression and error-mitigation options; select them only after understanding their extra runtime, sampling, and interpretation costs.
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Your source circuit is an algorithmic description. Transpilation may rewrite gates into the backend’s native basis, insert swaps to satisfy the coupling map, choose a physical-qubit layout, and simplify neighboring operations. A mathematically equivalent circuit can therefore have a different depth and a different exposure to noise on each backend.
- Instruction set: unsupported gates are decomposed into operations the QPU accepts.
- Connectivity: nonadjacent interactions require routing, often by inserting additional two-qubit gates.
- Layout: logical qubits are assigned to physical qubits with different calibration characteristics.
- Depth: optimization can shorten the schedule, which is important when coherence time is limited.
Compile after choosing the backend, not before. Reusing an ISA circuit on a different device can fail validation or silently defeat the intended optimization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Amazon Braket as an alternative route
Amazon Braket provides one cloud interface for gate-based QPUs from AQT, IonQ, IQM, and Rigetti, along with a QuEra analog Hamiltonian simulator. Its console displays device status, while its Python SDK and hosted notebooks support programmatic submission.
The documented Braket sequence is:
- Optionally open a managed notebook.
- Import the Braket SDK modules.
- Choose a simulator or QPU from the available devices.
- Construct the circuit with the SDK’s circuit representation.
- Run the circuit as a task with the desired shot settings.
- Collect and analyze the completed task result.
The conceptual model differs from IBM Runtime: Braket submits a task to a selected device, while IBM’s workflow centers on Runtime primitives and ISA circuits. In either service, you still authenticate, choose a target, compile or validate for that target, submit remotely, wait, and process probabilistic data.
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|---|---|---|
| Authentication and access | IBM Quantum Platform account, API key, and service instance; an Open Plan is available where eligible. | Cloud account access through the Braket console or SDK; the documented workflow begins by choosing a device. |
| Circuit representation | Qiskit QuantumCircuit. |
Braket SDK circuit objects. |
| Simulator path | Local fake_provider mode is documented for validating small circuits before hardware submission. |
Cloud simulators are selected through the same device-oriented workflow. |
| Hardware choice | Service backends, including operational non-simulator devices selected through the service API. | Gate-based QPUs from AQT, IonQ, IQM, and Rigetti, plus a QuEra analog Hamiltonian simulator. |
| Compilation concern | Generate a preset pass manager and produce an ISA circuit matching the backend’s gates and connectivity. | Choose the target device and submit a circuit in the SDK format accepted by that device; device-specific compilation requirements apply. |
| Execution object | Runtime primitive job, such as a Sampler or Estimator job, with a job identifier and result object. | Device task with a task result collected after execution. |
| Returned data | Sampler samples or Estimator expectation values and uncertainty statistics. | Task results appropriate to the selected simulator or QPU and circuit type. |
| Scheduling visibility | Hardware jobs can wait in a queue; backend operational status and workload affect timing. | The console exposes device status before submission. |
Provider inventory, access rules, and commercial terms change. Check the relevant console for current availability and pricing before planning a large run; the workflow itself does not establish a universal price or quota.
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How many shots should you use?
Shots are independent repetitions of the circuit used to estimate a distribution or mean. If a result has probability p, the ordinary sampling uncertainty of its frequency scales approximately as the square root of p(1-p)/N, where N is the shot count. This is a statistical guide, not a promise about hardware error.
- Use a small shot count for a fast code-path check on a simulator.
- Increase shots when distinguishing close probabilities or small expectation-value changes.
- Keep the same shot count when comparing conditions, unless you explicitly normalize the uncertainty.
- Report the shot count with every hardware result.
More shots cannot remove systematic gate, measurement, or calibration errors. They only reduce the random component of the estimate.
Troubleshoot the common failure points
Authentication errors
Recheck the API key, service instance, channel, and environment variables. Confirm that the account was saved under the same user profile and virtual environment that runs the script. Never paste a key into a notebook that will be shared.
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No suitable backend is returned
The least-busy query can return no device when all eligible hardware is offline, restricted, or filtered out. List the available backends, remove an unnecessarily strict filter, or choose a simulator while waiting for an operational QPU.
ISA or transpilation validation fails
Compile again after selecting the backend. Verify that the pass manager received the backend object and that you submit isa_circuit, not the original abstract circuit. A circuit compiled for one QPU is not automatically valid for another.
The job stays queued
Queue time is normal for shared hardware. Save the job ID, avoid resubmitting identical jobs repeatedly, and use a fake provider or simulator to test changes while the hardware job waits.
The result looks random or disagrees with the simulator
First check register order, bitstring endianness, and whether every intended qubit was measured. Then increase shots, inspect the transpiled depth and two-qubit count, and compare with current device calibration information. Hardware noise and finite sampling can both explain a difference; neither should be treated as a software exception.
An example works on one package version but not another
Inspect the installed Qiskit and Runtime versions, then follow the matching primitive and result-schema documentation. Versioned primitive classes and result accessors are especially likely to change during SDK updates.
A reproducible checklist
- The circuit produces the expected simulator or fake-provider result.
- Credentials are stored securely and the service instance is correct.
- The selected target is operational and has enough qubits.
- The circuit has been transpiled for that exact target.
- You chose Sampler for samples or Estimator for observable means.
- The shot count matches the precision and usage budget of the experiment.
- You recorded the backend, package versions, job ID, shot count, and result statistics.
- You compared hardware output with an ideal or device-like simulator baseline.
Which cloud route should you use?
Choose IBM Quantum with Qiskit when you want the documented Runtime primitive model, explicit ISA compilation, and a Qiskit-native workflow. Choose Amazon Braket when access to several QPU vendors through one SDK and console is the priority. In both cases, the decisive technical work remains the same: validate locally, compile for the selected target, submit a remote job, wait for completion, and interpret noisy samples with stated uncertainty.
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