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To run a circuit on real quantum hardware, you build it locally, authenticate with a cloud provider, compile it for a selected device, submit a job, wait for the queue, and analyze probabilistic results. The Python program is the control plane; the provider’s queued job is what executes on the remote quantum processor.
A reliable workflow is to test the circuit on a simulator first, then submit the same logic—after target-specific compilation—to a quantum processing unit (QPU). IBM Quantum and Amazon Braket both follow this pattern, but their authentication, compilation APIs, result objects, hardware catalogs, and scheduling models differ.
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The cloud-quantum workflow in one view
Every provider gives the process different names, but the decisions are the same:
- Map the problem: express the algorithm as gates, measurements, and (if needed) observables.
- Choose an execution target: a local or cloud simulator for development, or an operational QPU for hardware results.
- Optimize for that target: transpile or compile the abstract circuit into the device’s supported instruction set and qubit connectivity.
- Execute: submit a job through the provider SDK, receive a job identifier, and wait for completion.
- Post-process: read samples or expectation values, quantify uncertainty, and account for noise.
IBM describes these as map, optimize, execute, and analyze. The important boundary is compilation: a circuit that is mathematically valid is not automatically executable by a particular QPU.
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What you need before submitting hardware work
- A computer with a supported Python installation and internet access.
- A provider account and credentials. For IBM, this means an IBM Quantum Platform login, an API key, and the service-instance identifier (CRN) associated with your access.
- A circuit representation supported by the provider SDK. Qiskit uses
QuantumCircuit; Braket uses its SDK circuit/task objects. - A target choice. Device status, supported gates, qubit count, connectivity, queue, and calibration quality can change, so inspect the live status immediately before submission.
- A plan for results. Decide whether you need individual measurement samples or an observable’s expectation value; that choice determines whether a sampler-style or estimator-style primitive is appropriate.
Do not treat a cloud QPU like a local Python function. Your script sends a serialized program to a service, the service schedules it, and the remote device returns a result object after execution.
Test the circuit on a simulator first
Simulation catches syntax, measurement, register-order, and result-parsing mistakes without consuming hardware queue time. IBM’s documented workflow includes a local fake_provider mode for small circuits. A fake backend models a particular device’s interface and noise characteristics closely enough to validate compilation and result handling, while larger workloads may require a real simulator or QPU.
A practical simulator checklist
- Create the circuit and add every measurement you intend to analyze.
- Run it on an ideal simulator to verify the expected distribution or expectation value.
- Run against a fake-provider backend when you need to exercise target-specific gates, coupling-map constraints, and noisy-result handling.
- Confirm that your code can retrieve the result object and extract counts, bitstrings, or expectation values before changing the target to hardware.
A simulator is not a prediction of a real device’s exact output. It is a development step: it verifies that the program is well formed and that your analysis code handles the provider’s result format.
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1. Install Qiskit and the IBM Runtime client
IBM’s current example uses Qiskit 2.5.2 and qiskit-ibm-runtime 0.47.0 or newer. Package releases change, so check the IBM documentation and your environment’s compatibility before pinning versions. A typical installation is:
pip install 'qiskit[all]~=2.5.2' 'qiskit-ibm-runtime~=0.47.0'If Qiskit is already installed, the IBM-specific client can be installed separately:
pip install qiskit-ibm-runtimeUse a virtual environment for reproducibility. Record the versions that actually installed; a notebook copied months later may need small API changes because provider clients and primitive classes evolve.
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2. Create credentials and identify the service instance
Sign in to IBM Quantum Platform, use the free Open Plan if you are eligible, create an API key, and identify the service instance (CRN) you intend to use. Save the account once rather than embedding the key in source code. The exact channel label and optional arguments can vary by client release, so follow the current IBM authentication page for your installed version. The pattern is:
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from qiskit_ibm_runtime import QiskitRuntimeService
QiskitRuntimeService.save_account(
channel='ibm_quantum',
token='YOUR_API_KEY',
instance='YOUR_SERVICE_INSTANCE_CRN',
overwrite=True
)Prefer an environment variable or a local credential store over committing a token to Git. If a key is exposed, revoke it in the platform console and create a replacement.
3. Build a small measured circuit
A Bell circuit is a useful first test because it creates a predictable two-qubit correlation while exercising entangling and measurement operations:
from qiskit import QuantumCircuit
circuit = QuantumCircuit(2, 2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure([0, 1], [0, 1])The Hadamard places qubit 0 into a superposition; the controlled-X entangles qubits 0 and 1. In an ideal noiseless run, repeated measurements should concentrate on correlated bitstrings. Real hardware can produce additional outcomes because of gate, readout, and environmental errors.
4. Select an operational QPU
After loading the saved account, select a backend that is online and not a simulator. IBM’s example uses the least-busy operational hardware backend:
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service = QiskitRuntimeService()
backend = service.least_busy(simulator=False, operational=True)Least busy does not mean most accurate. For a serious experiment, inspect the device status and calibration data, then choose a backend whose qubit connectivity and error profile fit your circuit. Queue length can change between selection and submission.
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5. Transpile to the target’s ISA
Hardware execution requires an instruction-set-architecture (ISA) circuit: gates must be supported by the device and placed on connected physical qubits. IBM states, “Only ISA circuits can be run on IBM hardware using IBM Quantum Compute Service.” Use the preset pass manager to perform this mapping:
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
pass_manager = generate_preset_pass_manager(
backend=backend,
optimization_level=1
)
isa_circuit = pass_manager.run(circuit)The pass manager may insert swaps, decompose high-level gates into native operations, choose a physical-qubit layout, and optimize the resulting depth. Higher optimization levels can reduce operations but may take longer to compile; compare them on the simulator before spending hardware queue time.
6. Choose Sampler or Estimator
Use a Sampler when you need shot-by-shot measurement data: bitstrings, counts, or quasi-probabilities. Use an Estimator when the goal is the expectation value of one or more observables, such as a Pauli operator, rather than a complete distribution.
| Primitive | Input emphasis | Returned information | Typical use |
|---|---|---|---|
| Sampler | Measured ISA circuit | Samples or bitstring distributions over repeated shots | Histograms, correlations, and classification-style outputs |
| Estimator | ISA circuit plus observables | Expectation values with statistical uncertainty | Variational algorithms and energy or operator measurements |
The class names and constructor signatures can change between Runtime releases. Use the primitive version documented for your installed client, but keep the conceptual distinction: sampler for distributions, estimator for observables.
7. Submit, record the job ID, and retrieve the result
A current Runtime-style submission looks like this; consult the installed release for whether the primitive takes a backend or a session mode argument:
from qiskit_ibm_runtime import SamplerV2
sampler = SamplerV2(mode=backend)
job = sampler.run([isa_circuit], shots=5000)
print(job.job_id())
result = job.result()The job identifier is your durable handle for monitoring and later retrieval. A queued job may finish long after the Python process exits; save the identifier and reconnect through the service if necessary. Do not assume that a successful submission means the circuit has already run.
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8. Interpret shots, values, and uncertainty
Quantum hardware is probabilistic. IBM’s example sets default_shots = 5000; that is an example parameter, not a universal requirement. More shots generally reduce sampling uncertainty but increase execution time and resource use. Fewer shots are useful while debugging; increase them when estimating a small probability or comparing close expectation values.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFor sampler output, convert the returned data into counts or frequencies and check that the total number of shots matches what you requested (allowing for the result format’s conventions). For estimator output, retain both the expectation value and its reported standard deviation. Plot repeated runs when investigating drift rather than treating one number as exact.
Error suppression and mitigation options are available in IBM’s documented Runtime workflow. They can improve estimates at additional computational or sampling cost, but they do not turn noisy hardware into an ideal simulator. Report the mitigation settings alongside the result.
A complete IBM execution pattern
The following skeleton keeps the stages visible so each failure has a clear location. Replace the credential and primitive details with the syntax required by your installed release:
from qiskit import QuantumCircuit
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2
# Credentials should already be saved locally.
service = QiskitRuntimeService()
backend = service.least_busy(simulator=False, operational=True)
circuit = QuantumCircuit(2, 2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure([0, 1], [0, 1])
pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
isa_circuit = pm.run(circuit)
sampler = SamplerV2(mode=backend)
job = sampler.run([isa_circuit], shots=5000)
print('Submitted:', job.job_id())
result = job.result()
print(result)Keep the original abstract circuit and the compiled ISA circuit. If results look wrong, comparing them reveals whether the error entered during circuit construction, layout, measurement mapping, or result parsing.
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Amazon Braket provides a separate cloud route with a console, Python SDK, and notebook option. Its documented sequence is: optionally open a notebook, import the SDK, specify a QPU or simulator, instantiate the circuit, run it, and collect the result.
The service offers on-demand access to gate-based QPUs from AQT, IonQ, IQM, and Rigetti, as well as a QuEra analog Hamiltonian simulator. Device inventory and status are dynamic, so select a device in the console or SDK immediately before creating the task.
Braket execution steps
- Sign in to the cloud account and open the Braket console or a configured notebook.
- Import the Braket SDK modules in the Python environment.
- Choose a simulator or a named QPU and verify that it is available in your region and account.
- Construct the circuit using Braket’s circuit representation, including measurements required by your analysis.
- Create and submit a task to the selected device.
- Wait for task completion, then fetch the result object and post-process samples or observables.
Braket’s abstraction can make it easier to move between vendors, but portability is not automatic. Each QPU has its own native gates, topology, shot limits, calibration behavior, and result conventions. The SDK or service must translate your circuit for the chosen device, and you should validate the compiled form on a simulator before hardware submission.
IBM Quantum and Amazon Braket compared
| Decision point | IBM Quantum with Qiskit | Amazon Braket |
|---|---|---|
| Authentication | IBM Quantum Platform account, API key, and service-instance CRN saved through QiskitRuntimeService.save_account |
Cloud account credentials and Braket service configuration; exact setup depends on the account and region |
| Circuit representation | Qiskit’s QuantumCircuit |
Amazon Braket SDK circuit/task objects |
| Simulator path | Local simulators and documented fake-provider backends for development | Cloud simulators plus notebook and console workflows |
| Hardware choices | IBM operational QPUs selected through the Runtime service | QPU access from AQT, IonQ, IQM, and Rigetti, plus a QuEra analog Hamiltonian simulator |
| Compilation requirement | Explicit preset-pass-manager transpilation to an ISA circuit; IBM hardware accepts only ISA circuits | Compilation and device translation are handled through the Braket task workflow, with device-specific restrictions still requiring validation |
| Result style | Sampler samples or Estimator expectation values and standard deviations | Task results containing device-appropriate samples or observables |
| Scheduling | Jobs can wait in a backend queue; status and least-busy selection are time-sensitive | Task scheduling and availability depend on the selected device and cloud region |
| Error handling | Runtime documents error-suppression and mitigation options | Options vary by device and SDK workflow; verify current documentation |
| Current pricing and plan terms | Not stated here; check the live IBM Quantum plan and service terms | Not stated here; check current Braket device and cloud pricing |
Choose IBM when tight integration with Qiskit Runtime primitives and IBM’s ISA workflow is the priority. Choose Braket when a single cloud interface to several QPU vendors, simulators, and console-managed tasks is more useful. Neither choice removes the need to inspect the selected device and validate results.
Common failures and how to fix them
Authentication or account errors
- Invalid token: generate a new API key, revoke the exposed one, and save the replacement locally.
- Wrong instance or CRN: copy the service-instance identifier exactly from the platform account and ensure your plan grants access to it.
- Credential works locally but not in a notebook: configure the notebook’s environment separately; local credential files are not automatically mounted into hosted notebooks.
The backend is unavailable or the queue is too long
- Check the live operational status again; a backend can become unavailable after selection.
- Run the circuit on a simulator while waiting and preserve the job ID for the hardware run.
- Do not repeatedly submit identical jobs just because the first one is queued; that multiplies cost and makes result comparison harder.
The provider rejects the circuit as unsupported
- On IBM, rerun the preset pass manager against the exact backend selected for submission.
- Check that measurements are attached to the circuit and that the compiled circuit, not the abstract circuit, is passed to the primitive.
- For Braket, inspect the selected device’s supported gate set, qubit count, and connectivity and choose a compatible circuit or simulator.
Results contain unexpected bitstrings
- Verify classical-bit and qubit ordering in the measurement mapping.
- Compare the same compiled circuit on a simulator or fake-provider backend.
- Increase shots only after confirming that the circuit and parser are correct; more samples cannot fix a wiring or interpretation error.
Expectation values are noisy
- Retain the standard deviation returned with the estimate and repeat the run when the difference you care about is comparable to that uncertainty.
- Reduce circuit depth and unnecessary two-qubit gates before reaching for mitigation.
- Document any error-suppression or mitigation settings so another run is comparable.
Cost, quotas, and operational decisions
The supplied provider material does not establish current prices, quotas, promotional allowances, or regional availability. Treat every plan, device rate, and free-access statement as time-sensitive: check the live account and device pages immediately before running hardware work.
You can control resource use by debugging locally, using small shot counts during development, selecting a short circuit, and submitting only after the compiled circuit has been inspected. A 5,000-shot example is useful for demonstrating a workflow, but it is not a requirement for every experiment.
Hardware-run checklist
- Have you tested the circuit and result parser on an ideal simulator?
- Have you exercised target-specific compilation on a fake provider or cloud simulator?
- Are your credentials stored outside source control, and is the service instance correct?
- Is the selected backend operational, and does its connectivity support the circuit?
- Did you transpile after selecting that exact backend?
- Did you choose Sampler for distributions or Estimator for observables?
- Did you record the job or task ID, shot count, backend, software versions, and mitigation settings?
- Will your analysis report uncertainty instead of presenting one noisy run as an exact answer?
What a successful run actually tells you
A completed cloud job demonstrates that the provider accepted, compiled, scheduled, and executed your circuit on the selected target. Its output is a finite, noisy sample from that execution, not a deterministic proof that the ideal circuit behaved perfectly. The scientifically useful result includes the circuit version, target, compilation settings, number of shots, returned distribution or expectation value, and uncertainty.
Once this workflow is routine, you can replace the Bell test with a larger algorithm without changing the essential sequence: authenticate, select, compile to the target instruction set, submit, retrieve, and analyze.
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