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You can run a useful quantum experiment without buying a quantum computer. The best first circuit is a two-qubit Bell state: apply a Hadamard gate to qubit 0, connect it to qubit 1 with a controlled-NOT (CNOT), measure both qubits, and inspect the resulting histogram. Run it in a simulator first, then submit the same circuit to cloud hardware if you want to see the effects of real quantum noise.
IBM Quantum is the easier visual starting point because IBM Quantum Composer lets you drag gates onto a circuit and view the expected results. AWS Braket is a strong Python-first option: its SDK uses the same circuit description with a local simulator, managed simulators, or a selectable quantum-processing unit (QPU). This guide shows both workflows, explains what the results mean, and helps you choose the right next step.
What you need before starting
Neither service requires you to own a quantum computer. The practical prerequisites are an account with the relevant cloud service, permission to use its quantum tools, a web browser or Python environment, and a small amount of patience for simulator jobs or hardware queues.
- IBM Quantum: use IBM Quantum Composer in the browser, or install Qiskit and the
qiskit-ibm-runtimepackage for a Python workflow. The hardware path requires authentication and selection of an operational IBM backend. - AWS Braket: open a Braket notebook with the
conda_braketkernel or install the Braket SDK in your own Python environment. Managed simulator and QPU tasks also require the AWS account, permissions, and result storage configured for Braket. - For either service: start with simulation. It confirms that the circuit is constructed correctly before you wait for a hardware queue or investigate noise.
Cloud access is not the same as unlimited free access. Account, region, service-permission, simulator, storage, and hardware charges depend on the provider and the resource you select. Check the current service console and account terms before submitting a managed simulator or QPU task.
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The first circuit: a two-qubit Bell state
A Bell circuit is small enough for a beginner to understand and distinctive enough to reveal whether the circuit behaved as intended. It has two qubits and three essential operations:
- Apply an H (Hadamard) gate to qubit 0. This puts that qubit into an equal superposition of 0 and 1.
- Apply a CNOT (CX) with qubit 0 as the control and qubit 1 as the target. This correlates the two qubits.
- Measure both qubits.
In an ideal simulation, measurements are concentrated in 00 and 11, approximately half of each. The circuit does not produce only one fixed bit string: each shot samples the quantum state, so a 1,000-shot run will normally have counts near 500 for each result.
AWS’s documented example produced about 503 occurrences of 11 and 497 of 00 in one 1,000-shot run. Those values illustrate ordinary shot variation; they are not a guarantee for every run, simulator, backend, or shot count. On real hardware, 01 and 10 can also appear because gates, measurement, and the device itself are noisy.
Choose your beginner interface
| Goal | Best starting path | What you do |
|---|---|---|
| See gates and outcomes without writing Python | IBM Quantum Composer | Drag an H gate and a CNOT onto two wires, add measurements, and inspect the circuit and histogram. |
| Write a programmable IBM experiment | Qiskit with IBM Quantum Runtime | Install Qiskit and qiskit-ibm-runtime, authenticate, choose an operational backend, compile to its supported instruction set, and submit through Runtime. |
| Learn a Python SDK that can target several device types | AWS Braket notebook and SDK | Build the circuit with the Braket SDK, run it with LocalSimulator, then change the device object for a managed simulator or QPU. |
Run the Bell circuit in IBM Quantum Composer
Composer is IBM’s graphical circuit builder. It is useful when you want to understand the relationship between a gate, a circuit diagram, and a measurement histogram before dealing with Python syntax or authentication.
1. Open a Composer workspace
Sign in to IBM Quantum and open Quantum Composer. Start a new circuit with at least two qubits and two classical bits. Composer’s interface lets you place operations on individual qubit wires and inspect the resulting state or histogram.
2. Place the gates
- Drag an H operation onto qubit 0.
- Place a controlled-NOT (shown as CX or CNOT) with its control on qubit 0 and its target on qubit 1. The control and target must be on the same circuit layer or on successive layers according to the interface’s placement rules; what matters is the control direction.
- Add measurements from both qubits to the classical bits. Keep the mapping visible so you know which displayed bit corresponds to each qubit.
The visual circuit should represent H on the first wire, a CNOT linking the first wire to the second, and measurements at the end. If you reverse the CNOT direction, you are no longer running the same Bell-state preparation.
3. Inspect the simulator result
Use Composer’s simulator view to run a batch of shots and display the histogram. An ideal result should show two dominant bars, 00 and 11, with roughly equal height. Small differences are expected because a finite number of shots is being sampled.
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4. Export code when you are ready
Composer can generate OpenQASM or Qiskit code for the circuit. Exporting is a practical bridge from visual experimentation to a repeatable Python workflow: copy the generated circuit, preserve its measurement mapping, and then follow the IBM Runtime setup for hardware execution.
Run the Bell circuit with the AWS Braket SDK
Braket describes circuits with a Python SDK and separates the circuit from the device that executes it. That separation is useful for learning: the same Bell circuit can run locally, on a managed simulator, or on a QPU by changing the device object.
1. Open a Braket notebook or Python environment
In the AWS console, open a Braket notebook and select the conda_braket kernel, or install the Braket SDK in your own environment. A notebook is convenient for a first experiment because it keeps the circuit, execution call, and returned counts together.
2. Construct the circuit
from braket.circuits import Circuit
bell = Circuit().h(0).cnot(0, 1)
print(bell)The expression applies H to qubit 0 and CNOT from qubit 0 to qubit 1. Braket’s circuit execution adds measurement sampling when the task is run. Keep the qubit numbering and the output-bit order in mind when you interpret the returned strings.
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3. Run locally first
from braket.devices import LocalSimulator
device = LocalSimulator()
result = device.run(bell, shots=1000).result()
print(result.measurement_counts)A local run avoids a hardware queue and is the quickest way to validate the circuit and your Python environment. The returned measurement_counts mapping should be concentrated in 00 and 11. Counts will not necessarily be exactly equal at 1,000 shots.
AWS recommends the local simulator for smaller circuits, up to 25 qubits depending on your local computer’s hardware. That is a practical limit, not a promise that every laptop can simulate every 25-qubit circuit: memory use depends heavily on the circuit and simulator method.
4. Move to a managed simulator when needed
For a larger state-vector simulation, Braket documents SV1, an on-demand managed simulator for circuits up to 34 qubits. Managed tasks run in AWS rather than consuming your local machine, and Braket stores task results in Amazon S3. The exact cost, region availability, and account requirements are service details you should verify in the AWS console before submitting.
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The conceptual change is to replace LocalSimulator() with the appropriate managed-device object, while leaving the Bell circuit unchanged. Device identifiers and availability are shown in the Braket console and documentation; do not assume that every device is available in every AWS Region.
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Braket uses an AwsDevice object to represent a managed simulator or a QPU. Select a device ARN shown as available in your account, then submit the circuit with the desired shot count. QPU and SV1 task results are stored in S3, so the notebook role must be able to use the configured bucket and retrieve the result.
A hardware submission can return additional strings such as 01 and 10. That does not automatically indicate a coding mistake; it is the expected consequence of executing a short circuit on noisy hardware.
Run the circuit on IBM hardware with Python
IBM’s programmable route uses Qiskit and Qiskit Runtime. Runtime is IBM’s cloud service for executing quantum computations on IBM hardware, while Composer is the visual route.
1. Install the required packages
Install current versions of Qiskit and qiskit-ibm-runtime in the Python environment where you will build and submit the circuit. Package APIs change over time, so use the current installation and authentication instructions shown by IBM Quantum when setting up a new environment.
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Configure the IBM Quantum credentials required by the Runtime service. Treat the API token as a secret: do not paste it into a public notebook, commit it to source control, or include it in screenshots. If your organization uses a managed account, authentication and workspace selection may be handled by an administrator.
3. Build and measure the circuit
Create a two-qubit Qiskit circuit with an H gate on qubit 0, a CX/CNOT from qubit 0 to qubit 1, and measurements on both qubits. Composer can generate equivalent Qiskit code if you prefer to construct the circuit visually first.
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4. Select an operational backend
Query the backends available to your account and choose an operational, non-simulator IBM backend when you are ready for hardware. Availability, queue length, calibration, and supported instructions vary by backend and can change during the day. A backend that is listed in documentation is not necessarily accepting jobs at the moment you submit yours.
5. Transform the circuit to the backend’s ISA
Before submission, transpile or otherwise transform the circuit into the backend’s supported instruction set architecture (ISA). This step maps abstract operations such as CX onto the gates and connectivity that the selected device supports. Skipping it can produce an incompatibility error or an invalid circuit for that backend.
6. Submit through Runtime and read the result
Use the current IBM Runtime primitive and execution pattern for the installed package version. Submit the prepared circuit with a chosen shot count, wait for the job to complete, and inspect the returned counts or primitive result. The exact result object depends on the Runtime primitive and package version, so read the fields exposed by the version you installed rather than copying an older example unchanged.
On an IBM QPU, expect the histogram to be less clean than the ideal simulator’s histogram. A predominance of 00 and 11 is consistent with the intended Bell correlation; nonzero 01 and 10 counts are normal evidence of device and measurement error.
What simulation and hardware results mean
Ideal simulator
For the Bell circuit, an ideal simulator samples the state in which the two qubits are correlated. With enough shots, the histogram approaches 50 percent 00 and 50 percent 11. A finite run can show 497 versus 503, 480 versus 520, or another nearby split. The larger the shot count, the less relative shot noise you normally see.
Noisy simulator or real QPU
A noisy execution can produce all four two-bit strings. The key question is not whether every shot is 00 or 11, but whether those correlated outcomes remain dominant and whether the error rate is plausible for the selected device and calibration period.
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There is no single hardware histogram that every reader should expect. Results vary with the backend, calibration data, queue timing, compiler mapping, shot count, and the provider’s noise characteristics. Do not compare two runs as if they were identical experiments unless those conditions are controlled.
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IBM Quantum and AWS Braket compared
| Comparison point | IBM Quantum | AWS Braket |
|---|---|---|
| First visual experiment | Quantum Composer provides drag-and-drop circuit construction, circuit visualization, simulator views, and code export. | Primarily a Python SDK and notebook workflow; the circuit is explicit in code. |
| Python path | Qiskit plus qiskit-ibm-runtime, with IBM authentication, backend selection, ISA preparation, and Runtime submission. |
Braket SDK with a device abstraction; the same circuit can be sent to a local simulator, managed simulator, or QPU. |
| Hardware scope | Targets IBM Quantum QPUs. | Uses AWS device selection to expose supported QPU providers and managed simulators available through Braket. |
| Result handling | Runtime returns execution results or primitive results for the submitted job. | Managed simulator and QPU task results are stored in S3; local execution returns a result directly to Python. |
| Best beginner support | A visual Composer workflow plus IBM’s structured getting-started learning path. | A first-circuit tutorial, notebooks, and a clear local-to-managed-device progression. |
| Typical first choice | Choose this if seeing gates and histograms visually matters more than starting with code. | Choose this if you want Python immediately or expect to compare multiple device types through one SDK. |
Troubleshooting the first run
The histogram has only one result
Check that the H gate is actually on qubit 0, that the CNOT control is qubit 0 and target is qubit 1, and that both qubits are measured. Also confirm that you ran enough shots to see sampling rather than inspecting a single shot.
The simulator shows 01 or 10
Verify the circuit and bit-order convention first. If the circuit is correct, determine whether you are using a noisy simulation or hardware. Ideal Bell-state simulation should concentrate on 00 and 11; real devices can produce the other strings.
IBM rejects the circuit
Confirm that you selected an operational backend, authenticated the correct account or workspace, and transformed the circuit to the backend’s supported ISA. Backend availability and supported instructions are not universal, so a circuit that worked on one device may require a different mapping on another.
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Check the selected device’s availability in the AWS console, the AWS Region, the notebook or SDK permissions, and the S3 location used for results. A local simulator does not prove that your account is authorized to create a managed task or write to the required bucket.
The job is taking longer than expected
Local simulation normally avoids a cloud queue. Managed simulators and QPUs depend on service scheduling, device availability, account limits, and the selected shot count. Do not treat a variable queue time as evidence that the circuit is stuck; inspect the job or task status in the provider console and use its documented cancellation or retry controls if necessary.
A practical learning sequence after the Bell state
- Run the Bell circuit locally and save the counts.
- Change the shot count and observe how sampling variation changes.
- Export the Composer circuit as Qiskit code, or rewrite the Braket circuit in a notebook, so the experiment is reproducible.
- Run the circuit on a managed simulator only after you understand where its results are stored.
- Submit to a real QPU and compare the ideal and hardware histograms, recording the backend and shot count with your results.
- Modify one operation at a time—for example, remove the H gate or CNOT—and predict how the histogram should change before running it.
This progression separates three different problems: circuit logic, software/API setup, and hardware noise. Keeping them separate makes debugging substantially easier than starting with an opaque hardware failure.
Bottom line
For the fastest visual introduction, use IBM Quantum Composer: draw H, CNOT, and measurements, run the simulator, and export the circuit when you want code. For a Python-first workflow, use AWS Braket’s LocalSimulator with Circuit().h(0).cnot(0, 1), then swap in a managed device only after the local counts look right. IBM’s Qiskit Runtime path is the corresponding programmable route for IBM hardware. In every case, simulate first, treat 00/11 as the ideal Bell signature, and interpret extra outcomes on a QPU as noise to measure rather than as automatic proof that your circuit is wrong.
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