The simplest useful first experiment is a two-qubit Bell-state circuit: apply a Hadamard gate to qubit 0, apply a controlled-NOT (CNOT or CX) from qubit 0 to qubit 1, measure both qubits, and inspect the counts. You can build it visually in IBM Quantum Composer, run it with Qiskit Runtime on an IBM quantum processor, or use the Amazon Braket SDK in a notebook. Start with a simulator, then send the same circuit to cloud hardware when you understand the expected result.
What you need before running a circuit
You do not need to purchase a quantum computer. Cloud execution requires an account with the selected service, an internet connection, the relevant software, and—when using managed devices—any account permissions and storage configuration requested by that service.
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- IBM Quantum Composer: an IBM Quantum account and a browser. Composer is the quickest visual route.
- IBM with Python: Python, Qiskit, the
qiskit-ibm-runtimepackage, and an IBM Quantum API token. - AWS Braket: an AWS account, a Braket notebook using the
conda_braketkernel or a local Python environment with the Braket SDK. Managed simulator and QPU tasks also use Amazon S3 for result storage.
Cloud consoles change labels and account requirements periodically. Use the current sign-in, authentication, and device-availability labels shown in your account rather than relying on an older screenshot.
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The first circuit: a two-qubit Bell state
A Bell circuit has three essential operations:
- Apply
Hto qubit 0. The Hadamard gate places that qubit into a superposition of 0 and 1. - Apply
CNOT(0, 1)(calledCXin Qiskit). The target qubit flips when the control qubit is 1, correlating the pair. - Measure both qubits. Repeating the circuit produces a distribution of bit strings.
On an ideal simulator, measurements are concentrated in 00 and 11, approximately half each. A finite number of shots introduces sampling variation, so the two counts will not normally be exactly equal. On real hardware, noise can add 01 and 10.
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Option 1: Build and run it in IBM Quantum Composer
Composer is IBM’s graphical circuit builder. It lets you drag operations onto qubit wires, view the circuit and histogram, and export OpenQASM or Qiskit code.
Build the circuit
- Sign in to IBM Quantum and open Composer from the workspace or learning interface.
- Start a two-qubit circuit. If the editor opens with more qubits, leave the extras unused or remove them if the interface offers that option.
- Drag an H gate onto qubit 0.
- Place a CX or CNOT with its control on qubit 0 and target on qubit 1. Composer displays the control dot and target symbol connected by a line.
- Add measurement operations to both qubits, or use Composer’s measurement control if it offers one for the entire circuit.
Inspect an ideal result
Use Composer’s simulator view and choose a meaningful shot count, such as 1,000. The histogram should show bars for 00 and 11 near the same height. Small differences are normal shot noise. If 01 and 10 dominate, check the CNOT direction, the qubit wires, and whether both measurements were added.
Export code or submit to hardware
Composer can generate OpenQASM or Qiskit code. Exporting is useful when you want to reproduce the experiment in a notebook, add more gates, or place the circuit in an automated workflow. To use an IBM QPU from the platform, select an operational, non-simulator backend, choose the available run control, and submit after Composer has converted the circuit to instructions supported by that backend. Queue time and available devices vary, so the interface’s current status is authoritative.
Option 2: Run the circuit with Qiskit on IBM Quantum
Qiskit Runtime is IBM’s cloud service for executing quantum computations on IBM Quantum hardware. The programmable workflow is: install the packages, authenticate, create the circuit, choose an operational backend, transform the circuit to that backend’s instruction set, submit a job, and read the returned data.
Install the Python packages
python -m pip install qiskit qiskit-ibm-runtimeUse a virtual environment for a repeatable setup. Package APIs evolve, so check the installed package’s current Runtime examples if a class or argument has changed.
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Create and measure the Bell circuit
from qiskit import QuantumCircuit
circuit = QuantumCircuit(2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure_all()
print(circuit)The first qubit is the control for the CX operation. Reversing the control and target still creates a related entangled state, but it is easier to compare runs when you keep the same ordering everywhere.
Authenticate and choose a backend
Create or retrieve an IBM Quantum API token through your IBM Quantum account. The current Runtime service accepts the token directly or through the account configuration mechanism documented in your workspace. A typical selection rule is an operational device that is not a simulator and has at least two qubits:
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from qiskit_ibm_runtime import QiskitRuntimeService
service = QiskitRuntimeService(
channel="ibm_quantum",
token="YOUR_API_TOKEN"
)
backend = service.least_busy(
operational=True,
simulator=False,
min_num_qubits=2
)Keep the token out of notebooks that you share. Prefer the service’s saved-account or environment-variable method for longer-lived projects rather than committing credentials to source control.
Match the circuit to the selected processor
IBM processors do not all expose the same native gates or coupling connections. Before execution, transpile or otherwise transform the circuit into the backend’s supported instruction set (ISA). A current Runtime workflow commonly uses a preset pass manager associated with the selected backend, then submits the ISA-compliant circuit through a Runtime primitive. The exact helper names depend on your installed Qiskit versions, so use the version-matched IBM example in your environment.
Submit and read the result
from qiskit_ibm_runtime import SamplerV2 as Sampler
sampler = Sampler(mode=backend)
job = sampler.run([circuit], shots=1024)
result = job.result()
print(result)Runtime returns a job object while the service processes the request. The result contains the measurement data for the submitted circuit; inspect the returned publication and measurement fields shown by your installed Runtime version to obtain counts. A successful hardware run normally contains mostly 00 and 11, plus some erroneous strings caused by readout and gate noise.
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Option 3: Run the circuit locally with Amazon Braket
Braket uses a device abstraction: your circuit code stays the same while the device object determines whether execution is local, on a managed simulator, or on a QPU. AWS’s first-circuit example uses the Braket SDK and reads measurement_counts.
Create the circuit
from braket.circuits import Circuit
bell = Circuit().h(0).cnot(0, 1)
print(bell)Use the local simulator first
from braket.devices import LocalSimulator
device = LocalSimulator()
task = device.run(bell, shots=1000)
result = task.result()
print(result.measurement_counts)A local run is the fastest way to catch Python errors and confirm that your circuit has the intended gates. AWS recommends the local simulator for smaller circuits, up to 25 qubits depending on the computer running it. Memory and processor capacity determine the practical limit; a laptop may handle fewer qubits than a workstation.
Interpret the local histogram
For 1,000 shots, an illustrative ideal run can produce counts near 503 for 11 and 497 for 00. Those values describe one sample, not a promise. Your counts will differ because each shot samples the quantum state. Counts for 01 or 10 in an ideal local simulation should be absent or negligible, depending on the simulator and circuit configuration.
Move the same Braket circuit to a managed simulator
After the local result makes sense, select a managed device in the Braket console. The console shows which devices are available to your account and region. Replace the local device object with an AwsDevice created from the device ARN displayed there:
from braket.aws import AwsDevice
# Use the ARN copied from the Braket device page.
device = AwsDevice(device_arn_from_console)
task = device.run(
bell,
shots=1000,
s3_destination_folder=(bucket_name, prefix)
)
result = task.result()
print(result.measurement_counts)Managed simulator and QPU tasks use Amazon S3 for result storage. The bucket and prefix must be accessible to the AWS identity running the task, and the selected region and device must be compatible with your account. AWS documents SV1 as an on-demand state-vector simulator for circuits up to 34 qubits; that limit is a documented device capability, not a guarantee that every circuit of that size will be practical under every memory or cost constraint.
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Run the Bell circuit on a Braket QPU
- Open the Braket console’s device list and check current availability for a QPU you are permitted to use.
- Copy that device’s ARN and construct an
AwsDevicewith it. - Keep the circuit small and retain the measurement operations. Start with a modest shot count so you can inspect the distribution before increasing it.
- Submit the task with an S3 destination and wait for the task status to move from queued or running to completed or failed.
- Retrieve the result and compare the observed counts with the local-simulator baseline.
A QPU result is not expected to match the ideal histogram exactly. Device calibration, queue timing, connectivity, compilation, gate errors, and readout errors all affect the distribution. Seeing 01 and 10 does not by itself prove that the circuit is incorrect.
What changes between simulation and hardware
| Aspect | Simulator | Cloud QPU |
|---|---|---|
| Purpose | Validate syntax and understand the ideal circuit behavior. | Measure a physical processor with imperfect gates and readout. |
| Bell-state pattern | Counts concentrate in 00 and 11. |
Those strings should usually be prominent, but 01 and 10 can appear. |
| Timing | Usually starts immediately on a local machine; managed simulators still queue as cloud tasks. | Depends on device availability and queue; no fixed completion time is guaranteed. |
| Result storage | Returned directly by the local SDK call. | IBM returns a Runtime execution result; Braket managed tasks store results in S3. |
| Debugging value | Best first check for gate order, measurements, and shot handling. | Useful for learning noise and backend-specific behavior after the ideal result is understood. |
IBM Quantum or AWS Braket: which should a beginner choose?
| Need | Better starting point | Reason |
|---|---|---|
| See gates without writing Python | IBM Quantum Composer | Its drag-and-drop editor displays the circuit and histogram and can export code. |
| Learn a programmable IBM workflow | Qiskit with IBM Runtime | You work directly with Qiskit circuits, backend selection, ISA conversion, and Runtime jobs. |
| Run the same SDK circuit on local, simulated, and physical devices | AWS Braket | The device object changes while the Braket circuit can remain substantially the same. |
| Explore multiple QPU providers through one cloud service | AWS Braket | Braket exposes provider devices through the AWS device-selection model; availability depends on account, region, and current service status. |
| Follow a visual learning path | IBM Quantum | Composer and IBM’s structured getting-started materials provide a guided first experiment. |
| Use a notebook-first tutorial | AWS Braket | A Braket notebook with the conda_braket kernel is designed for SDK examples and managed tasks. |
Choose IBM if visual construction or an IBM-specific learning path matters most. Choose Braket if you want one Python device abstraction and the option to explore several supported QPU providers. Neither choice requires buying quantum hardware.
Troubleshoot the first run
The histogram has only one bit string
Confirm that the H gate is on qubit 0, the CNOT connects qubit 0 to qubit 1, and both qubits are measured. Also verify that you did not accidentally run a circuit with a reset, conditional operation, or stale notebook variable.
The simulator gives unexpected keys or data fields
Measurement-result formats differ between SDKs and package generations. In Braket, read the task result’s measurement_counts. In Qiskit Runtime, inspect the returned result object and use the measurement field exposed by your installed Runtime version. Printing the complete result object is often the quickest way to identify the field name.
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Check that your token is valid, the selected backend is operational and not a simulator, and the circuit has been transpiled to instructions and connectivity that backend supports. A backend can become unavailable after you select it; refresh the current device status and choose another operational processor.
A Braket task stays queued or cannot write results
Check the device’s current availability, the AWS region, IAM permissions, and the S3 bucket and prefix supplied to the task. Managed tasks are asynchronous, so a queued status is not necessarily an error. Read the task’s final failure message before changing the circuit.
Hardware counts look much noisier than the simulator
That is normal for a first physical run. Increase shots only after confirming the circuit and backend, and compare the relative weight of 00 and 11 rather than expecting an exact 50/50 split. Calibration and queue conditions can change the result between submissions.
Good next experiments
Once the Bell circuit works, change one feature at a time so you can attribute the result:
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- Keep the H gate but remove the CNOT to see superposition without two-qubit correlation.
- Increase or decrease the shot count and compare sampling variation.
- Insert a barrier or export the Composer circuit to Qiskit to understand how visual operations map to code.
- Run the same circuit on a simulator and a QPU, recording the backend, date, shots, and counts so later comparisons remain meaningful.
These controlled changes teach more than immediately adding many gates: they separate state preparation, entangling operations, measurement, sampling, and hardware noise.
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