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You can run a real quantum circuit from a web-connected computer without owning quantum hardware. Create an IonQ account, generate an API key, install a supported Python SDK, run a two-qubit Bell-pair circuit on a simulator, and then submit the same circuit to an IonQ QPU if your account has hardware access. The simulator gives immediate feedback on your code; the QPU job may queue and its measurements will include statistical variation and hardware noise.
What you need before writing code
A first IonQ experiment needs only a few pieces:
- An IonQ account with access to Quantum Cloud.
- An IonQ API key (also called a token) for direct SDK submissions.
- Python installed locally, plus either the Qiskit integration or the Cirq integration.
- A two-qubit circuit and a measurement plan. This guide uses a Bell pair because it demonstrates superposition and entanglement with the smallest useful example.
- Simulator access for the initial run. QPU access is separate and depends on the access model enabled for your account.
You can also reach IonQ processors through marketplace services such as Amazon Braket, Azure Quantum, or Google Cloud. Those routes have their own accounts, credential handling, SDKs, queue policies, and billing controls. The direct IonQ route below is the shortest path when you want to learn the IonQ API itself.
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Create an IonQ account and API key
1. Open the current IonQ access page
Use IonQ’s signup and access flow to create an account. The Quantum Cloud product offers simulator access for exploration and separate ways to request or purchase QPU use. IonQ describes on-demand, reservation, and volume-based access models; availability and pricing can change, so check the live account page for the region and organization type that applies to you.
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In the account console, open the API-key or token area, create a key, and copy it immediately to a password manager or environment variable. Treat the key like a password: do not commit it to Git, paste it into a public notebook, or embed it in a browser application. If a key is exposed, revoke it in the console and create a replacement.
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For a quick local test, you can place the value in the example code as replace-with-your-key. For repeatable work, store it outside the source file and read it from an environment variable instead. The SDK examples require the key when constructing the IonQ provider or service.
Choose a submission route
There is no single best route for every user. Direct IonQ access exposes IonQ’s own provider and targets; a marketplace can be more convenient if your organization already has a cloud account, centralized billing, or a multi-provider workflow.
| Route | Account and credentials | Typical Python interface | Simulator and QPU considerations | Queue and billing |
|---|---|---|---|---|
| Direct IonQ Quantum Cloud | IonQ account plus an IonQ API key | qiskit_ionq or cirq_ionq |
IonQ simulator targets are useful for development; QPU targets require hardware access | Managed in IonQ’s access model; QPU jobs can wait in a queue |
| Amazon Braket | Marketplace account and the credentials required by that service | Braket SDK, with provider-specific setup | Availability, target names, and simulator choices are controlled by the marketplace integration | Marketplace billing and queue rules apply |
| Azure Quantum | Azure workspace and the credentials required by that workspace | Azure-supported SDK workflow | Target availability and submission settings are defined by the workspace and provider | Azure billing and queue rules apply |
| Google Cloud | Google Cloud project and the credentials required by the configured service | Provider-specific integration | Check the current IonQ integration documentation for simulator and QPU targets | Cloud-project billing and queue rules apply |
The marketplace rows intentionally avoid universal promises: each service changes its console labels and credential flow independently. If you use a marketplace, follow that provider’s current IonQ integration guide rather than copying the direct-provider code unchanged.
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Qiskit installation
For the walkthrough in this article, install Qiskit and the IonQ provider package in a virtual environment:
python -m venv ionq-first-circuit
# macOS or Linux
source ionq-first-circuit/bin/activate
# Windows PowerShell
# .ionq-first-circuitScriptsActivate.ps1
python -m pip install --upgrade pip
python -m pip install qiskit qiskit-ionqPackage releases evolve. If an import fails after an upgrade, check the compatibility notes for the installed Qiskit and qiskit-ionq versions instead of assuming that an older tutorial’s imports are still current.
Cirq installation
If you prefer Cirq’s circuit model, install its IonQ integration:
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python -m pip install cirq cirq-ionqQiskit is convenient if you already use Qiskit circuits and count dictionaries. Cirq is attractive if you prefer explicit qubit objects and histogram results. Both paths can express the same Bell pair.
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Two qubits begin in the state |00>. A Hadamard gate on qubit 0 changes that qubit into an equal superposition of 0 and 1. A controlled-NOT (CNOT, written CX in Qiskit) then flips qubit 1 only when qubit 0 is 1. The resulting entangled state is approximately (|00> + |11>) / √2.
Measuring both qubits should therefore produce correlated results. In an ideal simulator, repeated shots yield only 00 and 11, close to a 50/50 split. The individual shot is random; the correlation appears in the aggregate counts.
| Operation | Purpose | Expected state or observation |
|---|---|---|
Start in |00> |
Initialize both qubits | Both classical values are zero |
| H on qubit 0 | Create superposition | Qubit 0 has equal 0 and 1 amplitudes |
| CX(0, 1) | Entangle qubit 1 with qubit 0 | State becomes a Bell state |
| Measure both qubits | Convert amplitudes to classical samples | Correlated bit strings, ideally 00 or 11 |
Run the circuit on an IonQ simulator with Qiskit
1. Create and measure the circuit
Save this as a Python file after replacing the key. The simulator target name shown here is the conventional IonQ Qiskit target; verify the exact target names exposed by your installed provider if your account displays different labels.
from qiskit import QuantumCircuit
from qiskit_ionq import IonQProvider
API_KEY = 'replace-with-your-key'
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])
provider = IonQProvider(API_KEY)
simulator = provider.get_backend('ionq_simulator')
job = simulator.run(qc, shots=1000)
print(job.get_counts())The shots=1000 setting requests 1,000 independent measurements. A simulator run should finish quickly, and its counts are a convenient way to confirm that your gates, measurement wiring, and result parsing are correct before using QPU time.
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2. Check the counts
A healthy ideal result has large counts for 00 and 11, with little or no 01 or 10. The exact numbers vary because sampling is random. A result such as 502 shots of 00 and 498 of 11 is not more correct than 520 and 480; both are consistent with the same ideal distribution.
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3. Try a hardware-noise simulation when available
IonQ documents simulator modes that can model hardware noise. Such a run is useful for learning how errors change a histogram, but it is an educational approximation, not a guarantee of what a particular QPU job will return. Keep ideal-simulator, noise-model, and QPU results in separate files or notebooks so you do not confuse their assumptions.
Submit the Bell pair to an IonQ QPU with Qiskit
After the simulator succeeds and your account has QPU permission, change the target to ionq_qpu. IonQ’s official polling pattern waits until the job reaches the completed state:
from qiskit import QuantumCircuit
from qiskit_ionq import IonQProvider
from qiskit.providers.jobstatus import JobStatus
import time
API_KEY = 'replace-with-your-key'
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])
provider = IonQProvider(API_KEY)
qpu = provider.get_backend('ionq_qpu')
job = qpu.run(qc, shots=1000)
while job.status() is not JobStatus.DONE:
time.sleep(60)
print(job.get_counts())A QPU submission can remain queued for a few minutes. IonQ’s tutorial says, “Grab a coffee! This can take up to a few minutes.” Do not treat that sentence as a universal service-level guarantee: queue time depends on the target, demand, access arrangement, and current service state.
The tutorial shows one illustrative 1,000-shot run with counts {'00': 513, '01': 6, '10': 4, '11': 477}. Those numbers belong to that particular run; they are not a benchmark, promised accuracy, or fixed error rate.
Make polling safer for a real script
The compact example waits only for DONE. In production code, also inspect the job status and handle failure or cancellation according to the SDK version you installed. Add a maximum wait time, log the job identifier, and retrieve results only after completion. If your program exits while the job is queued, the remote job may still exist; reopen it through the provider rather than submitting duplicate work.
Run the same circuit with Cirq
Cirq represents qubits as objects and returns a histogram keyed by the measurement result. This example submits directly to the QPU:
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import cirq
import cirq_ionq as ionq
q0, q1 = cirq.LineQubit.range(2)
circuit = cirq.Circuit(
cirq.H(q0),
cirq.CX(q0, q1),
cirq.measure(q0, q1, key='b'),
)
service = ionq.Service(api_key='replace-with-your-key')
result = service.run(circuit=circuit, repetitions=1000, target='qpu')
print(result.histogram(key='b'))Cirq’s histogram uses integers to encode measured bit strings. For this two-qubit circuit, integer 0 represents 00 and integer 3 represents 11; integers 1 and 2 correspond to the two anti-correlated outcomes, depending on the bit-order convention. Seeing keys 0 and 3 instead of text strings does not mean that Cirq ran a different circuit.
If you want to develop without consuming QPU access, use the simulator target supported by your installed Cirq-IonQ package, then change only the target when you are ready for hardware. Confirm the currently accepted target spelling in the package documentation because integrations can revise names.
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Why QPU counts are not perfectly ideal
Quantum measurements are probabilistic even when the circuit is correct. Real hardware adds computational noise, so occasional 01 and 10 results are expected in a Bell-pair experiment. The useful question is whether the correlated outcomes dominate and whether the distribution is stable enough for your purpose, not whether every shot matches the ideal state.
Use shots deliberately
More shots reduce ordinary sampling fluctuation but do not remove systematic hardware error. One hundred shots is adequate for a quick wiring check; 1,000 shots gives a clearer histogram and matches the official example. Increasing shots can increase cost or queue impact under your access arrangement, so use the smallest number that answers your question.
Check qubit and bit order
Different SDKs print classical bits in different orders. In Qiskit, inspect the classical-register mapping in measure([0, 1], [0, 1]) and verify it with a simulator. In Cirq, decode the integer histogram using the documented key order. A reversed label can look like a gate error even when the physical circuit is correct.
Troubleshoot the common first-run failures
Authentication or unauthorized errors
- Confirm that the key has no leading or trailing spaces and belongs to the account selected in the console.
- Check that the key is still active; revoke-and-reissue if it was exposed.
- Make sure you are using the direct IonQ SDK with an IonQ key, not a marketplace SDK that expects cloud-provider credentials.
Backend or target not found
- Print or inspect the provider’s available backends and compare the exact spelling with
ionq_simulatororionq_qpu. - Update the integration package in the virtual environment if the target list differs from an older tutorial.
- Verify that your account is entitled to the QPU target; a simulator-only account will not be able to submit hardware jobs.
The job remains queued
Queueing is normal for shared hardware. Keep the job identifier, continue polling at a sensible interval, and avoid launching repeated copies. If the service reports an error or cancellation, capture the status and message before retrying.
The histogram contains many anti-correlated results
- Run the exact circuit on the ideal simulator to rule out a coding or measurement mistake.
- Confirm that the control and target qubits in the CNOT were not reversed accidentally.
- Check bit-order decoding before changing the circuit.
- Repeat with the same shot count. A single small sample can look unusually poor by chance.
The code works locally but not in a notebook or CI job
Notebook kernels and automated jobs often use a different Python environment. Print the interpreter path, install the packages into that environment, and provide the API key through its secret manager or environment configuration rather than placing it in notebook output.
When to use native gates
IonQ integrations can expose a hardware-native gate interface for advanced optimization. A native-gate circuit can be useful when you understand the target’s gate set, connectivity assumptions, compilation behavior, and error trade-offs. IonQ’s Cirq guidance warns that using the hardware-native interface without a strong understanding of quantum circuits can produce a less-optimal structure. For a first experiment, keep the H-and-CX Bell pair at the SDK’s standard abstraction level and optimize only after you can explain the resulting circuit and measurements.
Costs, access models, and practical limits
There is no universal price or guaranteed queue time that applies to every IonQ user. Simulator availability, QPU entitlement, reservations, on-demand use, and volume arrangements depend on the current offering and your account or marketplace route. Review the live IonQ access terms before submitting hardware work, especially if you are working in a shared organization account.
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“The IonQ Quantum Cloud offers various access models to meet your needs.” — IonQ, Quantum Cloud product page
For learning, the most economical sequence is to develop locally, validate on the simulator, test with a modest shot count, and submit to a QPU only when the circuit and measurement interpretation are settled. Keep the simulator counts and hardware counts together with the circuit source, target name, shot count, and submission date so later comparisons remain meaningful.
Quick Recap
A repeatable first-circuit checklist
- Create the IonQ account and confirm simulator access.
- Create an API key, store it securely, and never publish it with source code.
- Set up an isolated Python environment and install either
qiskitplusqiskit-ionqorcirqpluscirq-ionq. - Build the two-qubit Bell pair: H on qubit 0, CNOT/CX from qubit 0 to qubit 1, then measure both.
- Run 100 or 1,000 shots on the simulator and verify that
00and11dominate. - Confirm that your account can see the QPU target before submitting.
- Submit a modest-shot QPU job, record its identifier, and poll until completion.
- Decode the returned counts with the SDK’s bit-order convention and expect occasional noise-induced errors.
- Only then move to larger circuits, more shots, native gates, or automated workflows.
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