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The safest way to run your first quantum program with Microsoft Azure Quantum is to start locally in Visual Studio Code with the Microsoft Quantum Development Kit (QDK) extension. You can simulate a two-qubit Bell pair without buying quantum hardware or creating an Azure workspace. Once the Q# program works locally, install the QDK Python package if you need notebooks or other quantum languages, create an Azure Quantum workspace, choose a currently available simulator or quantum processing unit (QPU) target, and submit the job after checking its access rules, price and quota.
This path teaches the complete progression: write Q#, understand a probabilistic result, validate it on a provider simulator, and only then send a compatible program to real hardware.
What you need before starting
Your requirements depend on whether you are learning locally or submitting a cloud job.
| Goal | Required | Not required |
|---|---|---|
| Run the first Q# example locally | Computer, Visual Studio Code and the Microsoft QDK extension | Azure subscription, Azure Quantum workspace and quantum hardware |
| Use Python or a notebook | Python 3.10 or later, the QDK Python package and, for Azure submission, an Azure subscription and workspace | Physical QPU access for local simulation |
| Submit to a provider simulator in Azure Quantum | Azure account, workspace, Azure CLI with the quantum extension, and a compatible target |
Buying a QPU |
| Submit to real hardware | Azure subscription, workspace, authenticated account, compatible program, selected provider target, available quota and any required access plan | Owning quantum hardware |
Provider targets, target names, regional availability, queue policies, prices and private-preview offers change. Treat the target list displayed in your own workspace as authoritative immediately before submission.
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Why the Bell pair is the right first program
A Bell pair is the shortest useful demonstration of two important quantum ideas. A Hadamard gate places the first qubit into a 50/50 superposition. A controlled-NOT (CNOT) then links the second qubit to the first, producing an entangled state. In an ideal simulation, the state contains |00⟩ and |11⟩ with 50% probability each.
When you measure the two qubits, the individual result is unpredictable, but the results are correlated: you should see either (Zero, Zero) or (One, One). This is a demonstration of superposition and entanglement, not evidence that a practical business workload has become faster. The local simulator lets you inspect and repeat the experiment without queue time or usage charges.
Run your first Q# program locally
1. Install Visual Studio Code and the QDK extension
Install the current Visual Studio Code release, then install Microsoft’s Quantum Development Kit extension from the Extensions view. The extension supplies Q# language support and the guided run command used by the quickstart.
2. Create a Q# file
Open a folder for the experiment and create a file with the .qs extension, such as BellPair.qs. Add an operation named Main. The operation below follows the first-program example documented in Microsoft’s Q# quickstart, whose page showed a last-updated date of 2024-10-29.
import Std.Diagnostics.*;
operation Main() : (Result, Result) {
use (q1, q2) = (Qubit(), Qubit());
H(q1);
CNOT(q1, q2);
DumpMachine();
let (m1, m2) = (M(q1), M(q2));
Reset(q1);
Reset(q2);
return (m1, m2);
}3. Run the operation
- Open
BellPair.qsin Visual Studio Code. - Use the Run command displayed above
Main, or pressCtrl+F5. - Read the output panel.
DumpMachine()prints the simulated state before measurement, and the returned tuple shows the two measured values.
The simulator may return different values on different runs because measurement is probabilistic. The important check is that the two values agree in the ideal Bell-pair example.
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4. Reset every allocated qubit
The calls to Reset(q1) and Reset(q2) return both qubits to the |0⟩ state before the operation ends. Resetting is good practice when an operation has measured qubits and is required by many larger programs that reuse them. The use block also ensures that the allocated qubits are released when the operation finishes.
Read the Bell-pair output correctly
What the gates do
H(q1): creates equal amplitudes for the first qubit’s zero and one states.CNOT(q1, q2): flipsq2only whenq1is one, correlating the two qubits.M(q1)andM(q2): convert quantum states into classicalResultvalues,ZeroorOne.
What a correct result looks like
For an ideal simulator, (Zero, Zero) and (One, One) are the expected outcomes. Seeing (Zero, One) or (One, Zero) indicates that the program, measurement order or execution environment is not the same as the example. A single run cannot establish the 50/50 distribution; repeat the operation many times if you want to estimate it statistically.
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DumpMachine() is a diagnostic operation for simulation. It does not represent a measurement that can be performed in the same way on every QPU, so remove or replace diagnostic calls when adapting a program for a hardware target that does not support them.
Use Python and notebooks when the project grows
Q# is the shortest path to the first run. Python becomes useful when you want notebooks, data processing, orchestration or a job written in another supported quantum framework. The current QDK submission guidance supports Q#, OpenQASM, Qiskit, Cirq and PennyLane programs, subject to the target’s input format and compatibility.
Install the SDK and Azure CLI extension
Use Python 3.10 or later. In a virtual environment, install the Azure-enabled QDK package:
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python -m pip install "qdk[azure]"Install or upgrade the Azure CLI extension that supplies Quantum commands:
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Authenticate and identify a workspace
- Sign in with the Azure CLI:
az login. - If your account has more than one subscription, select the subscription that contains the Quantum workspace.
- Create or select a workspace in the Azure portal and copy its resource ID.
- Construct a
qdk.azure.Workspaceobject with that resource ID in your Python session, following the current SDK signature. - Call
workspace.get_targets()to list the targets exposed by that workspace.
The resource ID identifies the workspace; it is not the same thing as a provider target name. Keep the two values separate in scripts and configuration.
Choose a target based on the program’s format
A Q# operation, an OpenQASM circuit and a circuit produced by Qiskit may require different compilation or submission settings. Before submitting, verify:
- the target accepts your language or serialized format;
- the required qubit count and gate set are supported;
- measurement, reset and other instructions are permitted;
- the target is a simulator, emulator, syntax checker or real QPU rather than a similarly named entry;
- your region, account and workspace have access to it.
Validate on an Azure Quantum simulator first
Microsoft describes provider simulators as available to Azure Quantum users and recommends them for testing Q# programs before real hardware. Simulator choices listed in the provider documentation include IonQ’s simulator, Quantinuum emulators, Pasqal emulators and Rigetti’s QVM. Some simulator targets are free, but the exact free allowance and any limits belong to the target shown in your workspace.
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A simulator is not automatically equivalent to a QPU. An emulator may reproduce a provider’s instruction set or noise model, while a general state-vector simulator may model ideal gates. Use it to catch syntax, wiring, measurement and result-handling errors, then treat hardware results as a separate validation step.
A practical simulator checklist
- Run the Bell pair locally in Q# and confirm correlated results.
- Port the circuit to the language you intend to submit, if necessary.
- Run that version on an available provider simulator.
- Compare returned data with the local ideal expectation, allowing for any documented noise model.
- Record the target name and configuration used, because provider catalogs change.
Create an Azure Quantum workspace
Portal setup
In the Azure portal, create an Azure Quantum workspace in a supported region and subscription. After deployment, open the workspace and copy its resource ID. The workspace’s Providers view shows which provider offers and targets are currently attached. Availability can differ by geography, subscription, provider agreement and preview status.
CLI and Python setup
After az login, select the correct subscription if needed. In Python, pass the copied resource ID to the QDK Azure workspace object. Listing targets is the first useful diagnostic: an empty or unexpectedly short list usually means the wrong subscription, region, workspace or provider configuration rather than a problem in the quantum circuit.
Submit a job to real quantum hardware
Real-hardware execution adds queueing, device constraints, usage charges and noise. Use this sequence rather than copying a target name from an old tutorial.
- Inspect current targets: use
workspace.get_targets()and the workspace’s Providers tab. Confirm the provider, target type, region and status. - Check compatibility: confirm the target’s supported language or format, qubit capacity, connectivity and instruction set. A circuit that simulates locally may need decomposition or fewer qubits on hardware.
- Check access: determine whether the target is generally available, requires an access plan or is a private preview. Preview access is not implied by seeing a provider in documentation.
- Check price and quota: review the provider’s current pricing and your workspace’s Operations > Quotas page before submission.
- Submit through the current SDK or CLI flow: select the target object returned by the workspace and send the compatible Q# or circuit program. Do not hard-code an obsolete target identifier.
- Monitor the job: record the job ID, watch its status and account for queue time. A submitted job can remain pending while the provider schedules it.
- Retrieve and interpret results: preserve measurement counts, metadata and any error information. Compare hardware output with the simulator, but expect noise and imperfect correlations.
For a Bell pair, hardware may occasionally produce the two mismatched outcomes because real devices have gate, readout and environmental errors. That does not necessarily mean the Q# logic is wrong; compare the error pattern with the provider’s documented noise and calibration information.
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Provider and target choices
The following comparison describes the decision axes documented for Azure Quantum providers. Names, capacities and access conditions are time-sensitive; verify every value in the workspace before running a paid job.
| Provider or target family | Examples of available non-QPU targets | Pricing unit described by Azure | What to verify |
|---|---|---|---|
| IonQ | Provider simulator | AQT or gate-shot based usage | Current QPU target, region, queue, supported gates, shot limits and account access |
| Quantinuum | Emulators | HQC or eHQC usage | Emulator versus QPU, error-correction or preview status, queue and minimum charge |
| Pasqal | Emulators | Execution-time pricing | Available neutral-atom target, execution-time definition, regional access and circuit limits |
| Rigetti | QVM | Execution-time pricing | QVM versus QPU, supported instructions, queue and execution-time accounting |
The provider target list last showed an update date of 2026-04-23, while the Azure pricing page showed 2025-12-23. Those dates help identify how quickly information can become stale; they are not guarantees that a target or price remains unchanged.
Understand Azure Quantum costs and quotas
There is no single Azure Quantum price for a quantum program. Each provider sets its own usage unit and rate. The pricing documentation describes IonQ usage in AQT or gate shots, Quantinuum usage in HQC or eHQC, and Pasqal and Rigetti usage by execution time. A simulator may be free, but “free” applies only to the particular target and allowance stated in the current workspace or provider terms.
| Cost question | Where to check | Why it matters |
|---|---|---|
| What unit is charged? | Provider pricing entry | Shots, gates, HQC/eHQC or elapsed execution time produce different cost behavior. |
| Is this target free? | Target details and current provider terms | Free simulators can have caps, expiration dates or restricted features. |
| Can the job run in this subscription? | Workspace Providers tab and access plan | Visibility in a catalog does not guarantee permission to submit. |
| How much usage remains? | Azure portal: Operations > Quotas | Provider-specific limits help prevent accidental overages or rejected jobs. |
Set a small shot count, reduce circuit size and run on a simulator before a paid QPU submission. Quotas are provider-specific, so a limit for one target does not establish a limit for another.
Common failures and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
The Run command is missing above Main |
QDK extension is not installed, the file is not recognized as Q# or no valid operation is present | Install or enable the QDK extension, confirm the .qs extension and check that Main has a valid return type. |
| Every Bell-pair run appears identical | The result was copied from one run or measurement was replaced by a fixed value | Run the operation repeatedly and check that results are limited to the correlated pairs. |
| Python cannot import the Azure package | The package was installed into a different interpreter or the Azure extra was omitted | Activate the intended virtual environment and rerun python -m pip install "qdk[azure]". |
az quantum is unavailable |
The CLI extension is missing or outdated | Run az extension add --upgrade -n quantum, then verify the Azure CLI installation. |
| No expected targets appear | Wrong subscription, resource ID, region or provider attachment | Re-authenticate with az login, select the workspace’s subscription, copy its resource ID again and inspect the Providers tab. |
| Submission is rejected | Unsupported language, gate, qubit count, access plan or quota | Read the target’s current constraints, transpile or reduce the circuit, and check Operations > Quotas. |
| Hardware results are less correlated than simulation | Gate and readout noise, connectivity constraints or calibration drift | Compare with a provider simulator, inspect the target’s documented limitations and rerun only after confirming the circuit mapping. |
How to learn beyond the first run
Microsoft’s Azure Quantum learning path is listed as approximately 3 hours 20 minutes across six modules and 4,500 XP. It is a reasonable next step after the Bell pair because it expands from syntax and simulation into resource estimation, providers and hardware execution.
Q# can simulate behavior, estimate resource requirements and run programs on real quantum hardware. The Microsoft Quantum resource estimator is intended to compare architectural decisions, qubit technologies and the resources required by an application. Use estimation before paying for large hardware experiments: it answers whether a proposed algorithm is feasible at a given scale, while a simulator answers whether your circuit behaves as intended.
A repeatable first-project workflow
- Keep the original two-qubit Q# file unchanged as a known-good test.
- Create a second operation for each experiment rather than editing away the working example.
- Run locally and inspect measurements before introducing Azure credentials.
- Move to Python or a notebook only when you need its orchestration or language ecosystem.
- Use a provider simulator to validate the submitted format and target-specific behavior.
- Inspect target availability, pricing and quota immediately before choosing a QPU.
- Submit the smallest useful hardware job, save the job ID and preserve its result metadata.
This sequence separates programming errors from cloud configuration errors and separates both from hardware noise, making each failure easier to diagnose.
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