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This guide covers how to use IonQ’s native gates in Qiskit. To learn more about what the native gates are and when to use them, refer to our guide on getting started with native gates.

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Introduction

Building and submitting circuits using IonQ’s hardware-native gateset enables you to bypass our compiler and optimizer, providing more control and transparency than the default abstract gateset (though often at the cost of performance and convenience). Before working with native gates in Qiskit, we recommend reviewing our guides on Getting Started with Native Gates and Getting Started with Qiskit. Native gates are also supported in the IonQ API, Cirq, and PennyLane. Note that the first few code examples in this section highlight specific components of native gate workflows. For end-to-end code snippets that you can copy-paste and run directly, skip to the full code examples section below.
This is an advanced-level feature. Using the hardware-native gate interface without a thorough understanding of quantum circuits is likely to result in less-optimal circuit structure and worse algorithmic performance overall than using our abstract gate interface.

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Building circuits with native gates

Native gate circuit construction is supported as of v0.3.1 of the Qiskit IonQ Provider. Gates are provided as part of the qiskit-ionq package, including:
  • GPIGate(phi)
  • GPI2Gate(phi)
  • MSGate(phi0, phi1, theta=0.25) for Aria systems
  • ZZGate(theta) for Forte systems
# Import circuit and gate definitions
from qiskit import QuantumCircuit
from qiskit_ionq import GPIGate, GPI2Gate, MSGate, ZZGate
For more details about these gate definitions and parameters, refer to the native gates guide.
The parameters in the IonQ native gate specification are always defined in turns, not in radians. One turn is 2π radians.
To add these gates to a circuit, use Qiskit’s circuit.append() method: 
# Initialize the quantum circuit
circuit = QuantumCircuit(2, 2)

# Add gates (remembering that parameters are in turns, not radians)
circuit.append(MSGate(0, 0), [0, 1])
circuit.append(GPIGate(0), [0])
circuit.append(GPI2Gate(1), [1])
circuit.measure([0, 1], [0, 1])
circuit.draw()
Circuit built using native gates Note that Qiskit also defines MS and ZZ gates in qiskit.circuit.library, but these gates are not equivalent to the IonQ native gates. To build a circuit in IonQ native gates, make sure you’re using the gates imported from qiskit_ionq. For a complete code example including circuit submission, skip to the full code examples below.

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Transpiling a circuit to native gates

Converting a circuit to native gates with Qiskit’s transpilation is supported as of v0.5.1 of the Qiskit IonQ Provider. Start with the usual imports, plus Qiskit’s transpile() method: 
from qiskit import QuantumCircuit, transpile
from qiskit_ionq import IonQProvider
Build a quantum circuit using the abstract (QIS) gateset: 
qc_abstract = QuantumCircuit(2, 2, name="hello world, native gates")
qc_abstract.h(0)
qc_abstract.cx(0, 1)
qc_abstract.measure([0, 1], [0, 1])

qc_abstract.draw()
Circuit built using abstract gates Next, set up an IonQProvider and backend, using gateset="native". Qiskit’s transpiler can use the target gateset defined by this backend. 
provider = IonQProvider()
backend_native = provider.get_backend("simulator", gateset="native")
Finally, use Qiskit’s transpile() method and the native-gates backend to convert the circuit to IonQ’s native gateset: 
qc_native = transpile(qc_abstract, backend=backend_native)
qc.draw()
Circuit transpiled to native gates Here, we can see that the Hadamard and CNOT gates in the original circuit were converted into a series of GPI2 gates and one MS gate. For a complete code example including circuit submission, skip to the full code examples below.

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Submitting a circuit that uses native gates

Whether you built a circuit in native gates originally, or you built a circuit in abstract gates and transpiled it with Qiskit, you’ll need to submit it to an IonQ backend that was set up with the native gateset. Circuits submitted this way will bypass IonQ’s compiler and optimizer. Set up an IonQ backend and specify the native gateset: here, we’ll use the ideal simulator, but you can also use the noisy simulator or an IonQ QPU. This tells the backend to expect circuits defined in native gates, and to bypass IonQ’s compiler and optimizer. With the default setting, gateset="qis", the backend will expect circuits defined in abstract gates. 
provider = IonQProvider()
backend_native = provider.get_backend("simulator", gateset="native")
After you define a quantum circuit qc_native, either by building it in native gates directly or building it in abstract gates and then transpiling it, you can submit it to the native gate backend: 
job_native = backend_native.run(qc_native, shots=1000)
Each quantum circuit submitted to the IonQ Cloud must use a consistent gateset throughout—you cannot mix and match native gates and abstract gates in the same circuit.
For a complete code example including circuit construction, continue to the full code examples below.

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Full code examples

These examples put together the pieces from the above sections to show two different complete workflows: one for building a circuit in native gates and submitting it to IonQ’s simulator; one for building a different circuit in abstract gates, transpiling it via Qiskit, and submitting it to IonQ’s simulator. 
from qiskit import QuantumCircuit
from qiskit_ionq import IonQProvider
from qiskit_ionq import GPIGate, GPI2Gate, MSGate, ZZGate

# Set up an IonQ backend to use native gates
provider = IonQProvider()
backend_native = provider.get_backend("simulator", gateset="native")

# Initialize a quantum circuit
circuit = QuantumCircuit(2, 2, name="build in native gates")

# Add gates (remember that parameters are in turns, not radians)
circuit.append(MSGate(0, 0), [0, 1])
circuit.append(GPIGate(0), [0])
circuit.append(GPI2Gate(1), [1])
circuit.measure([0, 1], [0, 1])

# Run the circuit on the native gates backend
job = backend_native.run(circuit, shots=1000)

# Print results
print(job.get_counts())

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Additional resources