Transpile and optimize your PennyLane circuits into IonQ's native trapped-ion gate set (GPI, GPI2, MS) with just a single extra line of code!

from ionizer.transforms import ionize

@qml.qnode(dev)
@ionize
def circuit(x): 
    qml.Hadamard(wires=0)
    qml.CNOT(wires=[0, 1])
    qml.RX(x, wires=1)
    return qml.expval(qml.PauliZ(0))

>>> qml.draw(circuit)(0.3)
0: ──GPI2(0.00)─╭MS──GPI2(-1.57)─────────────────────────┤  <Z>
1: ──GPI2(3.14)─╰MS──GPI2(1.57)───GPI(-1.42)──GPI2(1.57)─┤     

Requirements:

• PennyLane >= 0.33

The Ionizer is not currently available via a package manager. To install, clone the repository and run

python -m pip install .

or

python setup.py install

If you need to run Ionizer with a version of PennyLane between 0.29 and 0.32, please use version 0.1.2 of the package.

The Ionizer is implemented using quantum function transforms, similar to PennyLane's existing compilation tools. To compile and execute the circuit using trapped ion gates, the @ionize decorator performs the following steps:

• Decomposes all operations into Paulis/Pauli rotations, Hadamard, and CNOT
• Merges all single-qubit rotations
• Converts everything except RZ to GPI/GPI2/MS gates (@ionizer.transforms.convert_to_gpi)
• Virtually applies all RZ gates (@ionizer.transforms.virtualize_rz_gates)
• Repeatedly applies gate fusion and commutation through MS gates which performs simplification based on some circuit identities (@ionizer.transforms.single_qubit_fusion_gpi and @ionizer.transforms.commute_through_ms_gates)

from ionizer.transforms import ionize

@qml.qnode(dev)
@ionize
def circuit_ionized(params):
    for idx in range(5):
        qml.Hadamard(wires=idx)
        
    for idx in range(4):
        qml.RY(params[idx], wires=idx)
        qml.CNOT(wires=[idx+1, idx])
        
    for wire in dev.wires:
        qml.PauliX(wires=wire)
        
    return qml.expval(qml.PauliX(0))

>>> circuit_ionized(params)
tensor(0.99500417, requires_grad=True)
>>> qml.draw(circuit_ionized)(params)
0: ──GPI2(-1.57)──GPI(-1.52)──GPI2(-3.04)─╭MS───────────────────────────────────────────────────
1: ──GPI2(-1.92)──GPI(3.14)───GPI2(-1.22)─╰MS──GPI2(2.36)──GPI(1.67)──GPI2(0.99)─╭MS────────────
2: ──GPI2(-1.92)──GPI(3.14)───GPI2(-1.22)────────────────────────────────────────╰MS──GPI2(2.36)
3: ──GPI2(-1.92)──GPI(3.14)───GPI2(-1.22)───────────────────────────────────────────────────────
4: ──GPI2(-1.92)──GPI(3.14)───GPI2(-1.22)───────────────────────────────────────────────────────

────────────────────────────────────────────────────────────────────────────────────────────┤  <X>
────────────────────────────────────────────────────────────────────────────────────────────┤
───GPI(1.72)──GPI2(1.09)─╭MS────────────────────────────────────────────────────────────────┤
─────────────────────────╰MS──GPI2(2.36)──GPI(1.77)──GPI2(1.19)─╭MS─────────────────────────┤
────────────────────────────────────────────────────────────────╰MS──GPI2(0.00)──GPI2(1.57)─┤

Note that while this comes packaged together as the @ionize transform, the individual transforms can also be accessed and used independently.

There is currently not direct support for other frameworks. However, if you would like to do this with a Qiskit circuit, it can be accomplished as follows through the pennylane-qiskit package.

qiskit_circuit = QuantumCircuit(...)

# Turns a Qiskit circuit into a PennyLane quantum function
qfunc = qml.from_qiskit(qiskit_circuit)

@qml.qnode(dev)
@ionize
def pennylane_circuit():
    qfunc()
    return qml.expval(qml.PauliX(0))

This package is a work in progress. While it has been verified to work on some fairly large circuits, we still need to work on:

• finding circuit identities involving the 2-qubit gate
• improving the documentation and usage instructions
• ensuring differentiability of variational parameters
• writing more tests (compile at your own risk!)

• IonQ documentation
• Basic circuit compilation techniques for an ion-trap quantum machine

If you use the Ionizer as part of your workflow, we would appreciate if you cite it using the BibTeX below.

@software{di_matteo_2024_10761367,
  author       = {Di Matteo, Olivia},
  title        = {The Ionizer},
  month        = mar,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {0.2},
  doi          = {10.5281/zenodo.10761367},
  url          = {https://doi.org/10.5281/zenodo.10761367}
}