MQSS Pennylane Adapter

This repository implements a custom PennyLane backend called MQSSPennylaneDevice, which is able to send quantum jobs to LRZ's infrastructure using the PennyLane frontend. The users would be able to use all full-fletched PennyLane functions (optimization, QML etc.) while running their jobs on LRZ's Quantum Hardware.

🛠️ Installation

To install the package, simply run

pip install mqss-pennylane-adapter

🚀 Usage

MQSS PennyLane Provider has support for most of the native PennyLane features. For instance, you can define a quantum circuit using PennyLane quantum gates, and decorate the method with the MQSSPennylaneDevice object. Parametric gates can also be used.

import pennylane as qml
from pennylane import numpy as np
from mqss.pennylane_adapter.device import MQSSPennylaneDevice

dev = MQSSPennylaneDevice(wires=2, token=MQSS_TOKEN, backends=MQSS_BACKENDS)

@qml.qnode(dev)
def quantum_function_expval(x, y):
    """
    The function `quantum_function_expval` applies quantum operations RZ, CNOT, and RY to qubits and returns
    the expectation value of PauliZ on the second qubit.

    :param x: The parameter `x` in the `quantum_function_expval` represents the angle for the rotation gate
    `RZ` applied on the qubit at wire 0
    :param y: The parameter `y` in the `quantum_function_expval` function is used as the angle parameter for
    the rotation gate `RY(y, wires=1)`. This gate applies a rotation around the y-axis of the Bloch
    sphere by an angle `y` to the qubit on wire
    :return: The function `quantum_function_expval` returns the expected value of the given operator
    """
    qml.RZ(x, wires=0)
    qml.CNOT(wires=[0, 1])
    qml.RY(y, wires=1)
    qml.CNOT(wires=[1, 0])
    qml.RX(x, wires=1)
    return qml.expval(qml.PauliX(0) @ qml.PauliZ(1))
params = [np.pi / 3, np.pi / 17]
result = quantum_function_expval(*params)

Furthermore, you can define a Hamiltonian object within PennyLane, and calculate the expectation value with respect to that Hamiltonian. For these cases, Pennylane Provider simply creates a batch job for each term in the Hamiltonian, to calculate the expectation value.

import pennylane as qml
from pennylane import numpy as np
from mqss.pennylane_adapter.device import MQSSPennylaneDevice
dev_hamiltonian = MQSSPennylaneDevice(wires=2, token='<MQSS_TOKEN>', backends='<MQSS_BACKENDS>')
@qml.qnode(dev_hamiltonian)
def quantum_function_hamiltonian_expval(
    x: float, y: float, H: qml.Hamiltonian
) -> float:
    """
    The function `quantum_function_expval` applies quantum operations RZ, CNOT, and RY to qubits and returns
    the expectation value of PauliZ on the second qubit.

    :param x: The parameter `x` in the `quantum_function_expval` represents the angle for the rotation gate
    `RZ` applied on the qubit at wire 0
    :param y: The parameter `y` in the `quantum_function_expval` function is used as the angle parameter for
    the rotation gate `RY(y, wires=1)`. This gate applies a rotation around the y-axis of the Bloch
    sphere by an angle `y` to the qubit on wire
    :return: The function `quantum_function_expval` returns the expected value of a given operator
    :H: Pennylane Hamiltonian object
    """
    arbitrary_quantum_circuit(x, y)

    return qml.expval(H)

J = 0.5  # Interaction strength
h = 0.2  # Transverse field strength
coeffs = [-J, -h, -h]  # TFIM with 2 sites
obs = [
    qml.PauliZ(0) @ qml.PauliZ(1),  # Ising interaction between sites 0 and 1
    qml.PauliX(0),
    qml.PauliX(1),
]

hamiltonian = qml.Hamiltonian(coeffs, obs)
result = quantum_function_hamiltonian_expval(*params, hamiltonian)

🛠️ Upcoming Features

• Autograd support with Parameter-Shift
• Grouping of commuting terms in the Hamiltonians to reduce the number of circuits in the batch

🤝 Contributing

Feel free to open issues or submit pull requests to improve this project!