convert.py

"""
MIT License

Copyright (c) 2020-present TorchQuantum Authors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import torch
import argparse

import torchquantum as tq
import torchquantum.functional as tqf

import random
import numpy as np


class QLayer(tq.QuantumModule):
    def __init__(self):
        super().__init__()
        self.n_wires = 4
        self.random_layer = tq.RandomLayer(n_ops=50, wires=list(range(self.n_wires)))

        # gates with trainable parameters
        self.rx0 = tq.RX(has_params=True, trainable=True)
        self.ry0 = tq.RY(has_params=True, trainable=True)
        self.rz0 = tq.RZ(has_params=True, trainable=True)
        self.crx0 = tq.CRX(has_params=True, trainable=True)

    @tq.static_support
    def forward(self, q_device: tq.QuantumDevice):
        """
        1. To convert tq QuantumModule to qiskit or run in the static
        model, need to:
            (1) add @tq.static_support before the forward
            (2) make sure to add
                static=self.static_mode and
                parent_graph=self.graph
                to all the tqf functions, such as tqf.hadamard below
        """
        self.q_device = q_device

        self.random_layer(self.q_device)

        # some trainable gates (instantiated ahead of time)
        self.rx0(self.q_device, wires=0)
        self.ry0(self.q_device, wires=1)
        self.rz0(self.q_device, wires=3)
        self.crx0(self.q_device, wires=[0, 2])

        # add some more non-parameterized gates (add on-the-fly)
        tqf.hadamard(
            self.q_device, wires=3, static=self.static_mode, parent_graph=self.graph
        )
        tqf.sx(self.q_device, wires=2, static=self.static_mode, parent_graph=self.graph)
        tqf.cnot(
            self.q_device,
            wires=[3, 0],
            static=self.static_mode,
            parent_graph=self.graph,
        )


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--pdb", action="store_true", help="debug with pdb")

    args = parser.parse_args()

    if args.pdb:
        import pdb

        pdb.set_trace()

    seed = 0
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)

    q_model = QLayer()
    # convert the tq module to qiskit and draw
    from torchquantum.plugin import tq2qiskit, qiskit2tq

    circ = tq2qiskit(tq.QuantumDevice(n_wires=q_model.n_wires), q_model, draw=True)

    # convert the QiskitCircuit to tq module
    q_model_back = qiskit2tq(circ)
    print(q_model_back)


if __name__ == "__main__":
    main()