First experience

Build parameterized quantum circuit

The below example shows how to build a parameterized quantum circuit.

from mindquantum import *
import numpy as np

encoder = Circuit().h(0).rx({'a0': 2}, 0).ry('a1', 1)
print(encoder)
print(encoder.get_qs(pr={'a0': np.pi / 2, 'a1': np.pi / 2}, ket=True))

Then you will get,

q0: ────H───────RX(2*a0)──

q1: ──RY(a1)──────────────

-1/2j¦00⟩
-1/2j¦01⟩
-1/2j¦10⟩
-1/2j¦11⟩

In jupyter notebook, we can just call svg() of any circuit to display the circuit in svg picture (dark and light mode are also supported).

circuit = (qft(range(3)) + BarrierGate(True)).measure_all()
circuit.svg()

Train quantum neural network

ansatz = CPN(encoder.hermitian(), {'a0': 'b0', 'a1': 'b1'})
sim = Simulator('projectq', 2)
ham = Hamiltonian(-QubitOperator('Z0 Z1'))
grad_ops = sim.get_expectation_with_grad(
    ham,
    encoder + ansatz,
    encoder_params_name=encoder.params_name,
    ansatz_params_name=ansatz.params_name,
)

import mindspore as ms

ms.context.set_context(mode=ms.context.PYNATIVE_MODE, device_target='CPU')
net = MQLayer(grad_ops)
encoder_data = ms.Tensor(np.array([[np.pi / 2, np.pi / 2]]))
opti = ms.nn.Adam(net.trainable_params(), learning_rate=0.1)
train_net = ms.nn.TrainOneStepCell(net, opti)
for i in range(100):
    train_net(encoder_data)
print(dict(zip(ansatz.params_name, net.trainable_params()[0].asnumpy())))

The trained parameters are,

{'b1': 1.5720831, 'b0': 0.006396801}

Tutorials

Basic usage
- Variational Quantum Circuit
Variational quantum algorithm