Getting Started

This section covers the requirements for installing FlagQuantum and guides you through the installation process.

Your first quantum model

FlagQuantum is a PyTorch-first framework, so the shortest path to a working program is a small trainable circuit. The example below builds a two-qubit circuit, learns its rotation angle by minimizing the measured expectation value, and prints the trained measurement. It needs no GPU, no credentials and no optional backend.

import torch
import flagquantum as fq


def circuit(parameters):
    return fq.Circuit(2).ry(0, parameters[0]).cx(0, 1)


model = fq.Module(circuit, n_parameters=1, init=torch.tensor([0.25]))
training = fq.train(
    model,
    optimizer=torch.optim.Adam(model.parameters(), lr=0.05),
    objective=lambda z: z.mean(),
    steps=10,
)

trained_circuit = circuit(next(model.parameters()).detach())
measurement = fq.expectation(fq.Z(0))
result = fq.run(trained_circuit, outputs=measurement)
print(result.expectation())

Continue with:

• Requirements for the supported Python, PyTorch and hardware platforms (including the optional dependency groups);

• Install FlagQuantum to install the released package or a development checkout and verify it;

• the User Guide for circuits, training, representations, distributed execution and hardware targets.