User Guide#
This guide covers how to use FlagQuantum for quantum circuit simulation and training: building programs, planning and running them, training with PyTorch, choosing a simulation representation, adding noise and measurement, scaling across ranks, and moving the same program to hardware.
Build a circuit, plan it, run it and read the result.
Circuit construction, FlagQuantum IR, compilation and routing.
Trainable circuits, optimizers, named parameters and checkpoints.
Observables, result accessors, noise models and dynamic circuits.
Statevector, MPS, tensor network and JAX kernels.
Sharded statevector and rank-owned MPS training.
FlagOS accelerators, remote jobs, deployment packages.
Algorithm units, error-correction experiments and digital twins.
The tutorial series and the example catalog.
Test tiers and marker commands.