FatQat

FatQat is a quantum-computing toolkit built around one authoring interface: Program. Write the computation once, then choose how closely to model the machine beneath it.

Development status: FatQat is under active development, and its interfaces may change between releases. Pin an exact version when reproducibility matters.

The execution targets accept the same Program type and return results through the same Job/Result workflow. Each target still validates what it can physically or mathematically realize.

Installation

FatQat requires Python 3.12 or newer. Install FatQat:

python -m pip install fatqat

To work on the development version, install from a source checkout:

git clone https://github.com/spaceqat/fatqat.git
cd fatqat
python -m pip install .

Run a first Program

This Bell-state example contains the complete circuit-level workflow:

import fatqat as fq
import fatqat.operations as ops

program = fq.Program(2, 2)
program.add(ops.H, 0)
program.add(ops.CX, (0, 1))
program.measure_all()

result = fq.simulator.Simulator().run(
    program,
    shots=1000,
    simulation_config={"seed": 7},
).result()

print(result.get_counts())

Only 00 and 11 appear: the measured bits agree because the two qubits are entangled. The quickstart draws this Program, runs it, and turns the counts into a plot.

Grow the same authoring model

Program records registers and ordered instructions. It supports gates, measurement, reset, classical conditions, reusable parameters, logical qudits, mixed local dimensions, circuit drawing, and direct physical controls. These features stay together instead of splitting into separate circuit and pulse languages.

The Program guide builds those ideas step by step. Choose how much physics to model then runs one unchanged rotation through all three execution levels.

From there:

• Simulate a quantum program for states, sampling, and parameter sweeps.
• Ask questions of a run for counts, states, maps, and observables.
• Compare ideal and noisy execution.
• Measure performance and scaling.
• Test a hardware profile.
• Follow a Program into physical dynamics, then continue with the transmon or neutral-atom workflow.
• Connect OpenQASM and Qiskit.

The tutorial gallery contains longer algorithm and physics case studies. The API reference contains the exact signatures, supported operations, shapes, units, and validation contracts.

Release notes are in the changelog.

Development

For basic local development, install the source tree with the core test dependencies:

python -m pip install --upgrade pip
python -m pip install --editable . --group dev
python -m pytest

Before preparing a contribution, install the full test and lint environment:

python -m pip install --editable . --group test-full --group lint

test-full includes the dev dependencies and the optional Qiskit integration dependencies. lint adds Black and Pylint.

Before submitting a change, read Contributing to FatQat, including the policy for AI-assisted work. AI tools are permitted, but every contributor must understand, own, and lead the work they submit and the project conversations around it.

For documentation changes, follow the pinned setup and build workflow.

The main repository directories are:

• src/fatqat/ — package source.
• tests/ — behavior-focused test suite.
• docs/mkdocs/ — Material user guide, executable tutorials, and API reference.