Learn STRAWBERRY-FIELDS with Real Code Examples

Install Python 3.8+

Install Strawberry Fields via `pip install strawberryfields`

Optionally install TensorFlow or PyTorch for quantum ML

Verify installation with `import strawberryfields as sf`

Test built-in simulators using example circuits

Install Python 3.8+

Install Strawberry Fields via pip

Install optional ML libraries (TensorFlow, PyTorch)

Set up environment variables for hardware backends

Verify by running example circuits

config.toml - optional backend configuration

notebooks/ - interactive example programs

circuits/ - user-defined circuit modules

simulations/ - store simulation results

scripts/ - automation and utility functions

python example.py - run a Strawberry Fields script

sf --list-backends - list available simulators and hardware

sf --run-prog prog.py - execute a circuit

sf --export-circuit prog.py - export circuit diagram

sf --analyze-state prog.py - visualize quantum state

Used globally in research and academia

Documentation primarily in English

Compatible with international quantum algorithm standards

Integrates with widely-used Python ML frameworks

Community contributions worldwide

Python-based and cross-platform

Open-source Apache 2.0 license

Accessible for research, education, and hybrid algorithm development

Community tutorials and example notebooks available

Supports photonic hardware and local simulators

Jupyter notebooks for interactive experimentation

Matplotlib or Plotly for visualization

CLI outputs for simulation results

Optional GUI tools for circuit construction

Integration with dashboards for analysis

Track quantum program versions

Log simulation and hardware results

Store measurement outcomes

Maintain reproducibility

Manage hybrid quantum-classical pipelines

Serialize measurement data and states

Cache simulation results

Document gate parameters and circuit structure

Organize experiments for benchmarking

Analyze classical-quantum interface outputs