Learn FOREST-SDK with Real Code Examples

Install Python (e.g., 3.7+)

Install pyQuil via `pip install pyquil` :contentReference[oaicite:2]{index=2}

Download Forest SDK (includes QVM and quilc) from Rigetti (requires requesting link) :contentReference[oaicite:3]{index=3}

Set up QCS configuration if you want to access real hardware :contentReference[oaicite:4]{index=4}

Verify installation by running simple Quil programs and using QVM / quilc

Install Python and pyQuil

Download and install Forest SDK (QVM + quilc) from Rigetti

Set up QCS credentials if using real hardware

Install any classical optimizer libraries (e.g. SciPy) for hybrid algorithms

Verify by running small example programs

qcs_config.toml - for QCS (quantum cloud) configuration (if using hardware)

pyquil‑scripts/ - your quantum programs in Python

quil/ - raw Quil files (optional)

notebooks/ - Jupyter notebooks organizing experiments

data/ - results, measurement outcomes, logs

qvm - run the Quantum Virtual Machine (simulator)

quilc - start the Quil compiler server

pyquil scripts via Python (e.g. `python my_prog.py`)

QCS command-line tools / REST API for job submission

Forest‑Benchmarking tools for characterization (if used)

Global developer community around Forest and pyQuil

Documentation primarily in English

Used in academic research worldwide

Open-source nature encourages international contributions

Supports global cloud-based quantum experiments via QCS (internet access)

Python-based - widely accessible to developers

Open-source components (pyQuil, quilc, QVM) :contentReference[oaicite:15]{index=15}

Cloud access (QCS) available for real hardware

Hybrid algorithm support lowers barrier for classical + quantum developers

Documentation and examples from Rigetti and community

Jupyter notebooks for interactive quantum programming

Python scripts for production workflows

CLI for running QVM and compiler services

Visualization via matplotlib or custom tools for measurement statistics

Logging and dashboards (if integrated with classical infrastructure)

Track versions of Quil programs and pyQuil scripts

Store compiled versions of programs (quilc outputs)

Log measurement results from QVM / QPU

Maintain reproducible experiments via seed and parameter logging

Manage QCS jobs and track their status/results

Serialize measurement outcomes for later analysis

Use Jupyter or other notebooks to document experiments

Store classical optimizer history (for hybrid algorithms)

Archive compiled Quil and raw program code

Maintain version control for scripts and Quil files