QPIT-SQZsim

QPIT-SQZsim is a simulation framework for designing and analyzing optical parametric oscillators (OPOs), with a focus on cavity design, nonlinear crystal operation, and below-threshold squeezing performance.

The project is structured as a modular pipeline:

cavity → crystal → OPO

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What this project does

This framework allows you to:

• Design optical cavities and evaluate resonator properties and losses
• Compute phase matching and double-resonance conditions in nonlinear crystals
• Select physically consistent operating points
• Simulate below-threshold OPO behavior and squeezing spectra

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Simulation Pipeline

The simulation is performed in three sequential steps:

cavity
   ↓
crystal
   ↓
OPO

• Cavity: computes geometry, mode size, FSR, and loss rates (kappa, escape efficiency)
• Crystal: computes phase matching and double resonance, and selects the active operating point
• OPO: builds the operating-point model and computes squeezing spectra

Each stage consumes the output of the previous one.

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Installation

Clone the repository:

git clone <repository-url>
cd QPIT-SQZsim

Create a virtual environment (recommended):

python -m venv .venv
source .venv/bin/activate   # macOS/Linux
# .venv\Scripts\activate    # Windows

Install dependencies:

pip install -r requirements.txt

Optional (editable install):

pip install -e .

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Quick Start

Run the full simulation pipeline:

python src/cavity/cavity_main.py
python src/crystal/crystal_main.py
python src/opo/opo_main.py

Each step depends on the outputs produced by the previous one.

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Project Structure

Main modules:

• src/cavity — cavity geometry, mode properties, resonant loss model, and derived cavity quantities
• src/crystal — phase matching, double resonance, operating-point selection, and crystal → OPO handoff
• src/opo — OPO operating-point model, Langevin equations, squeezing spectra, and diagnostic plots
• src/common — shared utilities, constants, and results-path helpers used across the pipeline

Representative files:

• src/cavity/cavity_main.py — cavity entry point and geometry/loss configuration
• src/crystal/crystal_main.py — crystal entry point and operating-point selection
• src/opo/opo_main.py — OPO entry point and final simulation stage
• src/common/constants.py — shared physical and numerical constants
• src/common/results_paths.py — common helpers for structured output paths

Documentation and outputs:

• docs/ — high-level and module-specific documentation
• results/ — generated JSON outputs and plots for each simulation stage

For detailed concepts and definitions, see the documentation in the docs/ directory.

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Outputs

Each stage writes results to disk (JSON + plots). These outputs are used as inputs for the next stage in the pipeline.

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Documentation

High-level documentation:

• Overview
• Architecture
• Workflow
• Physics
• Outputs

Module-specific documentation:

• Cavity
• Crystal
• OPO

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Notes

• The current OPO model is:

  • below-threshold
  • degenerate
  • single-mode

• Full non-degenerate and multimode dynamics are not yet implemented.

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Future Work

The current implementation focuses on a below-threshold, degenerate, single-mode OPO model. Several extensions are planned to broaden the physical scope and modeling capabilities of the simulator.

Planned Features

• Non-degenerate OPO model

  • Explicit signal and idler dynamics
  • Two-mode squeezing and frequency correlations

• Pulsed (time-domain) squeezing

  • Broadband / multimode description
  • Time-domain and frequency-resolved correlations beyond single-sideband analysis

• Waveguide-based nonlinear interaction

  • Reduced mode area and enhanced nonlinear coupling
  • Modified overlap, dispersion, and effective interaction length

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Design Approach

These features are intended to be integrated without modifying the high-level pipeline:

cavity → crystal → OPO

Instead of introducing new pipeline stages, they should be implemented as configurable modes within the existing modules:

• Crystal module

  • Extend to support different physical platforms (e.g. bulk vs waveguide)
  • Modify mode area, overlap, and interaction properties accordingly

• OPO module

  • Extend to support different dynamical regimes (e.g. continuous-wave vs pulsed)
  • Generalize the quantum model to include multi-mode and non-degenerate behavior

A possible internal structure is:

• src/crystal/platforms/

  • bulk.py
  • waveguide.py

• src/opo/regimes/

  • cw.py
  • pulsed.py

This approach preserves the modular structure of the project while enabling more advanced physical models to be added incrementally.

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Future Extensions: Optimization

A natural extension of the current framework is the introduction of parameter optimization capabilities.

Thanks to the modular pipeline:

cavity → crystal → OPO

it is possible to study how design parameters affect the final squeezing performance.

Use Cases

This would allow systematic investigation of the impact of:

• Mirror coatings (input/output reflectivity and internal losses)
• Cavity geometry and beam waist
• Crystal length and phase-matching conditions
• Pump operating point

For example, varying mirror reflectivity directly affects:

• cavity losses (kappa)
• escape efficiency
• OPO threshold
• achievable squeezing

Where Optimization Fits in the Pipeline

Optimization should not be implemented as a new stage, but as an external loop around the existing pipeline.

The idea is to repeatedly run:

cavity → crystal → OPO

while modifying selected input parameters (e.g. mirror coatings in the cavity stage).

Each iteration produces a new simulation output, from which a figure of merit (such as the minimum squeezing level) can be extracted.

In practice:

• Cavity stage
  Parameters like mirror reflectivity and losses are varied here. This is typically the primary optimization entry point.

• Crystal stage
  Can be optimized for phase matching or double resonance conditions (e.g. temperature, poling period, crystal length).

• OPO stage
  Evaluates the final performance (squeezing spectrum), which serves as the optimization target.

This structure allows the simulator to be used not only for analysis, but also as a design tool for exploring and optimizing OPO configurations.