A Modular PyTheus Quantum Network Interpreter: Automated Analysis and Visualization of Optimized Quantum Architectures

TL;DR

This paper introduces a modular interpreter for PyTheus-optimized quantum networks that automates analysis and visualization, aiding understanding of complex machine-designed quantum architectures.

Contribution

It provides the first robust, modular algorithms for analyzing and visualizing diverse PyTheus-generated quantum network architectures.

Findings

Successfully analyzes complex quantum network structures

Reveals functional roles and architecture features

Validates with multiple quantum network examples

Abstract

We present a modular interpreter for PyTheus-optimized quantum networks that automatically analyzes and visualizes complex quantum architectures discovered through automated optimization. The interpreter addresses the critical challenge of understanding machine-designed quantum networks by providing robust algorithms for functional role identification, graph-theoretical analysis, and physically meaningful visualization across the major classes of PyTheus-generated networks. Our interpreter accepts both file-based and in-memory network representations, automatically identifies sources, detectors, beam splitters, and ancillas through priority-based classification, and generates coordinated native graph plots and optical table representations. We demonstrate the interpreter's capabilities through two complementary approaches: (1) analysis of a newly developed five-node quantum key distribution network that reveals distributed source architecture and dual-role node functionality, and (2) comprehensive validation using existing PyTheus examples including W4 state generation, heralded Bell state preparation, and GHZ state networks. The interpreter successfully handles complex connectivity patterns across diverse quantum network architectures within the tested classes, avoids visualization artifacts, and provides validation mechanisms for architectural consistency. Our primary contribution is the development of robust modular interpretation algorithms that can analyze the major classes of PyTheus-generated quantum networks, enabling better understanding of automated quantum architecture design.