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.
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.
As quantum networking hardware remains costly and not yet widely accessible, simulation tools are essential for the design and evaluation of quantum network architectures and protocols. However, designing a scalable and computationally efficient quantum network simulator is intrinsically challenging: i) quantum dynamics must be emulated on classical computing platforms while capturing the stateful and non-local nature of entanglement, a quantum resource without any classical networking analog; ii) quantum networking is inherently hybrid, as protocol execution also fundamentally depends on classical signaling. This makes a tight and faithful co-simulation of quantum operations and classical message exchanges a core requirement. In this light, we present Q2NS, a modular and extensible quantum network simulator, built on top of ns-3, designed to seamlessly integrate quantum-network primitives with ns-3's established classical protocol stack. Q2NS adopts a modular architecture that decouples protocol control logic from node- and channel-level operations, enabling rapid prototyping and adaptation across heterogeneous and evolving Quantum Internet scenarios. Q2NS natively supports multiple quantum state representations through a unified interface, allowing interchangeable state-vector, density-matrix, and stabilizer backends. We validate Q2NS through realistic use-case studies and comprehensive benchmarks, demonstrating superior computational efficiency over representative state-of-the-art alternatives, while preserving modeling flexibility. Finally, we provide a dedicated visualization tool that jointly captures physical and entanglement-enabled connectivity and supports entangled-state manipulations, facilitating an intuitive interpretation of entanglement dynamics and protocol behavior. Q2NS offers a flexible, open, and scalable simulation platform for advancing Quantum Internet research.
As quantum networking hardware remains costly and not yet widely accessible, simulation tools are essential for the design and evaluation of quantum network architectures and protocols. However, designing a scalable and computationally efficient quantum network simulator is intrinsically challenging: (i) quantum dynamics must be emulated on classical computing platforms while capturing the stateful and non-local nature of entanglement, a unique quantum resource without any classical networking analog; (ii) moreover, quantum networking is inherently hybrid, as protocol execution also fundamentally depends on classical signaling. This makes a tight and faithful co-simulation of quantum operations and classical message exchanges a core requirement. In this light, we present Q2NS, a modular and extensible quantum network simulator, built on top of ns-3, designed to seamlessly integrate quantum-network primitives with ns-3’s established classical protocol stack. Q2NS adopts a modular architecture that decouples protocol control logic from node- and channel-level operations, enabling rapid prototyping and adaptation across heterogeneous and evolving Quantum Internet scenarios. Q2NS natively supports multiple quantum state representations through a unified plug-in interface, allowing interchangeable state-vector, density-matrix, and stabilizer backends. We validate Q2NS through realistic use-case studies and comprehensive benchmarks, demonstrating superior computational efficiency over representative state-of-the-art alternatives, while preserving modeling flexibility. Finally, we provide a dedicated visualization tool that jointly captures physical and entanglement-enabled connectivity and supports entangled-state manipulations, facilitating an intuitive interpretation of entanglement dynamics and protocol behavior. Overall, Q2NS offers a flexible, open, and scalable simulation platform for advancing Quantum Internet research.
As quantum networking hardware remains costly and not yet widely accessible, simulation tools are essential for the design and evaluation of quantum network architectures and protocols. However, designing a scalable and computationally efficient quantum network simulator is intrinsically challenging: (i) quantum dynamics must be emulated on classical computing platforms while capturing the stateful and non-local nature of entanglement, a unique quantum resource without any classical networking analog; (ii) moreover, quantum networking is inherently hybrid, as protocol execution also fundamentally depends on classical signaling. This makes a tight and faithful co-simulation of quantum operations and classical message exchanges a core requirement. In this light, we present Q2NS, a modular and extensible quantum network simulator, built on top of ns-3, designed to seamlessly integrate quantum-network primitives with ns-3’s established classical protocol stack. Q2NS adopts a modular architecture that decouples protocol control logic from node- and channel-level operations, enabling rapid prototyping and adaptation across heterogeneous and evolving Quantum Internet scenarios. Q2NS natively supports multiple quantum state representations through a unified plug-in interface, allowing interchangeable state-vector, density-matrix, and stabilizer backends. We validate Q2NS through realistic use-case studies and comprehensive benchmarks, demonstrating superior computational efficiency over representative state-of-the-art alternatives, while preserving modeling flexibility. Finally, we provide a dedicated visualization tool that jointly captures physical and entanglement-enabled connectivity and supports entangled-state manipulations, facilitating an intuitive interpretation of entanglement dynamics and protocol behavior. Overall, Q2NS offers a flexible, open, and scalable simulation platform for advancing Quantum Internet research.
The performance of quantum neural network models depends strongly on architectural decisions, including circuit depth, placement of parametrized operations, and data-encoding strategies. Selecting an effective architecture is challenging and closely related to the classical difficulty of choosing suitable neural-network topologies, which is computationally hard. This work investigates automated quantum-circuit construction for regression tasks and introduces a genetic-algorithm framework that discovers Reduced Regressor QNN architectures. The approach explores depth, parametrized gate configurations, and flexible data re-uploading patterns, formulating the construction of quantum regressors as an optimization process. The discovered circuits are evaluated against seventeen classical regression models on twenty-two nonlinear benchmark functions and four analytical functions. Although classical methods often achieve comparable results, they typically require far more parameters, whereas the evolved quantum models remain compact while providing competitive performance. We further analyze dataset complexity using twelve structural descriptors and show, across five increasingly challenging meta-learning scenarios, that these measures can reliably predict which quantum architecture will perform best. The results demonstrate perfect or near-perfect predictive accuracy in several scenarios, indicating that complexity metrics offer powerful and compact representations of dataset structure and can effectively guide automated model selection. Overall, this study provides a principled basis for meta-learning-driven quantum architecture design and advances the understanding of how quantum models behave in regression settings--a topic that has received limited exploration in prior work. These findings pave the way for more systematic and theoretically grounded approaches to quantum regression.
Quantum network simulators are essential for the design and evaluation of quantum network architectures and protocols. However, developing an effective simulator is particularly challenging due to the stateful and non-local nature of quantum entanglement, which requires persistent state awareness and tight in-network coordination. Moreover, because quantum communication protocols inherently rely on classical signaling, a realistic simulator must seamlessly integrate classical and quantum communication primitives within a unified framework. In this work, we present Q2NS, a modular and extensible quantum network simulation framework built on top of ns-3. The architectural design of Q2NS enforces a clear separation of concerns between the operational functionalities of modules and entities and the overarching control logic, enabling easy adaptation to heterogeneous and rapidly evolving Quantum Internet paradigms while leveraging ns-3's mature classical networking stack. Additionally, Q2NS includes a dedicated visualization tool to facilitate developing an intuitive understanding of entanglement-based quantum networking. This design enables accurate modeling of entanglement dynamics and protocol control logic, by providing a flexible, open, and scalable platform for advancing the Quantum Internet research.
Quantum network simulators are essential for the design and evaluation of quantum network architectures and protocols. However, developing an effective simulator is particularly challenging due to the stateful and non-local nature of quantum entanglement, which requires persistent state awareness and tight in-network coordination. Moreover, because quantum communication protocols inherently rely on classical signaling, a realistic simulator must seamlessly integrate classical and quantum communication primitives within a unified framework. In this work, we presentQ2NS, a modular and extensible quantum network simulation framework built on top of ns-3. The architectural design ofQ2NSenforces a clear separation of concerns between the operational functionalities of modules and entities and the overarching control logic, enabling easy adaptation to heterogeneous and rapidly evolving Quantum Internet paradigms while leveraging ns-3's mature classical networking stack. Additionally,Q2NSincludes a dedicated visualization tool to facilitate developing an intuitive understanding of entanglement-based quantum networking. This design enables accurate modeling of entanglement dynamics and protocol control logic, by providing a flexible, open, and scalable platform for advancing the Quantum Internet research.