We introduce the Simulator for Quantum Networks and Channels ($\texttt{SQUANCH}$), an open-source Python library for creating parallelized simulations of distributed quantum information processing. The framework includes many features of a general-purpose quantum computing simulator, but it is optimized specifically for simulating quantum networks. It includes functionality to allow users to easily design complex multi-party quantum networks, extensible classes for modeling noisy quantum channels, and a multiprocessed NumPy backend for performant simulations. We present an overview of the structure of the library, describing how the various API elements represent the underlying physics and providing simple usage examples for each module. Finally, we present several demonstrations of canonical quantum information protocols implemented using this framework.
We introduce the Simulator for Quantum Networks and Channels (SQUANCH), an open-source Python library for creating parallelized simulations of distributed quantum information processing. The framework includes many features of a general-purpose quantum computing simulator, but it is optimized specifically for simulating quantum networks. It includes functionality to allow users to easily design complex multi-party quantum networks, extensible classes for modeling noisy quantum channels, and a multiprocessed NumPy backend for performant simulations. We present an overview of the structure of the library, describing how the various API elements represent the underlying physics and providing simple usage examples for each module. Finally, we present several demonstrations of canonical quantum information protocols implemented using this framework.
As quantum network technologies develop, the need for teaching and engineering tools such as simulators and emulators rises. QuNetSim addresses this need. QuNetSim is a Python software framework that delivers an easy-to-use interface for simulating quantum networks at the network layer, which can be extended at little effort of the user to implement the corresponding link layer protocols. The goal of QuNetSim is to make it easier to investigate and test quantum networking protocols over various quantum network configurations and parameters. The framework incorporates many known quantum network protocols so that users can quickly build simulations using a quantum-networking toolbox in a few lines of code and so that beginners can easily learn to implement their own quantum networking protocols. Unlike most current tools, QuNetSim simulates with real time and is, therefore, well suited to control laboratory hardware. Here, we present a software design overview of QuNetSim and demonstrate examples of protocols implemented with it. We describe ongoing work, which uses QuNetSim as a library, and describe possible future directions for the development of QuNetSim.
Abstract The simulation of quantum circuits on classical computers is an important problem in quantum computing. Such simulation requires representations of distributions over very large sets of basis vectors, and recent work has used symbolic data-structures such as Binary Decision Diagrams (BDDs) for this purpose. In this tool paper, we present Quasimodo, an extensible, open-source Python library for symbolic simulation of quantum circuits. Quasimodo is specifically designed for easy extensibility to other backends. Quasimodo allows simulations of quantum circuits, checking properties of the outputs of quantum circuits, and debugging quantum circuits. It also allows the user to choose from among several symbolic data-structures—both unweighted and weighted BDDs, and a recent structure called Context-Free-Language Ordered Binary Decision Diagrams (CFLOBDDs)—and can be easily extended to support other symbolic data-structures.
Quantum networks in their most general configuration consist of nodes, containing sources, detectors, memories, and logic gates, connected by both quantum and classical channels. Quantum networks leverage the unique properties of quantum mechanics to enable "beyond classical" communication, sensing, and computing protocols. The research community has recently emphasized computational simulation of these networks as an approach to gauging their performance, understanding their properties, and considering the impact of technical standards. Despite this, the implications of quantum networks in military contexts with limited resources remains underexplored. We review a recently introduced quantum network simulation framework that we developed, named QuanTACT, that is specifically tailored for integration into existing tactical simulation tools, such as the Extendable Mobile Ad hoc Network Emulator (EMANE). The QuanTACT framework is built from the SQUANCH quantum network simulation tool by adding quantum channel models for optical fiber and various subroutines required for entanglement-based QKD simulation such as key sifting, information reconciliation, and privacy amplification. Our choice of SQUANCH as opposed to other quantum network simulation tools is due to the agent-based model utilized by SQUANCH which allows for fully parallelized simulations with multiple agents running their own processes. This distributed architecture is, by design, consistent with EMANE and facilitates future integration. Building off of benchmark results based on field-deployed QKD experiments, we investigate the computational scaling properties of this system for linear chains of nodes that exchange keys via an XOR operation.
Abstract Distributed quantum information processing is essential for building quantum networks and enabling more extensive quantum computations. In this regime, several spatially separated parties share a multipartite quantum system, and the most natural set of operations is Local Operations and Classical Communication (LOCC). As a pivotal part in quantum information theory and practice, LOCC has led to many vital protocols such as quantum teleportation. However, designing practical LOCC protocols is challenging due to LOCC’s intractable structure and limitations set by near-term quantum devices. Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks. As applications, we explore various quantum information tasks such as entanglement distillation, quantum state discrimination, and quantum channel simulation. We discover protocols with evident improvements, in particular, for entanglement distillation with quantum states of interest in quantum information. Our approach opens up new opportunities for exploring entanglement and its applications with machine learning, which will potentially sharpen our understanding of the power and limitations of LOCC. An implementation of LOCCNet is available in Paddle Quantum, a quantum machine learning Python package based on PaddlePaddle deep learning platform.
Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues for building networks that scale with the number of users, communication distance, and application demands by simulating alternative hardware designs and control protocols. Several quantum network simulators have been recently developed with these goals in mind. As the size of the simulated networks increases, however, sequential execution becomes time-consuming. Parallel execution presents a suitable method for scalable simulations of large-scale quantum networks, but the unique attributes of quantum information create unexpected challenges. In this work, we identify requirements for parallel simulation of quantum networks and develop the first parallel discrete-event quantum network simulator by modifying the existing serial simulator SeQUeNCe. Our contributions include the design and development of a quantum state manager (QSM) that maintains shared quantum information distributed across multiple processes. We also optimize our parallel code by minimizing the overhead of the QSM and decreasing the amount of synchronization needed among processes. Using these techniques, we observe a speedup of 2 to 25 times when simulating a 1,024-node linear network topology using 2 to 128 processes. We also observe an efficiency greater than 0.5 for up to 32 processes in a linear network topology of the same size and with the same workload. We repeat this evaluation with a randomized workload on a caveman network. We also introduce several methods for partitioning networks by mapping them to different parallel simulation processes. We have released the parallel SeQUeNCe simulator as an open source tool alongside the existing sequential version.
We present a framework for the unification and standardization of quantum network protocols, making their realization easier and expanding their use cases to a broader range of communities interested in quantum technologies. Our framework is available as an open-source repository, the Quantum Protocol Zoo. We follow a modular approach by identifying two key components: Functionality, which connects real-world applications; and Protocol, which is a set of instructions between two or many parties, at least one of which has a quantum device. Based on the different stages of the quantum internet and use-case in the commercialization of quantum communication, our framework classifies quantum cryptographic functionalities and the various protocol designs implementing these functionalities. Towards this classification, we introduce a novel concept of resource visualization for quantum protocols, which includes two interfaces: one to identify the building blocks for implementing a given protocol and another to identify accessible protocols when certain physical resources or functionalities are available. Such classification provides a hierarchy of quantum protocols based on their use-case and resource allocation. We have identified various valuable tools to improve its representation with a range of techniques, from abstract cryptography to graphical visualizations of the resource hierarchy in quantum networks. We elucidate the structure of the zoo and its primary features in this article to a broader class of quantum information scientists, physicists, computer science theorists and end-users. Since its introduction in 2018, the quantum protocol zoo has been a cornerstone in serving the quantum networks community in its ability to establish the use cases of emerging quantum internet networks. In that spirit we also provide some of the applications of our framework from different perspectives.