QuantumSavory: Write Symbolically, Run on Any Backend -- A Unified Simulation Toolkit for Quantum Computing and Networking

TL;DR

The paper tackles the need for end-to-end, cross-layer modeling in quantum computing and networking by introducing QuantumSavory, a Julia-based toolkit with a symbolic frontend and interchangeable numerical backends. It enables backend-agnostic descriptions of quantum states, operations, and protocols, orchestrated by a discrete-event LOCC engine and coordinated via a tagging/messaging system, complemented by modular Zoos of reusable components. Through full-stack examples and a cross-tool comparison, the authors demonstrate how QSavory supports scalable, reusable codesign across heterogeneous quantum hardware and network dynamics. The work promises practical impact by enabling digital-twin style simulations, optimization with autodifferentiation, and rapid experimentation across backends and protocols, with future plans for tensor-network backends, surrogates, and richer physical models.

Abstract

Progress in quantum computing and networking depends on codesign across abstraction layers: device-level noise and heterogeneous hardware, algorithmic structure, and distributed classical control. We present QuantumSavory, an open-source toolkit built to make such end-to-end studies practical by cleanly separating a symbolic computer-algebra frontend from interchangeable numerical simulation backends. States, operations, measurements, and protocol logic are expressed in a backend-agnostic symbolic language; the same model can be executed across multiple backends (e.g., stabilizer, wavefunction, phase-space), enabling rapid exploration of accuracy-performance tradeoffs without rewriting the model. Furthermore, new custom backends can be added via a small, well-defined interface that immediately reuses existing models and protocols. QuantumSavory also addresses the classical-quantum interaction inherent to LOCC protocols via discrete-event execution and a tag/query system for coordination. Tags attach structured classical metadata to quantum registers and message buffers, and queries retrieve, filter, or wait on matching metadata by wildcards or arbitrary predicates. This yields a data-driven control plane where protocol components coordinate by publishing and consuming semantic facts (e.g., resource availability, pairing relationships, protocol outcomes) rather than by maintaining rigid object graphs or bespoke message plumbing, improving composability and reuse as models grow. Our toolkit is also not limited to qubits and Bell pairs; rather, any networking dynamics of any quantum system under any type of multipartite entanglement can be tackled. Lastly, QuantumSavory ships reusable libraries of standard states, circuits, and protocol building blocks with consistent interfaces, enabling full-stack examples to be assembled, modified, and compared with minimal glue code.

Figures (9)

• Figure 1: Comparison to existing tools: NetSquid coopmans2021netsquid, SimulaQron dahlberg2018simulaqron, SeQUeNCe wu2021sequence, Squanch bartlett2018distributed, QuISP satoh2022quisp, and QuNetSim diadamo2021qunetsim, versus QuantumSavory. All of these tools are quite capable, however we focus on the new features we consider of the greatest importance for a scalable full-stack codesign toolkit.
• Figure 2: Two largely independent axes determine the cost--fidelity tradeoffs of classical algorithms for modeling quantum systems. The first axis concerns how classical uncertainty and correlations are represented: one can evolve explicit, dense probability objects (e.g., exponentially large stochastic vectors or large coupled rate equations), or instead use sparse, sample-based representations such as Monte Carlo trajectories that concentrate effort on the parts of the distribution that matter; for realistic network-scale studies, the dense option is rarely the right tool (and thus the use of density matrices instead of Monte Carlo over state vectors is almost always misguided and expensive). The second axis concerns how quantum correlations and entanglement are represented: exact state-vector or density-matrix methods incur unavoidable exponential scaling in the generic case, but specialized representations can be highly efficient for restricted dynamics (e.g. tensor-network methods for low-entanglement structure, or Clifford and Gaussian formalisms for restricted gate and noise sets). Pushing simplification too far can be counterproductive: extremely coarse models that track only a few summary parameters (e.g., fidelities of Werner-state pairs) are only marginally more efficient but often discard the dynamical information needed to make accurate protocol- and hardware-level decisions.
• Figure 3: The operation of (a) two-way and (b) one-way quantum networks to distribute entanglement. End nodes and repeaters are in blue. Red lines represent an established Bell pair. "BSM" (Bell state measurement) is process which can enable an "entanglement swap", turning two short-distance pairs into one long distance pair.
• Figure 4: A high-level illustration of (a) entanglement distillation and (b) quantum error correction. Dashed lines represent low-quality entanglement, solid lines signify improved quality (a.k.a. fidelity). While the error-correction approach is not heralded, i.e. it always reports a success, it might suffer from more "false positives". The probabilistic distillation process offers a different tradeoff between rate of entanglement and fidelity, a central question studied in quantum networking.
• Figure 5: The cluster state preparation example from the QSavory overview section. EntanglerProt is called on opposing edge pairs to entangle communication qubits. Two opposite edges are attempted first since they do not overlap and can be done in parallel. After the first round of entanglement generation, the quantum states are transferred to the storage slots. Then, the remaining edges are entangled (again through their communication qubits). Lastly, fusion circuits are executed, fusing the four pairs into a single cluster state stored in the storage qubits.