Rate-Fidelity Tradeoffs in All-Photonic and Memory-Equipped Quantum Switches

• URL: http://arxiv.org/abs/2603.02610v1
• Date: Tue, 03 Mar 2026 05:30:06 GMT
• Title: Rate-Fidelity Tradeoffs in All-Photonic and Memory-Equipped Quantum Switches
• Authors: Panagiotis Promponas, Leonardo Bacciottini, Paul Polakos, Gayane Vardoyan, Don Towsley, Leandros Tassiulas,
• Abstract summary: Quantum entanglement switches are a key building block for early quantum networks.<n>We compare two architectures: an all-photonic entanglement generation switch (EGS) that repeatedly attempts Bell-state measurements (BSM) without storing qubits, and a quantum memory-equipped switch that buffers entanglement and triggers measurements only when heralded connectivity is available.<n>We formalize both models under a common hardware abstraction and characterize their achievable rate-fidelity regions, yielding a benchmarking methodology that translates hardware and protocol parameters into network-level performance.
• Score: 12.71781482098232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum entanglement switches are a key building block for early quantum networks, and a central design question is whether near-term devices should use only flying photons or also incorporate quantum memories. We compare two architectures: an all-photonic entanglement generation switch (EGS) that repeatedly attempts Bell-state measurements (BSM) without storing qubits, and a quantum memory-equipped switch that buffers entanglement and triggers measurements only when heralded connectivity is available (herald-then-swap control). These two designs trade off simple, memoryless operation that avoids decoherence and memory-induced latency against heralding-based control that buffers entanglement to use BSMs more efficiently. We formalize both models under a common hardware abstraction and characterize their achievable rate-fidelity regions, yielding a benchmarking methodology that translates hardware and protocol parameters into network-level performance. Numerical evaluation quantifies the rate-fidelity tradeoffs of both models, identifies operating regions in which each architecture dominates, and shows how hardware and protocol knobs can be tuned to meet application-specific targets.

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