A framework for partially-blind single-qubit classification (PB-SQC) is proposed and simulated on a credit-card fraud dataset using realistic hardware parameters in a heterogeneous quantum network, with performance approaching a classical deep-belief network.
Hybrid Single-Ion Atomic-Ensemble Node for High-Rate Remote Entanglement Generation
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abstract
Different quantum systems possess different favorable qualities. On the one hand, ensemble-based quantum memories are suited for fast multiplexed long-range entanglement generation. On the other hand, single-atomic systems provide access to gates for processing of information. Both of those can provide advantages for high-rate entanglement generation within quantum networks. We develop a hybrid architecture that takes advantage of these properties by combining trapped-ion nodes and nodes comprised of spontaneous parametric down conversion photon pair sources and absorptive memories based on rare-earth ion ensembles. To this end, we solve the central challenge of matching the different bandwidths of photons emitted by those systems in an initial entanglement-generation step. This enables the parallel execution of multiple probabilistic tasks in the initial stage. As a particular example, we show that our approach can lead to a significant speed-up for the fundamental task of creating ion-ion entanglement over hundreds of kilometers in a quantum network.
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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Partially-Blind Single-Qubit Classification over a Prototype Hybrid Quantum Network
A framework for partially-blind single-qubit classification (PB-SQC) is proposed and simulated on a credit-card fraud dataset using realistic hardware parameters in a heterogeneous quantum network, with performance approaching a classical deep-belief network.