Partially-Blind Single-Qubit Classification over a Prototype Hybrid Quantum Network

• URL: http://arxiv.org/abs/2607.01998v1
• Date: Thu, 02 Jul 2026 10:32:34 GMT
• Title: Partially-Blind Single-Qubit Classification over a Prototype Hybrid Quantum Network
• Abstract summary: Single-qubit classifiers (SQCs) are small-scale hybrid quantum-classical machines capable of performing a basic machine learning task: classifying data.<n>Blind quantum computation (BQC) enables a client may run delegated quantum computations on an untrusted server with information-theoretic security.<n>We propose a prototype experiment for a SQC where it is known to the server that a classification is being performed, but the data and outcome stay hidden.<n>This can be integrated into a quantum network to deliver quantum-secured classifications to remote clients.
• Score: 33.72751145910978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the NISQ era, there is a need for resource-efficient proof-of-principle experiments that can be built up to genuine utility. Single-qubit classifiers (SQCs) are small-scale hybrid quantum-classical machines capable of performing a basic machine learning task: classifying data. In principle, these can be scaled up to many-qubit quantum classifiers capable of quantum computational advantage. Another type of quantum advantage is enabled by blind quantum computation (BQC), wherein a client may run delegated quantum computations on an untrusted server with information-theoretic security. In this paper, we develop a framework and propose a prototype experiment for a SQC where it is known to the server that a classification is being performed, but the data and outcome stay hidden, i.e., it performs partially-blind SQC (PB-SQC). This can be integrated into a quantum network to deliver quantum-secured classifications to remote clients; we study this for a heterogeneous quantum network link in which entanglement is shared between a server and a client equipped with a multiplexed solid-state quantum memory using entanglement swapping. The framework we develop for PB-SQC on this setup is tested in a simulation with realistic hardware parameters on a real-world credit card transaction fraud database with classification outcomes approaching those of its equivalent classical deep-belief network. In addition, we show how a two-qubit classifier (TQC) instead of a SQC enables verification of the computation. These results pave the way towards a short- to mid-term quantum network offering use-case-ready quantum applications.

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