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arXiv:2607.01998

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

Matteo Pasini, Tzula Benjamin Propp, Janice van Dam, Garazi Muguruza Lasa, Alexandre Wanick, Hugues de Riedmatten, Gustavo C. do Amaral

quant-phSubmitted Jul 2, 2026• Announced Jul 3, 2026• 0 citations
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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.

Comments
21 pages, 10 figures

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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.
Blind Quantum Computation (BQC) enables clients to delegate quantum computations to a quantum server while maintaining the privacy of their data and algorithms, even when the server is untrusted. In this work, we extend BQC frameworks to Quantum Machine Learning (QML) by implementing a network of entangled clients and a quantum server. Specifically, we explore the integration of Variational Quantum Classifiers (VQC) and Quantum Convolutional Neural Networks (QCNNs) within this paradigm. Our proposed model allows clients to perform classical preprocessing and optimization locally while leveraging the quantum server for computationally expensive quantum tasks. The entanglement-based network is managed by a controller that dynamically allocates resources according to the BQC protocol, allowing secure and efficient execution. We present simulation results indicating the feasibility of this approach, including an analysis of network efficiency and resource consumption, alongside the F1 score on QML benchmark datasets.
Blind quantum computation (BQC) provides a solution for clients with limited quantum capabilities to delegate their quantum computational tasks to remote quantum servers while keeping their own data private. In this paper, we first propose three multi-party verifiable blind quantum computation (MPVBQC) protocols, each of which can handle one type of clients with certain simple quantum capabilities such as making single-qubit measurements, preparing single qubits, or performing a few single-qubit gates. Then a flexible and hybrid MPVBQC framework for multi-type clients in quantum networks is given by combining the three proposed MPVBQC protocols. It simultaneously allows at least three types of clients in quantum networks to achieve BQC depending on their own quantum devices. Furthermore, all the proposed protocols can achieve identity authentication, resist both insider and outsider attacks, and be verifiable which means that the clients can verify the correctness of their computational results.
Abstract Blind quantum computation (BQC) allows clients with limited quantum capabilities to delegate computational tasks to remote quantum servers while keeping the privacy of their data. However, existing BQC protocols often fail to balance resource consumption and practical feasibility, which is particularly significant in the noisy intermediate-scale quantum era. In this paper, we propose a practical BQC model based on the parity quantum computing framework. It requires the server to perform operations only on adjacent qubits and eliminates the need for additional SWAP gates when two-qubit gates should be applied to non-adjacent qubits, greatly facilitating the physical implementation on real quantum devices. Furthermore, we prove that the proposed BQC model ensures the privacy of client’s information and satisfies the property of verifiability which enables clients to identify dishonest servers. Finally, a detailed example is given and simulated on the IBM’s quantum platform to demonstrate its feasibility.
Blind quantum computation is a cryptographic primitive that allows a limited-capability client to delegate its complex computation to a remote server without revealing its data and/or computation. This branch of quantum cryptography has been bifurcated into two distinct primitives, quantum homomorphic encryption (concerning the security of only data) and universal blind quantum computation (concerning the security of data and the computing algorithm). These primitives have immense applicability in problems like secure cloud computing, secure quantum variational algorithms, quantum federated learning, and secure multiparty computation. However, no software tools exist for the rapid prototyping of such protocols, hindering the academic interrogation for potential applications. In this paper, we describe the development of the first such library for transpiling circuits written in Qiskit to its blind counterpart, which can then be delegated in a client-server architecture without revealing the client's data and/or computation. The proposed library is designed in modular and reusable component layers, enabling easier scalability to newer BQC primitives and robustness against changes in underlying primitives. We show the implementation of these primitives to a blind variational quantum classifier for the IRIS dataset.
As quantum computing technology matures, an asymmetric computational landscape is emerging where powerful quantum data centers serve users with limited quantum capabilities. The Quantum Internet will enable users to access remote quantum resources, but this delegation raises privacy and trust concerns when the server cannot be assumed to be fully trusted. In this work, we study an implementation-oriented case study in which a Blind Quantum Computing (BQC) protocol is integrated with the encoding stage of a quanvolutional feature extractor for image classification. The goal is to evaluate how an existing BQC-compatible remote state preparation mechanism behaves when used inside a hybrid Quantum Machine Learning workflow. The client encodes classical pixel values as private rotation angles, while fresh random masks hide these angles from an honest-but-curious server at the protocol level. The quantum preprocessing is simulated with finite-shot computational-basis measurements in SquidASM/NetSquid, rather than ideal expectation values, and is evaluated on a small MNIST subset selected for computational feasibility. We analyze the resulting trade-offs among finite-shot sampling, communication and entanglement overhead, depolarizing link and device noise, seed sensitivity, and random-circuit complexity. The study provides practical evidence on the resource and performance constraints that arise when embedding BQC primitives into quantum machine-learning applications, and identifies limitations that must be addressed before deployment-oriented privacy-preserving QML can be considered practical.
Blind quantum computation (BQC) allows low-resource clients to securely delegate computations to a quantum server, but server resource costs scale with the computation size, posing a bottleneck for implementations. By leveraging Pauli-based computation (PBC), we achieve BQC with a server whose size depends only on the non-Clifford gate count. Our protocol inherits fault tolerance and qubit virtualization from PBC and reveals that classical-client BQC is possible even in the absence of classical simulability. Finally, we present an entanglement-based dual protocol that performs a resource state computation with a dramatically reduced execution cost.