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arXiv:1507.07195

Blind quantum machine learning

Yu‐Bo Sheng, Lan Zhou

Published Jul 26, 2015• 0 citations
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Abstract

Blind quantum machine learning (BQML) enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server in such a approach that the privacy data is preserved. Here we propose the first BQML protocol that the client can classify two-dimensional vectors to different clusters, resorting to a remote small-scale photon quantum computation processor. During the protocol, the client is only required to rotate and measure the single qubit. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for quantum machine learning.

DOI
10.48550/arxiv.1507.07195

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Blind quantum machine learning (BQML) enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server in such a approach that the privacy data is preserved. Here we propose the first BQML protocol that the client can classify two-dimensional vectors to different clusters, resorting to a remote small-scale photon quantum computation processor. During the protocol, the client is only required to rotate and measure the single qubit. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for quantum machine learning.
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) protocol allows a client having partial quantum ability to delegate his quantum computation to a remote quantum server without leaking any information about the input, the output and the intended computation to the server. Several BQC protocols have been proposed, e.g., Li et al. in [1] proposed a triple-server BQC protocol and Xu et al. in [2] proposed a single-server BQC protocol. Though both papers claimed that their protocols can satisfy the requirement of privacy, this paper points out a security loophole in their protocols. With that the server can reveal the private information of the client.
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.
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 computing is a new secure quantum computing protocol where a client who does not have any sophisticated quantum technology can delegate her quantum computing to a server without leaking any privacy. It is known that a client who has only a measurement device can perform blind quantum computing [T. Morimae and K. Fujii, Phys. Rev. A 87, 050301(R) (2013)]. It has been an open problem whether the protocol can enjoy the verification, i.e., the ability of the client to check the correctness of the computing. In this paper, we propose a protocol of verification for the measurement-only blind quantum computing.
Current cloud-based quantum processors offer access to advanced hardware hosted on a remote server, but do not guarantee data or algorithm privacy. Blind quantum computation provides information-theoretic privacy by enabling a client to execute an algorithm without disclosing information about either the task or the final result. Here, we execute a measurement-based blind quantum computation protocol on a superconducting processor comprising two flip-chip-bonded modules, one acting as a server and the other as a client. The server generates a two-dimensional cluster state and forwards it to the client. Using this resource, the client implements a universal gate set with only adaptive single-qubit rotations and measurements. To illustrate this approach, we execute a three-qubit instance of the Deutsch-Jozsa algorithm. We analyze the server's quantum state after each rotation of a measurement-based single-qubit gate to verify that negligible information about the computation is revealed to the server, consistent with the one-way flow of information that guarantees blindness. This proof-of-principle demonstration establishes key elements of blind quantum computation in superconducting-circuit architectures, indicating that intermediate-scale implementations of blind protocols may become feasible with realistic near-term improvements in gate fidelities.