differentially private quantum sensing

This work addresses the vulnerability of quantum sensing networks to privacy attacks when handling sensitive data, a challenge exacerbated by the difficulty of existing entanglement-based protocols in simultaneously achieving high precision and strong privacy guarantees. The study introduces differential privacy into quantum sensing for the first time, proposing a novel protocol that injects noise directly into the sensing Hamiltonian. Under the assumption of honest nodes, this approach achieves $(\varepsilon, \delta)$-differential privacy with arbitrarily small $\delta$, while preserving Heisenberg-limited mean squared error scaling ($O(1/n^2)$). By integrating entangled sensing, distributed randomness, and locally implementable mechanisms, the protocol effectively resists both classical and quantum adversaries and demonstrates a superior privacy–utility trade-off compared to classical schemes, thereby establishing a clear quantum advantage.

Existing quantum differential privacy (QDP) frameworks fail to jointly model multiple noise sources—specifically quantum channel noise and measurement noise—thereby compromising privacy guarantees under realistic hardware conditions. Method: We propose the Hybrid Quantum Differential Privacy (Hybrid QDP) framework and the novel Lifted QDP paradigm, enabling the first unified modeling of heterogeneous noise sources and joint optimization of the privacy budget. Our approach integrates quantum channel theory, classical differential privacy principles, and noise-aware measurement analysis to construct a noise-adaptive privacy–utility trade-off mechanism. Contribution/Results: First, we overcome the limitation of single-noise-source assumptions, substantially enhancing the randomness and robustness of privacy auditing. Second, under real-device noise conditions, we achieve Pareto improvements in both privacy protection strength and algorithmic practicality. Third, our framework increases the trustworthiness of privacy assessments for quantum machine learning algorithms and improves their compatibility with near-term quantum hardware deployments.

This work addresses the inherent tension between estimation precision and parameter privacy in distributed quantum sensing, where achieving Heisenberg-limited accuracy along a target direction may inadvertently leak information about orthogonal, irrelevant parameters. The authors establish a dual inequality for the quantum Fisher information in orthogonal directions, revealing a fundamental trade-off: for any $N$-qubit probe state, $F_Q(\mathbf{w}^\top\boldsymbol{\theta}) + F_Q(\mathbf{v}^\top\boldsymbol{\theta}) \leq N$, with equality attained by the GHZ state for $N \geq 2$. This result demonstrates that Heisenberg-limited sensitivity in a desired direction can be achieved while completely suppressing information leakage in any orthogonal direction, thereby enabling intrinsically private sensing. The analysis leverages many-body quantum state characterization, a local phase encoding model, and information-theoretic bounds under directional orthogonality constraints.

This work addresses the lack of effective composition theorems in quantum differential privacy (QDP), which hinders end-to-end privacy guarantees. For the first time, it explicitly identifies structural conditions under which QDP operates with tensor-product channels and product-adjacent inputs. Under this setting, the paper introduces an operator-valued privacy loss and a quantum moment accounting framework that leverages matrix moment-generating functions and measured Rényi divergence to derive provable composition bounds. The resulting analysis recovers advanced composition bounds whose leading-order terms match those of classical differential privacy, thereby establishing a rigorous theoretical foundation for privacy analysis of quantum algorithms.

Quantum machine learning (QML) models suffer from vulnerability to adversarial attacks and lack formal guarantees of privacy and robustness. Method: This paper establishes, for the first time, a theoretical connection between quantum noise channels and differential privacy, proposing an (α,γ)-parameterized quantum noise channel construction framework grounded in ε-differential privacy. We design a semidefinite programming (SDP)-based optimizer to enhance certified robustness against depolarizing noise, and systematically quantify the impact of α and γ on robustness via quantum state encoding analysis, revealing the critical role of encoding strategies. Contribution/Results: Experiments on small-scale QML models demonstrate significant improvements in adversarial accuracy. The framework provides a novel paradigm for QML that simultaneously ensures provable robustness and rigorous privacy protection, advancing the foundation for trustworthy quantum learning systems.

This work addresses the problem of answering counting queries over quantum-encoded datasets under differential privacy. By reformulating such queries as amplitude estimation tasks distinguishing between two orthogonal quantum states, the authors construct the first differentially private quantum protocol for counting queries, combining repeated computational-basis measurements with classical amplitude estimation algorithms. The key contributions include demonstrating that quantum randomness, amplified through repeated measurements, inherently enhances privacy guarantees; deriving a tight bound on global sensitivity tailored to counting queries; and designing an efficient protocol amenable to blind execution on a quantum server. Compared to generic query mechanisms, the proposed approach achieves a significantly improved trade-off between privacy and utility.

This work addresses the challenge of balancing privacy preservation and data utility in quantum computing by proposing a geometry-aware differential privacy framework grounded in the spectral structure of quantum Fisher information (QFI). By replacing conventional isotropic noise with direction-dependent perturbations, the method enables optimized allocation of the privacy budget. It introduces a QFI-aligned optimal noise mechanism that elucidates the impact of decoherence basis selection on privacy and establishes a privacy–utility uncertainty relation. Integrating adaptive QFI estimation, subspace projection, and zero-knowledge auditing, the approach is validated on IBM Quantum hardware and Qiskit Aer GPU simulations, achieving a privacy parameter ε ≈ 0.001 at equivalent utility—significantly outperforming classical differential privacy methods (ε ≈ 4800)—and demonstrating, for the first time, the privacy-amplifying potential of intrinsic hardware noise.

This study addresses the high-error bottleneck of classical two-party differential privacy protocols under information-theoretic security. We construct quantum communication protocols that leverage non-orthogonal message protection mechanisms to estimate Hamming distance under pure and approximate quantum differential privacy. Through protected coherent round-trip transmission, the isometric Gram rigidity principle, and hockey-stick divergence analysis, we demonstrate for the first time that quantum communication achieves O(1) error under information-theoretic security, matching the precision of classically computationally secure protocols while establishing message retention as a privacy resource. Our approach attains constant expected error with O(n) communication complexity, rigorously separating the security boundaries across distinct privacy models and significantly outperforming classical methods.

This study investigates the fundamental precision limits of distributed quantum sensor fusion under the joint influence of Byzantine faults and quantum decoherence. It proposes a unified framework for the mean squared error lower bound, jointly modeling entanglement visibility and fault fraction for the first time, and derives a performance boundary that continuously interpolates between the standard quantum limit and the Heisenberg limit—thereby bridging quantum metrology with classical stream processing architectures. The approach integrates Brooks–Iyengar interval overlap functions, SPOTLESS spatiotemporal validation, and Data-Cleaning Trees, enhanced by 80–20 power-law clustering to improve robustness. Monte Carlo simulations confirm the theoretical scaling laws, and experiments on the Intel Berkeley dataset demonstrate 20–27 dB signal-to-noise ratio gains per cluster, revealing an equivalence between classical data loss and quantum decoherence in degrading fusion consistency.

研究利用氮空位（NV）传感和量子计算传感（QCS）解决电网隐蔽攻击检测问题，通过物理状态一致性分析和测量效率优势提高检测准确性。