Andrew Nemec, Andreas Klappenecker
Hybrid codes simultaneously encode both quantum and classical information, allowing for the transmission of both across a quantum channel. We construct a family of nonbinary error-detecting hybrid stabilizer codes that can detect one error while also encoding a single classical bit over the residue class rings $\mathbb{Z}_{q}$ inspired by constructions of nonbinary non-additive codes.
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Generic mesoscopic quantum systems that interact with their environment tend to display appreciable correlations with environment that often play an important role in the physical properties of the system. However, the experimental methods needed to characterize such systems either ignore the role of initial correlations or scale unfavourably with system dimensions. Here, we present a technique that is agnostic to system-environment correlations and can be potentially implemented experimentally. Under a specific set of constraints, we demonstrate the ability to detect and measure specific corr…
We derive a set of Bell-type inequalities for arbitrarily high-dimensional systems, based on the assumption of partial separability in the hybrid local-nonlocal hidden variable model. Partially entangled states would not violate the inequalities, and thus upon violation, these Bell-type inequalities are sufficient conditions to detect the full $N$-particle entanglement and validity of the hybrid local-nonlocal hidden variable description.
Superconductor-semiconductor hybrid devices are at the heart of several proposed approaches to quantum information processing, but their basic properties remain to be understood. We embed a two-dimensional Al-InAs hybrid system in a resonant microwave circuit, probing the breakdown of superconductivity due to an applied magnetic field. We find a strong fingerprint from the two-component nature of the hybrid system, and quantitatively compare with a theory that includes the contribution of intraband $p \pm i p$ pairing in the InAs, as well as the emergence of Bogoliubov-Fermi surfaces due to ma…
While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied. In this paper, we explore how a variational quantum circuit could be integrated into a classical neural network for the problem of detecting pneumonia from chest radiographs. We substitute one layer of a classical convolutional neural network with a variational quantum circuit to create a hybrid neural network. We train both networks on an image dataset containing chest radiographs and benchmark their performance. To mitigate the influence of differen…
Q-Detection: A Quantum-Classical Hybrid Poisoning Attack Detection Method
针对数据投毒攻击，提出量子-经典混合检测方法Q-Detection，引入量子加速优化Q-WAN，实验证明优于基线且可比肩最先进方法，理论上可获超20%加速。
The past two decades have witnessed a surge of interest in exploring correlation and coherence measures to investigate quantum phase transitions (QPTs). Here, motivated by the continued push along this direction, we propose a measure which is built upon the so-called degree of coherence, and advocate using the susceptibility of our measure to detect QPTs. We show that our measure can capture both the notions of coherence and correlations exhibited in bipartite states and therefore represents a hybrid of these two notions. Through examining the XXZ model and the Kitaev honeycomb model, we demon…
Enhanced quantum sensing with hybrid exceptional-diabolic singularities
该研究提出在四模玻色系统的 HED 奇点附近进行线性微扰检测，利用高阶奇异响应将估计误差标度提升两倍，并证明外差检测可达到量子 Fisher 信息给出的最优界限。
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with quantum kernels and applying them to satellite data analysis. The design and implementation of SVMs with quantum kernels (hybrid SVMs) are presented. Here, the pixels are mapped to the Hilbert space using a family of parameterized quantum feature maps (related to quantum kernels). The parameters are optimized to maximize the kernel target alignment. The quantum kernels have been selected such that they enabled analysi…
We describe a hybrid quantum system composed of a micrometer-size carbon nanotube (CNT) longitudinally coupled to a flux qubit. We demonstrate the usefulness of this device for generating high-fidelity nonclassical states of the CNT via dissipative quantum engineering. Sideband cooling of the CNT to its ground state and generating a squeezed ground state, as a mechanical analogue of the optical squeezed vacuum, are two additional examples of the dissipative quantum engineering studied here. Moreover, we show how to generate a long-lived macroscopically-distinct superposition (i.e., a Schröding…
A Cognitive-Aware QML-CRL Framework for Detecting Affinity and Romance-Investment Fraud
提出混合量子-经典框架，通过量子电路建模认知偏差并联合强化学习智能体逐轮决策，用于检测亲和与婚恋投资欺诈，在含硬负样本的合成对话上评估性能。
We present a complete optimization procedure for hybrid quantum-classical circuits with classical parity logic. While common optimization techniques for quantum algorithms focus on rewriting solely the pure quantum segments, there is interest in applying a global optimization process for applications such as quantum error correction and quantum assertions. This work, based on the pure-quantum circuit optimization procedure by Duncan et al., uses an extension of the formal graphical ZX-calculus called ZX-ground as an intermediary representation of the hybrid circuits to allow for granular optim…
Hybrid quantum transfer learning for crack image classification on NISQ hardware
针对NISQ时代量子图像处理尺寸受限问题，提出一种混合量子迁移学习方法用于灰度图裂纹检测，并比较了PennyLane标准量子比特与IBM模拟器及真实后端的性能与训练时间。
QuPCG: Quantum Convolutional Neural Network for Detecting Abnormal Patterns in PCG Signals
本文提出混合量子-经典卷积神经网络QuPCG，通过小波特征提取与自适应阈值压缩将PCG信号转为8像素图像仅需8量子比特，实现S3和杂音异常分类，测试准确率达93.33%，是QCNN在生物声学信号处理中的首次应用。
Hybrid Quantum-Classical Learning of Nonlinear Entanglement Witnesses via Continuous-Variable Quantum Neural Networks
该研究提出一种混合量子-经典框架，利用连续变量量子神经网络直接从量子数据学习非线性纠缠见证，在双模和三模上实现超99%分类准确率，并证明其理论近似能力，为数据驱动量子态表征提供新途径。
A Non-Monotonic Relationship: An Empirical Analysis of Hybrid Quantum Classifiers for Unseen Ransomware Detection
本文实证分析混合量子分类器检测未知勒索软件，发现信息瓶颈及非单调性能趋势，指出需协同发展数据压缩与量子优化策略。
Combining Error Detection and Mitigation: A Hybrid Protocol for Near-Term Quantum Simulation
针对近期量子硬件难以实现实用量子纠错的问题，提出整合Pauli twirling、概率错误抵消与$[[n, n-2, 2]]$量子检测码的混合错误抑制协议，通过优化Pauli算符数量等降低开销，并在H₂基态能量估算电路上经仿真和真实量子处理器验证。
Detecting Phishing in Ethereum Networks using Quantum Machine Learning
研究利用量子机器学习检测以太坊网络中的网络钓鱼，提出混合量子 - 经典集成框架，对比QSVM、VQC与统计方法、深度学习技术的效果，用QRAC编码数据提升模型性能，发现部分QML算法在量子处理器上有良好表现。
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