Kernel Archives - IEEE Transactions on Quantum Engineering

DT-QFL: Dual-Timeline Quantum Federated Learning With Time-Symmetric Updates, Temporal Memory Kernels, and Reversed Gradient Dynamics

September 9, 2025Koffka Khan Khouler KhanQuantum Computing, UncategorizedAdaptation models, Computational modeling, Data models, Federated Learning, Kernel, Neural networks, Quantum computing, Robustness, Servers, Training

Abstract: Federated learning has emerged as a powerful paradigm for decentralized model training, ensuring privacy preservation by allowing clients to collaboratively learn a shared model without exchanging raw data. Quantum federated learning (QFL) extends this approach by leveraging quantum computing to enhance computational efficiency and security. However, existing QFL frameworks face challenges in handling temporal […]

Analysis of Parameterized Quantum Circuits: On the Connection Between Expressibility and Types of Quantum Gates

May 19, 2025Yu Liu; Kazuya Kaneko; Kentaro Baba; Jumpei Koyama; Koichi Kimura; Naoyuki TakedaQuantum Computing, UncategorizedAccuracy, Additives, Computational modeling, Integrated circuit modeling, Kernel, Logic gates, Predictive models, Quantum circuit, Qubit, Topology

Abstract: Expressibility is a crucial factor of a parameterized quantum circuit (PQC). In the context of variational-quantum-algorithm-based quantum machine learning (QML), a QML model composed of a highly expressible PQC and a sufficient number of qubits is theoretically capable of approximating any arbitrary continuous function. While much research has explored the relationship between expressibility and […]

Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks

February 13, 2025Diego Alvarez-EstevezQuantum SoftwareBenchmark testing, Estimation, Kernel, Machine learning, Quantum computing, Quantum mechanics, Quantum state, Support vector machines, Training, Vectors

Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafted toy problems, and given the current limitations of quantum hardware. For more about this […]

Quantum Kernels for Real-World Predictions Based on Electronic Health Records

November 2, 2022Zoran KrunicOpen Access, Quantum SoftwareKernel, Licenses, Machine learning, Measurement, Quantum computing, Support vector machines, Training

Research on near-term quantum machine learning has explored how classical machine learning algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely classical counterparts. Although theoretical work has shown a provable advantage on synthetic data sets, no work done to date has studied empirically whether the quantum advantage is attainable and with […]