Parametrized Energy-Efficient Quantum Kernels for Network Service Fault Diagnosis

Abstract: In quantum kernel learning, the primary method involves using a quantum computer to calculate the inner product between feature vectors, thereby obtaining a Gram matrix used as a kernel in machine learning models such as support vector machines (SVMs). However, a method for consistently achieving high performance has not been established. In this study, we investigate the diagnostic accuracy using a commercial dataset of a network service fault diagnosis system used by telecommunications carriers, focusing on quantum kernel learning, and propose a method to stably achieve high performance.We show significant performance improvements and an efficient achievement of high performance over conventional methods can be attained by applying quantum entanglement in the portion of the general quantum circuit used to create the quantum kernel, through input data parameter mapping and parameter tuning related to relative phase angles. Furthermore, experimental validation of the quantum kernel was conducted using IBM' s superconducting quantum computer IBM-Kawasaki, and its practicality was verified while applying the error suppression feature of Q-CTRL' s Fire Opal.

• The paper introduces a novel quantum kernel learning methodology that uses parametrized entanglement to improve network fault diagnosis.
• It employs optimized phase rotations in Z-gates to map high-dimensional data onto qubits, demonstrating up to 96% accuracy with 20 qubits.
• Hardware tests on IBM quantum systems with error mitigation validate its potential for robust performance in complex telecom environments.

Overview of Parametrized Energy-Efficient Quantum Kernels for Network Service Fault Diagnosis

The paper presents a novel algorithmic approach for employing quantum kernel learning within the field of network service fault diagnosis. The primary objective outlined in the study is to harness quantum kernel learning on quantum computers to construct efficient inference models using Quantum Support Vector Machines (QSVM). The authors introduce "Parametrized Energy-Efficient Quantum Kernels" designed to stabilize and enhance the performance of diagnosing network service faults, a task increasingly crucial given the complexity introduced by advancements in telecommunications infrastructure, such as 5G networks.

Methods

The authors develop an innovative quantum machine learning algorithm that parametrically enhances the extraction of feature vectors by significantly optimizing the entanglement generation process in quantum circuits. This enhancement is achieved by tuning phase rotation parameters in Z-gates during the quantum entanglement process. The approach leverages the super-spatial-temporal dimensions in Hilbert space, aiming to surpass classical computing methods. By strategically mapping input data onto qubits, the proposed model enables efficient explorations of high-dimensional feature spaces, emphasizing unitary operations crucial to quantum coherence.

Experimentation involves extensive comparisons between the newly proposed method, classical algorithms utilizing Radial Basis Function (RBF) kernels, and existing QSVMs, performed within simulated and real hardware environments. Numerical simulations and hardware trials were conducted using IBM's superconducting quantum computer, demonstrating noticeable progress in accuracy, particularly when the algorithm is enhanced with Q-CTRL’s Fire Opal, an error suppression technology.

Results and Implications

Findings exhibit a notable advancement in classification accuracy when the Parametrized Energy-Efficient Quantum Kernels were employed with optimized α-parameter settings, achieving up to 96% accuracy with a 20-qubit configuration in simulations. The approach displayed a clear advancement over existing QSVMs, which is significant for practical applications requiring high diagnostic accuracy and robustness.

The tests conducted on IBM hardware confirm the viability of this approach in a noisy quantum environment, especially when coupled with modern error mitigation techniques. Hardware experiments indicated performance improvements attributable to enhanced error suppression, aligning results closer to theoretical predictions.

Future Prospects

The demonstrated ability of quantum kernels not only to achieve but also potentially exceed classical algorithm performance in diagnostic tasks suggests profound implications for the future of network operations and broader applications. The study indicates that with ongoing improvements in quantum hardware and error correction methods, there is immense potential to expand the utility of quantum machine learning in various real-world industrial applications.

Additionally, the study raises interesting questions about the representational capacity of quantum states as the number of qubits increases and the relationship between parameter tuning and data complexity. Future research could further explore the scalability of quantum machine learning models, optimize parameter settings for various applications, and elucidate the conditions under which quantum superiority over classical algorithms becomes more pronounced.

Overall, this research contributes to a growing body of evidence suggesting that quantum computing can play an instrumental role in advancing machine learning applications, particularly in scenarios requiring comprehensive data processing capabilities amid complex, high-dimensional spaces.