Combining the advantages of ultrahigh photon emission rates achievable in the gap surface plasmon polaritons with high extraction decay rates into low-loss nanofibers, we demonstrate theoretically the efficient photon emission of a single dipole emitter and one-dimensional nanoscale guiding in metallic nanorod-coupled nanofilm structures coupled to dielectric nanofibers. We find that total decay rates and surface plasmon polariton channel decay rates orders of magnitude larger than those characteristic of metallic nanofilms alone can be achieved in ultrastrong hot spots of gap plasmons. For the requirement of practical applications, propagating single photons with decay rates of 290γ_{0}-770γ_{0} are guided into the phase-matched low-loss nanofibers. The proposed mechanism promises to have an important impact on metal-based optical cavities, on-chip bright single photon sources and plasmon-based nanolasers.

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Support vector machine(SVM) is one of the most popular machine learning(ML) methods which are widely used as the methodology of choice in Breast Cancer detection because of its unique advantages in critical features detection from complex breast cancer datasets. Quantum support vector machine(QSVM) uses the power of quantum mechanics to improve the performance of classical support vector machine(SVM) algorithms with theoretical acceleration advantage. However, it still suffers from the wide error problem and hardware limits in Noisy Intermediate-Scale Quantum computing(NISQ). Consequently, we propose a quantum kernel estimation method with measurement error mitigation and test it using the Wisconsin Breast Cancer database first on the IBM quantum processors. The experimental results show that we can achieve remarkable performance improvement in accuracy for solving such binary classification problems compared to state-of-the-art models, which shows the great potential for the design and implementation of machine learning algorithms with quantum advantages in the future.

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Graph computation is a core technique for solving realistic problems of graph representations. In solving the shortest path problem (SPP), the current classical methods are encountering a huge performance bottleneck. Attempting to solve this dilemma, we try to solve the SPP with a Quantum Approximate Optimal Algorithm (QAOA)-based quantum method. In this paper, we propose a QAOA-based shortest path algorithm (SPA) by constructing a suitable Hamiltonian quantity and using the idea of variational quantum computing, and verify the algorithm using a quantum simulator and an International Business Machines cloud quantum computer. The proposed algorithm is able to achieve a near-optimal solution with a correct rate that significantly exceeds the invalid solutions, reaching a good preliminary result. Furthermore, the proposed algorithm is expected to achieve a huge advantage over the classical algorithm and the SPA based on Grover’s algorithm with a suitable selection of parameters and number of steps. In addition, the proposed algorithm requires fewer quantum bits than other quantum algorithms, thus promising quantum computing superiority on current noisy intermediate-scale quantum (NISQ) quantum computing devices.