QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting
Forecasting in probabilistic time series is a complex endeavor that extends beyond predicting future values to also quantifying the uncertainty inherent in these predictions. Gaussian process regression stands out as a Bayesian machine learning technique adept at addressing this multifaceted challenge. This paper introduces a novel approach that blends the robustness of this Bayesian technique with the nuanced insights provided by the kernel perspective on quantum models, aimed at advancing quantum kernelized probabilistic forecasting. We incorporate a quantum feature map inspired by Ising interactions and demonstrate its effectiveness in capturing the temporal dependencies critical for precise forecasting. The optimization of our model's hyperparameters circumvents the need for computationally intensive gradient descent by employing gradient-free Bayesian optimization. Comparative benchmarks against established classical kernel models are provided, affirming that our quantum-enhanced approach achieves competitive performance.
Code (1)
Tasks
Bayesian OptimizationTime SeriesTime Series ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
QUACK: Quantum Aligned Centroid Kernel
Quantum computing (QC) seems to show potential for application in machine learning (ML). In particular quantum kernel methods (QKM) exhibit promising properties for use in supervised ML tasks. However, a major disadvanta…
Dimensionality ReductionQuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
Quantum optimization, a key application of quantum computing, has traditionally been stymied by the linearly increasing complexity of gradient calculations with an increasing number of parameters. This work bridges the g…
Operator learningQuantum Machine LearningQuantum-classical hybrid models based on error correction for time series forecasting
Time series forecasting largely benefits from combining the strengths of different models, especially using a scheme where a model corrects another model by capturing supplementary patterns from forecasting errors. Concu…
Time Series ForecastingA Multiclass Quantum Aligned Centroid Kernel
Kernel methods are powerful tools in machine learning but commonly used full-Gram kernels face three key limitations: (1) quadratic scaling with training set size; (2) the use of fixed, non-trainable kernels; and (3) the…
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
The Mahalanobis distance is a classical tool used to measure the covariance-adjusted distance between points in $\bbR^d$. In this work, we extend the concept of Mahalanobis distance to separable Banach spaces by reinterp…
Anomaly DetectionNovelty DetectionSemi-supervised Anomaly DetectionSupervised Anomaly Detection+2