The identification of user segments based on their energy consumption characteristics is useful for a wide range of modern electrical engineering applications, from microgrid design to energy market forecasts. Numerous machine learning models have been applied to this problem, with varying levels of success. In this paper, a state-of-the-art quantum machine learning model known as variational quantum classifier, is applied as a proof of concept to tackle this issue. A dataset consisting of $\mathbf{1. 1}$ million measurements from Italian users was adapted for being use, the performance obtained was not on par with classical machine learning model but it shows promising characteristics.
The identification of user segments based on their energy consumption characteristics is useful for a wide range of modern electrical engineering applications, from microgrid design to energy market forecasts. Numerous machine learning models have been applied to this problem, with varying levels of success. In this paper, a state-of-the-art quantum machine learning model known as variational quantum classifier, is applied as a proof of concept to tackle this issue. A dataset consisting of 1.1 million measurements from Italian users was adapted for being use, the performance obtained was not on par with classical machine learning model but it shows promising characteristics.
In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these classifiers when using a hyperparameter search on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, this paper introduces a novel dataset based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results. Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms, such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers such as XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes. Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field. To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository.
The stability of a smart grid architecture is one of the most important parameters to assess its effectiveness and reliability. As a result of increased industrialization and use of renewable energy, predicting stability with respect to different scenarios becomes increasingly important to mitigate potential instabilities. Therefore, employing machine learning approaches to forecast system stability is crucial for sustainability. Traditionally, classical computers have been employed for machine learning-based methods; however, the proliferation of producers, consumers, and prosumers in newly built smart grid structures generates massive volumes of data, posing significant challenges for traditional computational methods in processing and analyzing these data efficiently. Quantum computing offers a promising solution for handling large amounts of data generated by smart grids, potentially leading to more accurate and efficient stability predictions. This study investigates a benchmark-based approach by comparing classical machine learning with a quantum machine learning technique to predict the stability of the smart grid. We utilized the "Electrical Grid Stability Simulated Data" dataset, focusing on a 4-node architecture smart grid. We approached this task as a classification problem, comparing the performance of a classical Support Vector Machine (SVM) model with a quantum machine learning approach, namely the Variational Quantum Classifier (VQC). Both methods were tested and evaluated on the basis of their ability to classify the grid’s stability into "stable" and "unstable" categories. Our experimental results demonstrate the potential advantages and limitations of quantum computing in improving smart grid stability forecasting, and energy research in general.
Variational Quantum Classifiers (VQCs) stand out as one of the most popular classification approaches within the Quantum Machine Learning (QML) community. They serve as an accessible entry point for newcomers to QML world, particularly those with a background in classical machine learning, due to their workflow similarity to conventional neural networks. In essence, VQCs boast a layered structure conducive to training via a classical optimizer. Recently, Evolutionary Algorithms (EAs) have emerged as a suitable methodology for this training goal. However, current quantum tools provide implementations for various gradient-based optimizers and just one EA tailored for VQC training. Moreover, it is important to note that access to this toolset remains largely confined to expert users. This highlights the necessity for platforms that facilitate the utilization of EAs for VQC training by non-experts in quantum or evolutionary computing. This work introduces a web application designed to implement quantum classifiers to be trained on user-supplied datasets by means of a set of EAs, which can be customized by users. The capabilities of the proposed web application are shown in the experimental session, where a VQC is trained to classify a well-known dataset using a variety of EAs.
Machine learning is the process of discovering patterns in data and using those patterns to forecast or predict future occurrences of those patterns. The machine learning process consists of three steps: input data management, model construction, and model evaluation. Combining machine learning and quantum computing allows for the design and operation of quantum circuits with behavior resembling that of classical machine learning. Variable quantum circuits are frequently used in quantum machine learning. Parameterized variables, a crucial part of variable quantum circuits, must be adjusted in order to achieve good performance of these circuits. It is possible to modify parameterized variables in a variety of ways, such as by using gradient methods or linear programming to determine the circuit’s optimal point. This study proposes a method for automatically designing a variational quantum circuit to classify the different datasets, including the well-known 1-dimensional and 2-dimensional datasets, iris and MNIST, using a genetic algorithm to find the best values for the parameterized variables. The Qiskit quantum simulation tools are employed in these experiments. The results show that genetic algorithms can find solutions with greater accuracy than linear-based optimizers by 94 percent and 37.5 percent, respectively, for the iris dataset and the MNIST dataset.
Dementia is the fifth cause of death worldwide with 10 million new cases every year. Healthcare applications using machine learning techniques have almost reached the physical limits while more data is becoming available resulting from the increasing rate of diagnosis. Recent research in Quantum Machine Learning (QML) techniques have found different approaches that may be useful to accelerate the training process of existing machine learning models and provide an alternative to learn more complex patterns. This work aims to report a real-world application of a Quantum Machine Learning Algorithm, in particular, we found that using the implemented version for Variational Quantum Classiffication (VQC) in IBM's framework Qiskit allows predicting dementia in elderly patients, this approach proves to provide more consistent results when compared with a classical Support Vector Machine (SVM) with a linear kernel using different number of features.
Electricity theft could result in massive economic loss and cause potential security problems to the grids. There-fore, it is of great importance to detect electricity theft from both an economic and social perspective. With the help of Advanced Metering Infrastructure (AMI) in smart grids, now it is possible to detect theft activities through analyzing the recorded electricity consuming data using advanced techniques, such as machine learning. However, training a learning model, especially for the ones with a huge amount of parameters, is always resource-consuming, and users could encounter performance bottlenecks due to hardware limitations. To improve the problem, using quantum computing for machine learning tasks has shown to be an effective approach to speed up the learning process. However, the relevant techniques have not been studied in the domain of energy yet. As a pioneering exploration, in this work, we are trying to leverage a quantum classification algorithm to classify the electricity dataset and on that basis to perform a case study on theft activity detection. Specifically, we have given the details of our design in the study, and our experimental results show that the prediction accuracy can be significantly improved with increasing training epochs, demonstrating the feasibility of quantum machine learning for energy data analysis.