Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizin
Despite the mounting anticipation for the quantum revolution, the success of quantum machine learning (QML) in the noisy intermediate-scale quantum (NISQ)
Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum reinforcement learning has garnered attention, often realized using variational quantum circuits ...
Quantum computing for realistic problems Quantum machine learning Applying machine learning in quantum computing Enhancing machine learning leveraging quantum computing Quantum experiment Efficient benchmarking and calibration of quantum hardware Expe...
The more complex we make the model f, the lower the bias is, but in exchange, the estimation error increases. This tradeoff is analyzed in Section 2.5. Show moreView chapter Book 2014, Quantum Machine LearningPeter Wittek Chapter Supervised Learning and Support Vector Machines 7.6 Generalization ...
This quantum advantage in the decision-making process of the quantum PS agent was recently experimentally demonstrated using a small-scale quantum information processor based on trapped ions43. In the PS model, learning is realized by internal modification of the clip network, both in terms of its...
Each of the individual interactions is context-independently law-like, determined by the quantum mechanics of electron exchange interactions. But each such reaction occurs in the spatially organised context of all the others and only in that complex empirical constraint context do they constitute ...
quantum interacting level [77]. In such circumstances, the reason for quantum RG running of couplings is absent, namely there are also no perturbative UV divergences and the theory is completely UV-finite. In general, it is quite difficult to find models of such finite interacting theories when...
Symbolic Regression (SR) on high-dimensional datasets often encounters significant challenges, resulting in models with poor generalization capabilities. W
Label smoothing can aim to improve generalization in machine learning by avoiding over-confidence over labels. Although the working mechanism of label smoothing continues to be a topic of research, it has been shown to be helpful in a broad variety of machine learning tasks. Label smoothing can ...