Abstract: Quantum neural networks (QNNs) are gaining attention as versatile models for quantum machine learning, but training them effectively remains a challenge. Most existing approaches, such as quantum multilayer perceptrons, use fidelity-based cost functions. While well-suited for pure states, these measures are less reliable when inputs and outputs are mixed states—a situation common in learning […]
Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafted toy problems, and given the current limitations of quantum hardware. For more about this […]
The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantages are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This […]
The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantages are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This […]
The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantages are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This […]
Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate-scale quantum computers. A fundamental challenge in quantum machine learning is generalization, as the designer targets performance under testing conditions while having access only to limited training data. Existing generalization analyses, while identifying important general […]
Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate-scale quantum computers. A fundamental challenge in quantum machine learning is generalization, as the designer targets performance under testing conditions while having access only to limited training data. Existing generalization analyses, while identifying important general […]
Research on near-term quantum machine learning has explored how classical machine learning algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely classical counterparts. Although theoretical work has shown a provable advantage on synthetic data sets, no work done to date has studied empirically whether the quantum advantage is attainable and with […]
In this article, we propose a machine-learning framework for parameter estimation of single-mode Gaussian quantum states. Under a Bayesian framework, our approach estimates parameters of suitable prior distributions from measured data. For phase-space displacement and squeezing parameter estimation, this is achieved by introducing expectation–maximization (EM)-based algorithms, while for phase parameter estimation, an empirical Bayes method […]
In this article, we propose a machine-learning framework for parameter estimation of single-mode Gaussian quantum states. Under a Bayesian framework, our approach estimates parameters of suitable prior distributions from measured data. For phase-space displacement and squeezing parameter estimation, this is achieved by introducing expectation–maximization (EM)-based algorithms, while for phase parameter estimation, an empirical Bayes method […]