Quantum extreme learning machines (QELMs) have emerged as a promising framework for
quantum machine learning. Their appeal lies in the rich feature map induced by the …

Recent developments have led to the possibility of embedding machine learning tools into
experimental platforms to address key problems, including the characterization of the …

The expressive capacity of physical systems employed for learning is limited by the
unavoidable presence of noise in their extracted outputs. Though present in physical …

Quantum reservoir computing is an emerging field in machine learning with quantum
systems. While classical reservoir computing has proven to be a capable concept for …

Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to
efficiently process information encoded in input quantum states, avoiding the high …

We provide a new perspective on shadow tomography by demonstrating its deep
connections with the general theory of measurement frames. By showing that the formalism …

Abstract Machine learning techniques have achieved impressive results in recent years and
the possibility of harnessing the power of quantum physics opens new promising avenues to …

Quantum machine learning algorithms are expected to play a pivotal role in quantum
chemistry simulations in the immediate future. One such key application is the training of a …

Quantum Extreme Learning Machine (QELM) is an emerging technique that utilizes
quantum dynamics and an easy-training strategy to solve problems such as classification …

Quantum computing has brought a paradigm change in computer science, where non-
classical technologies have promised to outperform their classical counterpart. Such an …