We use octave-spanning, phase-shaped supercontinuum generation as an analog
computing element in a neural network. We can perform standard machine learning tasks …

Continuous-variable (CV) quantum computing has shown great potential for building neural
network models. These neural networks can have different levels of quantum-classical …

We present an erratum to our Letter [Opt. Lett. 47, 802 (2022) 10.1364/OL. 448571]. This
erratum corrects an error in the sign of one of the higher-order dispersion coefficient used in …

Photonic quantum computing is a leading approach toward universal quantum computation.
Here, we propose a realistic model for the implementation of neural networks on photonic …

We introduce a general method for building neural networks on quantum computers. The
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …

We provide a perspective on the fundamental relationship between physics and
computation, exploring the conditions under which a physical system can be harnessed for …

Artificial neural networks are computational network models inspired by signal processing in
the brain. These models have dramatically improved performance for many machine …

Neural networks have proven effective for solving many difficult computational problems, yet
implementing complex neural networks in software is computationally expensive. To explore …

Today's quantum processors composed of fifty or more qubits have allowed us to enter a
computational era where the output results are not easily simulatable on the world's biggest …

Different from the concept of universal computation, the universality of a quantum neural
network focuses on the ability to approximate arbitrary functions and is an important …