One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

Quantum computing technologies are making steady progress. This has opened new
opportunities for tackling problems whose complexity prevents their description on classical …

The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …

Electric power systems provide the backbone of modern industrial societies. Enabling
scalable grid analytics is the keystone to successfully operating large transmission and …

Quantum machine learning models have shown successful generalization performance
even when trained with few data. In this work, through systematic randomization …

Ground state preparation is classically intractable for general Hamiltonians. On quantum
devices, shallow parametrized circuits can be effectively trained to obtain short-range …

Adaptive quantum variational algorithms are particularly promising for simulating strongly
correlated systems on near-term quantum hardware, but they are not yet viable due, in large …

Quantum computing is gaining popularity across a wide range of scientific disciplines due to
its potential to solve long-standing computational problems that are considered intractable …

Variational quantum algorithms are a promising class of algorithms that can be performed
on currently available quantum computers. In most settings, the free parameters of a …

Much is understood about 1-dimensional spin chains in terms of entanglement properties,
physical phases, and integrability. However, the Lie algebraic properties of the Hamiltonians …