Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

Quantum computers can in principle solve certain problems exponentially more quickly than
their classical counterparts. We have not yet reached the advent of useful quantum …

Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …

Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …

Neural networks enjoy widespread success in both research and industry and, with the
advent of quantum technology, it is a crucial challenge to design quantum neural networks …

Many experimental proposals for noisy intermediate scale quantum devices involve training
a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum …

We introduce a quantum neural network, QNN, that can represent labeled data, classical or
quantum, and be trained by supervised learning. The quantum circuit consists of a sequence …

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) …

Quantum computing has rapidly advanced in recent years due to substantial development in
both hardware and algorithms. These advances are carrying quantum computers closer to …

Our world, of course, changed in many other ways as well since the publication of the first
edition. The global pandemic impacted all areas of society and will probably transform how …