Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

The rapid development of quantum computer hardware has laid the hardware foundation for
the realization of QNN. Due to quantum properties, QNN shows higher storage capacity and …

Inference is the task of drawing conclusions about unobserved variables given observations
of related variables. Applications range from identifying diseases from symptoms to …

Preparing thermal states on a quantum computer can have a variety of applications, from
simulating many-body quantum systems to training machine learning models. Variational …

We introduce a method to perform imaginary time evolution in a controllable quantum
system using measurements and conditional unitary operations. By performing a sequence …

Generative models have the capacity to model and generate new examples from a dataset
and have an increasingly diverse set of applications driven by commercial and academic …

Universal quantum algorithms (UQA) implemented on fault-tolerant quantum computers are
expected to achieve an exponential speedup over classical counterparts. However, the …

The road to computing on quantum devices has been accelerated by the promises that
come from using Shor's algorithm to reduce the complexity of prime factorization. However …

The variational quantum eigensolver (VQE) is a promising algorithm to demonstrate
quantum advantage on near‐term noisy‐intermediate‐scale quantum (NISQ) computers …

Optimized power system planning and operation are core to delivering a low-cost and high-
reliability transition path to net-zero carbon emissions. The major technological changes …