Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to
solve combinatorial optimization problems. In recent years, advances in quantum …

With quantum computing technologies nearing the era of commercialization and quantum
supremacy, machine learning (ML) appears as one of the promising'killer'applications …

Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for
solving binary optimization problems. Hardware implementations of quantum annealing …

Variational autoencoders (VAEs) are powerful generative models with the salient ability to
perform inference. Here, we introduce a quantum variational autoencoder (QVAE): a VAE …

Mainstream machine-learning techniques such as deep learning and probabilistic
programming rely heavily on sampling from generally intractable probability distributions …

Quantum machine learning promises to revolutionize traditional machine learning by
efficiently addressing hard tasks for classical computation. While claims of quantum speed …

We present a generalization of the binary paint shop problem (BPSP) to tackle an
automotive industry application, the multi-car paint shop (MCPS) problem. The objective of …

Quantum annealers are designed to utilize quantum tunneling to find good solutions to hard
optimization problems. When constructing a family of synthetic inputs to test the potential of a …

We investigate whether quantum annealers with select chip layouts can outperform classical
computers in reinforcement learning tasks. We associate a transverse field Ising spin …

Current quantum annealing (QA) hardware suffers from practical limitations such as finite
temperature, sparse connectivity, small qubit numbers, and control error. We propose new …