Despite its undeniable success, classical machine learning remains a resource-intensive
process. Practical computational efforts for training state-of-the-art models can now only be …

Quantum computing is a game-changing technology for global academia, research centers
and industries including computational science, mathematics, finance, pharmaceutical …

Quantum machine learning (QML) has been identified as one of the key fields that could
reap advantages from near-term quantum devices, next to optimization and quantum …

Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …

We propose a generalization of the Wasserstein distance of order 1 to the quantum states of
n qudits. The proposal recovers the Hamming distance for the vectors of the canonical basis …

Quantum computers are expected to have substantial impact on the finance industry, as they
will be able to solve certain problems considerably faster than the best known classical …

Quantum neural network (QNN), or equivalently, the parameterized quantum circuit (PQC)
with a gradient-based classical optimizer, has been broadly applied to many experimental …

Machine Learning (ML) aims at designing models that learn from previous experience,
without being explicitly formulated. Applications of machine learning are inexhaustible …

Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of
machine learning approaches in adversarial settings and developing techniques …

Modeling joint probability distributions is an important task in a wide variety of fields. One
popular technique for this employs a family of multivariate distributions with uniform …