Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

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

We review the state of the art and recent advances in quantum computing applied to
derivative pricing and the computation of risk estimators like Value at Risk. After a brief …

Principal component analysis (PCA) has been widely adopted to reduce the dimension of
data while preserving the information. The quantum version of PCA (qPCA) can be used to …

The vast amount of data generated by various applications necessitates the need for
advanced computing capabilities to process, analyze and extract insights from it. Quantum …

Dimensionality reduction (DR) techniques play an extremely critical role in the data mining
and pattern recognition field. However, most DR approaches involve large-scale matrix …

Quantum state tomography (QST) is a fundamental technique in quantum information
processing (QIP) for reconstructing unknown quantum states. However, the conventional …

The dynamic mode decomposition (DMD) algorithm is a widely used factorization and
dimensionality reduction technique in time series analysis. When analyzing high …

In this article, we propose a low-complexity quantum principal component analysis (qPCA)
algorithm. Similar to the state-of-the-art qPCA, it achieves dimension reduction by extracting …

Quantum Machine Learning has gained significant attention in recent years as a way to
leverage the relationship between quantum information and machine learning. Principal …