At a time when data are doubling every two years, the U.S. is projected to create over 40 billion gigabytes of data by 2025. To prepare for the influx, Kennesaw State University associate professor ...

A study has used the power of machine learning to overcome a key challenge affecting quantum devices. For the first time, the findings reveal a way to close the 'reality gap': the difference between ...

Instructor: Mahdi Hosseini Course Description: This course provides an introduction to Quantum Engineering, integrating foundational quantum theory with real-world applications in modern technology.

The quantum tangent kernel method is a mathematical approach used to understand how fast and how well quantum neural networks can learn. A quantum neural network is a machine learning model that runs ...

This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms. All methods ...

Finding high-performing candidates in the vast search space of bosonic qubit encodings represents a complex optimization task, which the researchers address with reinforcement learning, an advanced ...

Within the STRUCTURES Cluster of Excellence, two research teams at the Interdisciplinary Center for Scientific Computing (IWR) have refined a computing process, long held to be unreliable, such that ...

Imagine a future where quantum computers supercharge machine learning—training models in seconds, extracting insights from massive datasets and powering next-gen AI. That future might be closer than ...