Near-term quantum computers are limited by the decoherence of qubits to only being able to
run low-depth quantum circuits with acceptable fidelity. This severely restricts what quantum …

Quantum machine learning (QML) has attracted significant research attention over the last
decade. Multiple models have been developed to demonstrate the practical applications of …

Numerical modeling of radio-frequency waves in plasma with sufficiently high spatial and
temporal resolution remains challenging even with modern computers. However, such …

Building efficient large-scale quantum computers is a significant challenge due to limited
qubit connectivities and noisy hardware operations. Transpilation is critical to ensure that …

Quantum neural networks have emerged as a novel machine intelligence (MI), showing
increasing achievements despite its dawn of literature. Although the structural design for …

We study the multi-qubit quantum gates intrinsic to a general array of semiconductor
quantum dots and investigate how they can be implemented in a scalable way. The intrinsic …

Quantum computers are here, and the search for applications and use of these allow us to
overcome the limits that today's hardware information processing gives us is constantly …

The principles of Machine Learning through the criteria of Mitchell are used to validate a
concrete quantum-mechanics interpretation from a classical radiation scheme inside the …