Variational quantum algorithms use non-convex optimization methods to find the optimal
parameters for a parametrized quantum circuit in order to solve a computational problem …

Current quantum programs are mostly synthesized and compiled on the gate-level, where
quantum circuits are composed of quantum gates. The gate-level workflow, however …

The advantages of quantum pulses over quantum gates have attracted increasing attention
from researchers. Quantum pulses offer benefits such as flexibility, high fidelity, scalability …

Quantum machine learning (QML) based on Noisy Intermediate-Scale Quantum (NISQ)
devices requires the optimal utilization of limited quantum resources. The commonly used …

Variational quantum algorithms are tailored to perform within the constraints of current
quantum devices, yet they are limited by performance-degrading errors. In this study, we …

Optimization of quantum circuits for a given problem is very important in order to achieve
faster calculations as well as reduce errors due to noise. Optimization has to be achieved …

For a large number of tasks, quantum computing demonstrates the potential for exponential
acceleration over classical computing. In the NISQ era, variable-component subcircuits …

Pulse-based quantum machine learning (QML) models possess full expressivity when they
are ensemble controllable. However, it has also been shown that barren plateaus emerge in …

So far, the quantum computing ecosystem, defined by the quantum computing stack,
remains very similar to classical computing. However, it is not always beneficial to adopt this …