Quantum computing is a fascinating interdisciplinary research field that promises to
revolutionize computing by efficiently solving previously intractable problems. Recent years …

Adversarial robustness and generalization are both crucial properties of reliable machine
learning models. In this paper, we study these properties in the context of quantum machine …

In recent years, variational quantum algorithms (VQAs) have gained significant attention due
to their adaptability and efficiency on near-term quantum hardware. They have shown …

Quantum coherence plays a central role in Grover's search algorithm. We study the Tsallis
relative $\alpha $ entropy of coherence dynamics of the evolved state in Grover's search …

State convertibility is fundamental in the study of resource theory of quantum coherence. It is
aimed at identifying when it is possible to convert a given coherent state to another using …

Noise in quantum computing devices poses a key challenge in their realization. In this
paper, we study the robustness of optimal quantum annealing (QA) protocols against …

Characterization of quantum devices generates insights into their sources of disturbances.
State-of-the-art characterization protocols often focus on incoherent noise and eliminate …

A leading approach to algorithm design aims to minimize the number of operations in an
algorithm's compilation. One intuitively expects that reducing the number of operations may …

Influence of the commutator properties of Hamiltonians on the robustness of quantum circuits -
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Quantum machine learning (QML) continues to be an area of tremendous interest from
research and industry. While QML models have been shown to be vulnerable to adversarial …