[HTML][HTML] The variational quantum eigensolver: a review of methods and best practices
The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …
received significant attention from the research community in recent years. It uses the …
Robust data encodings for quantum classifiers
Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …
quantum machine learning with near-term quantum computers, equally important …
Diverse solitary and Jacobian solutions in a continually laminated fluid with respect to shear flows through the Ostrovsky equation
MMA Khater - Modern Physics Letters B, 2021 - World Scientific
In this paper, the generalized Jacobi elliptical functional (JEF) and modified Khater (MK)
methods are employed to find the soliton, breather, kink, periodic kink, and lump wave …
methods are employed to find the soliton, breather, kink, periodic kink, and lump wave …
[HTML][HTML] Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions
Quantum computing and machine learning (ML) have received significant developments
which have set the stage for the next frontier of creative work and usefulness. This paper …
which have set the stage for the next frontier of creative work and usefulness. This paper …
The impact of cost function globality and locality in hybrid quantum neural networks on nisq devices
Quantum neural networks (QNNs) are often challenged with the problem of flat cost function
landscapes during training, known as barren plateaus (BP). A solution to potentially …
landscapes during training, known as barren plateaus (BP). A solution to potentially …
Unified framework for quantum classification
Quantum machine learning is an emerging field that combines machine learning with
advances in quantum technologies. Many works have suggested great possibilities of using …
advances in quantum technologies. Many works have suggested great possibilities of using …
[HTML][HTML] AutoQML: Automatic generation and training of robust quantum-inspired classifiers by using evolutionary algorithms on grayscale images
A new hybrid system is proposed for automatically generating and training quantum-inspired
classifiers on grayscale images by using multiobjective genetic algorithms. It is defined a …
classifiers on grayscale images by using multiobjective genetic algorithms. It is defined a …
Quantum generative adversarial networks for anomaly detection in high energy physics
The standard model (SM) of particle physics represents a theoretical paradigm for the
description of the fundamental forces of nature. Despite its broad applicability, the SM does …
description of the fundamental forces of nature. Despite its broad applicability, the SM does …
The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of hqnns
Recent advances in quantum computing and machine learning have brought about a
promising intersection of these two fields, leading to the emergence of quantum machine …
promising intersection of these two fields, leading to the emergence of quantum machine …
Robust in practice: Adversarial attacks on quantum machine learning
State-of-the-art classical neural networks are observed to be vulnerable to small crafted
adversarial perturbations. A more severe vulnerability has been noted for quantum machine …
adversarial perturbations. A more severe vulnerability has been noted for quantum machine …