Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation
Quantum computing is a game-changing technology for global academia, research centers
and industries including computational science, mathematics, finance, pharmaceutical …
and industries including computational science, mathematics, finance, pharmaceutical …
Recent advances for quantum neural networks in generative learning
Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …
beyond the reach of classical computers. A leading method towards achieving this goal is …
Escaping from the barren plateau via gaussian initializations in deep variational quantum circuits
Variational quantum circuits have been widely employed in quantum simulation and
quantum machine learning in recent years. However, quantum circuits with random …
quantum machine learning in recent years. However, quantum circuits with random …
One qubit as a universal approximant
A Pérez-Salinas, D López-Núñez, A García-Sáez… - Physical Review A, 2021 - APS
A single-qubit circuit can approximate any bounded complex function stored in the degrees
of freedom defining its quantum gates. The single-qubit approximant presented in this work …
of freedom defining its quantum gates. The single-qubit approximant presented in this work …
Quantum error correction with quantum autoencoders
DF Locher, L Cardarelli, M Müller - Quantum, 2023 - quantum-journal.org
Active quantum error correction is a central ingredient to achieve robust quantum
processors. In this paper we investigate the potential of quantum machine learning for …
processors. In this paper we investigate the potential of quantum machine learning for …
Variational quantum anomaly detection: Unsupervised mapping of phase diagrams on a physical quantum computer
One of the most promising applications of quantum computing is simulating quantum many-
body systems. However, there is still a need for methods to efficiently investigate these …
body systems. However, there is still a need for methods to efficiently investigate these …
Variational quantum one-class classifier
One-class classification (OCC) is a fundamental problem in pattern recognition with a wide
range of applications. This work presents a semi-supervised quantum machine learning …
range of applications. This work presents a semi-supervised quantum machine learning …
[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 …
Searching for anomalous quartic gauge couplings at muon colliders using principal component analysis
YF Dong, YC Mao, JC Yang - The European Physical Journal C, 2023 - Springer
Searching for new physics (NP) is one of the areas of high-energy physics that requires the
most processing of large amounts of data. At the same time, quantum computing has huge …
most processing of large amounts of data. At the same time, quantum computing has huge …
Unsupervised detection of decoupled subspaces: Many-body scars and beyond
Highly excited eigenstates of quantum many-body systems are typically featureless thermal
states. Some systems, however, possess a small number of special, low-entanglement …
states. Some systems, however, possess a small number of special, low-entanglement …