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Article
Quantum Science & Technology
Jun Qi et al.
Summary: In this work, an end-to-end quantum neural network TTN-VQC is proposed, which combines a quantum tensor network based on tensor-train network (TTN) for dimensionality reduction and a VQC for functional regression. The error performance analysis is conducted to evaluate the representation and generalization powers of TTN-VQC, and the optimization properties are characterized using the Polyak-Lojasiewicz condition. Additionally, experiments on a handwritten digit classification dataset are conducted to validate the theoretical analysis.
NPJ QUANTUM INFORMATION
(2023)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora et al.
Summary: Quantum computing is a promising research area with the D-Wave machine offering solutions to complex problems. In order to address problems with p-body interactions, it is necessary to compute 2-body effective Hamiltonians, with recent meta-heuristic methods showing promising results.
APPLIED SOFT COMPUTING
(2021)
Article
Multidisciplinary Sciences
M. Cerezo et al.
Summary: In this study, the authors rigorously prove that defining cost functions with local observables can avoid the barren plateau problem, while defining them with global observables leads to exponentially vanishing gradients. The results indicate a connection between locality and trainability in variational quantum algorithms (VQAs).
NATURE COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Kumar S. Chandar
Summary: Stock market prediction is a critical issue in the financial market. Artificial Neural Networks (ANNs) combined with nature inspired algorithms are increasingly playing an important role in various fields, including the stock market. This study proposed nine new integrated models for forecasting intraday stock prices based on three ANNs and nature inspired algorithms. PSO-BPNN model yielded the highest prediction accuracy among the developed models.
PATTERN RECOGNITION LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Cenk Tuysuz et al.
Summary: The HL-LHC will increase the rate of particle collisions and detector hits, posing challenges in reconstructing particle trajectories. The study explores converting a novel graph neural network model to a hybrid quantum-classical model, and compares the training performance of parametrized quantum circuits to quantify expected benefits for future developments in circuit-based quantum-classical graph neural networks.
QUANTUM MACHINE INTELLIGENCE
(2021)
Article
Quantum Science & Technology
Yuxuan Du et al.
Summary: The study examines the learning potential of parameterized quantum circuits with gradient-based classical optimizers on noisy intermediate-scale quantum devices and finds that high gate noise, limited quantum measurements, and deep circuit depth may result in slower learning. It also proves that certain concept classes can be efficiently learned through this method.
Review
Physics, Applied
M. Cerezo et al.
Summary: Variational quantum algorithms, utilizing classical optimizers to train parameterized quantum circuits, have emerged as a leading strategy to address the limitations of quantum computing. Despite challenges, they appear to be the best hope for achieving quantum advantage.
NATURE REVIEWS PHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Andrea Skolik et al.
Summary: The study focuses on a layerwise learning strategy for parametrized quantum circuits, which incrementally grows circuit depth and updates subsets of parameters to mitigate challenges posed by cost function landscapes; this strategy can help avoid barren plateaus of the error surface due to sampling noise, making it preferable for execution on noisy intermediate-scale quantum devices.
QUANTUM MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Hyunki Lim et al.
PATTERN RECOGNITION LETTERS
(2020)
Article
Optics
Maria Schuld et al.
Review
Quantum Science & Technology
Marcello Benedetti et al.
QUANTUM SCIENCE AND TECHNOLOGY
(2019)
Article
Optics
Maria Schuld et al.
Article
Multidisciplinary Sciences
Jarrod R. McClean et al.
NATURE COMMUNICATIONS
(2018)
Article
Quantum Science & Technology
John Preskill
Review
Multidisciplinary Sciences
Jacob Biamonte et al.
Article
Computer Science, Artificial Intelligence
Giovanni Acampora et al.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2015)
Article
Computer Science, Artificial Intelligence
Ferrante Neri et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2012)
Article
Computer Science, Artificial Intelligence
Xianshun Chen et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2011)