Noisy intermediate-scale quantum algorithms
A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …
integer factorization and unstructured database search requires millions of qubits with low …
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 …
Noise-assisted quantum autoencoder
Quantum autoencoder is an efficient variational quantum algorithm for quantum data
compression. However, previous quantum autoencoders fail to compress and recover high …
compression. However, previous quantum autoencoders fail to compress and recover high …
Quantum error correction with quantum autoencoders
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 …
[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 adaptive agents with efficient long-term memories
Central to the success of adaptive systems is their ability to interpret signals from their
environment and respond accordingly—they act as agents interacting with their …
environment and respond accordingly—they act as agents interacting with their …
Clustering and enhanced classification using a hybrid quantum autoencoder
Quantum machine learning (QML) is a rapidly growing area of research at the intersection of
classical machine learning and quantum information theory. One area of considerable …
classical machine learning and quantum information theory. One area of considerable …
On Exploring the Potential of Quantum Auto-Encoder for Learning Quantum Systems
Y Du, D Tao - IEEE Transactions on Neural Networks and …, 2024 - ieeexplore.ieee.org
The frequent interactions between quantum computing and machine learning revolutionize
both fields. One prototypical achievement is the quantum auto-encoder (QAE), as the …
both fields. One prototypical achievement is the quantum auto-encoder (QAE), as the …
Jarzynski-like equality of nonequilibrium information production based on quantum cross-entropy
The two-time measurement scheme is well studied in the context of quantum fluctuation
theorem. However, it becomes infeasible when the random variable determined by a single …
theorem. However, it becomes infeasible when the random variable determined by a single …