Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicators
Efficient methods for loading given classical data into quantum circuits are essential for
various quantum algorithms. In this paper, we propose an algorithm called Approximate …
various quantum algorithms. In this paper, we propose an algorithm called Approximate …
On the expressibility and overfitting of quantum circuit learning
CC Chen, M Watabe, K Shiba, M Sogabe… - ACM Transactions on …, 2021 - dl.acm.org
Applying quantum processors to model a high-dimensional function approximator is a
typical method in quantum machine learning with potential advantage. It is conjectured that …
typical method in quantum machine learning with potential advantage. It is conjectured that …
Efficient discrete feature encoding for variational quantum classifier
Recent days have witnessed significant interests in applying quantum-enhanced techniques
for solving a variety of machine learning tasks. Variational methods that use quantum …
for solving a variety of machine learning tasks. Variational methods that use quantum …
Quantum semi-supervised generative adversarial network for enhanced data classification
K Nakaji, N Yamamoto - Scientific reports, 2021 - nature.com
In this paper, we propose the quantum semi-supervised generative adversarial network
(qSGAN). The system is composed of a quantum generator and a classical …
(qSGAN). The system is composed of a quantum generator and a classical …
Quantum tensor networks, stochastic processes, and weighted automata
Modeling joint probability distributions over sequences has been studied from many
perspectives. The physics community developed matrix product states, a tensor-train …
perspectives. The physics community developed matrix product states, a tensor-train …
Implementation and learning of quantum hidden markov models
V Markov, V Rastunkov, A Deshmukh, D Fry… - arXiv preprint arXiv …, 2022 - arxiv.org
In this article we use the theory of quantum channels and open quantum systems to provide
an efficient unitary characterization of a class of stochastic generators known as quantum …
an efficient unitary characterization of a class of stochastic generators known as quantum …
Quantum circuit learning with error backpropagation algorithm and experimental implementation
M Watabe, K Shiba, CC Chen, M Sogabe… - Quantum …, 2021 - mdpi.com
Quantum computing has the potential to outperform classical computers and is expected to
play an active role in various fields. In quantum machine learning, a quantum computer has …
play an active role in various fields. In quantum machine learning, a quantum computer has …
Learning quantum drift-diffusion phenomenon by physics-constraint machine learning
C Li, Y Yang, H Liang, B Wu - IEEE/ACM Transactions on …, 2022 - ieeexplore.ieee.org
Recently, deep learning (DL) is widely used to detect physical phenomena and has
obtained encouraging results. Several works have shown that it can learn quantum …
obtained encouraging results. Several works have shown that it can learn quantum …
Quantum circuit parameters learning with gradient descent using backpropagation
M Watabe, K Shiba, M Sogabe, K Sakamoto… - arXiv preprint arXiv …, 2019 - arxiv.org
Quantum computing has the potential to outperform classical computers and is expected to
play an active role in various fields. In quantum machine learning, a quantum computer has …
play an active role in various fields. In quantum machine learning, a quantum computer has …
Expressiveness and learning of hidden quantum markov models
Extending classical probabilistic reasoning using the quantum mechanical view of
probability has been of recent interest, particularly in the development of hidden quantum …
probability has been of recent interest, particularly in the development of hidden quantum …