QGANs with quantum generators and classical discriminators generate financial time series matching target distributions and desired temporal correlations, with quality varying by circuit depth, bond dimension, and simulation method.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2verdicts
UNVERDICTED 2representative citing papers
A six-qubit quantum reservoir achieves over 86% accuracy in classifying stock trend movements for quantum-sector companies using daily and intraday volume data.
citing papers explorer
-
Quantum generative modeling for financial time series with temporal correlations
QGANs with quantum generators and classical discriminators generate financial time series matching target distributions and desired temporal correlations, with quality varying by circuit depth, bond dimension, and simulation method.
-
A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets
A six-qubit quantum reservoir achieves over 86% accuracy in classifying stock trend movements for quantum-sector companies using daily and intraday volume data.