4 papers
Improving Quantum Recurrent Neural Networks with Amplitude Encoding
Jack Morgan, Hamed Mohammadbagherpoor, Eric Ghysels
Quantum machine learning holds promise for advancing time series forecasting. The Quantum Recurrent Neural Network (QRNN), inspired by classical RNNs, encodes temporal data into qu…
On the Classical Shadow Nonparametric Bootstrap
Eric Ghysels, Jack Morgan
Classical shadows are an efficient method for constructing an approximate classical description of a quantum state using very few measurements. In the paper we propose to enhance c…
On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models
Eric Ghysels, Jack Morgan
We formulate quantum computing solutions to a large class of dynamic nonlinear asset pricing models using algorithms, in theory exponentially more efficient than classical ones, wh…
On Quantum and Quantum-Inspired Maximum Likelihood Estimation and Filtering of Stochastic Volatility Models
Eric Ghysels, Jack Morgan, Hamed Mohammadbagherpoor
Stochastic volatility models are the backbone of financial engineering. We study both continuous time diffusions as well as discrete time models. We propose two novel approaches to…