most citedContextual Quantum Neural Networks for Stock Price Prediction

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

quant-ph2026

Dynamic Observable Subspaces: Fast Simulation of Observable Quantum Dynamics

Hannes Leipold

By decomposing quantum dynamics across the Lie orbits of a list of observables, we find polynomial bounded classical simulations for the dynamics of the quantum system given a poly…

quant-ph2026

Qvine: Vine Structured Quantum Circuits for Loading High Dimensional Distributions

David Quiroga, Hannes Leipold, Bibhas Adhikari

Loading high dimensional distributions is an important task for utilizing quantum computers on applications ranging from machine learning to finance. The high dimensionality leads…

quant-ph2026

A Perfectly Distributable Quantum-Classical Algorithm for Estimating Triangular Balance in a Signed Edge Stream

Steven Kordonowy, Bibhas Adhikari, Hannes Leipold

We develop a perfectly distributable quantum-classical streaming algorithm that processes signed edges to efficiently estimate the counts of triangles of diverse signed configurati…

quant-ph2025★ 2 cited

The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization

Steven Kordonowy, Hannes Leipold

The XY-mixer has widespread utilization in modern quantum computing, including in variational quantum algorithms, such as Quantum Alternating Operator Ansatz (QAOA). The XY ansatz…

cs.LG2025★ 4 cited

Contextual Quantum Neural Networks for Stock Price Prediction

Sharan Mourya, Hannes Leipold, Bibhas Adhikari

In this paper, we apply quantum machine learning (QML) to predict the stock prices of multiple assets using a contextual quantum neural network. Our approach captures recent trends…