3 papers
quant-ph2026
Probabilistic Design of Parametrized Quantum Circuits through Local Gate Modifications
Grier M. Jones, Aviraj Newatia, Alexander Lao +3
Within quantum machine learning, parametrized quantum circuits provide flexible quantum models, but their performance is often highly task-dependent, making manual circuit design c…
cs.CE2025
A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning
Shivesh Prakash, Nandan Patel, Hans-Arno Jacobsen +1
We introduce MHNpath, a machine learning-driven retrosynthetic tool designed for computer-aided synthesis planning. Leveraging modern Hopfield networks and novel comparative metric…
quant-ph2025
Parametrized Quantum Circuit Learning for Quantum Chemical Applications
Grier M. Jones, Viki Kumar Prasad, Ulrich Fekl +1
In the field of quantum machine learning (QML), parametrized quantum circuits (PQCs) -- constructed using a combination of fixed and tunable quantum gates -- provide a promising hy…