9 papers
Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Natansh Mathur, Panagiotis Kl. Barkoutsos, Masako Yamada +2
Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circu…
Protein folding on a 64 qubit trapped-ion hardware via counterdiabatic quantum optimization
Alejandro Gomez Cadavid, Pavle NikaÄeviÄ, Pranav Chandarana +11
We report the largest trapped-ion hardware demonstration of lattice protein-folding optimization to date, using bias-field digitized counterdiabatic quantum optimization (BF-DCQO)…
Quantum-Enhanced Neural Exchange-Correlation Functionals
Igor O. Sokolov, Gert-Jan Both, Art D. Bochevarov +6
Kohn-Sham Density Functional Theory (KS-DFT) provides the exact ground state energy and electron density of a molecule, contingent on the as-yet-unknown universal exchange-correlat…
Learning Reduced Representations for Quantum Classifiers
Patrick Odagiu, Vasilis Belis, Lennart Schulze +6
Data sets that are specified by a large number of features are currently outside the area of applicability for quantum machine learning algorithms. An immediate solution to this im…
Experimental differentiation and extremization with analog quantum circuits
Evan Philip, Julius de Hond, Vytautas Abramavicius +8
Solving and optimizing differential equations (DEs) is ubiquitous in both engineering and fundamental science. The promise of quantum architectures to accelerate scientific computi…
Evaluation of derivatives using approximate generalized parameter shift rule
Vytautas Abramavicius, Evan Philip, Kaonan Micadei +5
Parameter shift rules are instrumental for derivatives estimation in a wide range of quantum algorithms, especially in the context of Quantum Machine Learning. Application of singl…