12 citations · 12 across the 6 of their papers we have counts for
6 papers
Protein folding on a 64 qubit trapped-ion hardware via counterdiabatic quantum optimization
Alejandro Gomez Cadavid, Pavle Nikačević, 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)…
Efficient Quantum Protein Structure Prediction with Problem-Agnostic Ansatzes
Hanna Linn, Rui-Hao Li, Alexander Holden +6
Accurately predicting protein structures from amino acid sequences remains a fundamental challenge in computational biology, with profound implications for understanding biological…
Designing lattice proteins with variational quantum algorithms
Hanna Linn, Lucas Knuthson, Anders Irbäck +3
Quantum heuristics have shown promise in solving various optimization problems, including lattice protein folding. Equally relevant is the inverse problem, protein design, where on…
Quantum Algorithm for Protein Structure Prediction Using the Face-Centered Cubic Lattice
Rui-Hao Li, Hakan Doga, Bryan Raubenolt +16
In this work, we present the first implementation of the face-centered cubic (FCC) lattice model for protein structure prediction with a quantum algorithm. Our motivation to encode…
Resource analysis of quantum algorithms for coarse-grained protein folding models
Hanna Linn, Isak Brundin, Laura García-Álvarez +1
Protein folding processes are a vital aspect of molecular biology that is hard to simulate with conventional computers. Quantum algorithms have been proven superior for certain pro…
Detecting quantum speedup of random walks with machine learning
Hanna Linn, Yu Zheng, Anton Frisk Kockum
We explore the use of machine-learning techniques to detect quantum speedup in random walks on graphs. Specifically, we investigate the performance of three different neural-networ…