54 citations · 54 across the 5 of their papers we have counts for
7 papers
manylp: reproducible, exact, and batched linear programming for genome-scale dynamic flux balance analysis
Fabio Cumbo
Dynamic flux balance analysis (dFBA) and its community, spatial and ensemble variants predict microbial metabolism by solving one linear program (LP) per species and time step, oft…
Logarithmic-scale variational quantum eigensolver for off-lattice protein structure prediction in continuous torsional angle space
Fabio Cumbo, Bryan Raubenolt, Varun Puram +3
Classical and current quantum approaches to protein structure prediction (QPSP) face limitations, notably massive qubit requirements restricting near-term models to simplistic on-l…
hdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures
Fabio Cumbo, Kabir Dhillon, Daniel Blankenberg
Following the initial publication of hdlib, a Python library for designing Vector-Symbolic Architectures (VSA), we introduce a major extension that significantly enhances its machi…
An Automatic Pipeline for the Integration of Python-Based Tools into the Galaxy Platform: Application to the anvi'o Framework
Fabio Cumbo, Jayadev Joshi, Daniel Blankenberg
The integration of command-line tools into the Galaxy platform is crucial for making complex computational methods accessible to a broader audience and ensuring reproducible resear…
Quantum Hyperdimensional Computing: a foundational paradigm for quantum neuromorphic architectures
Fabio Cumbo, Rui-Hao Li, Bryan Raubenolt +4
A significant challenge in quantum computing (QC) is developing learning models that truly align with quantum principles, as many current approaches are complex adaptations of clas…
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…
A perspective on protein structure prediction using quantum computers
Hakan Doga, Bryan Raubenolt, Fabio Cumbo +5
Despite the recent advancements by deep learning methods such as AlphaFold2, \textit{in silico} protein structure prediction remains a challenging problem in biomedical research. W…