54 citations · 115 across the 11 of their papers we have counts for
12 papers
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…
InterQ: Communication-Aware Scheduling Across Modular QPUs with Classical and Quantum Links
Vinooth Kulkarni, Jaehyun Lee, Lauren Li +5
As quantum computing scales toward practical workloads, future systems are expected to move beyond single monolithic processors toward modular architectures that connect multiple Q…
QuMod: Parallel Quantum Job Scheduling on Modular QPUs using Circuit Cutting
Vinooth Kulkarni, Aaron Orenstein, Xinpeng Li +3
The quantum computing community is increasingly positioning quantum processors as accelerators within classical HPC workflows, analogous to GPUs and TPUs. However, many real-world…
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…
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…
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…
Quantum machine learning framework for longitudinal biomedical studies
Maria Demidik, Filippo Utro, Alexey Galda +3
Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders…
How quantum computing can enhance biomarker discovery
Frederik F. Flöther, Daniel Blankenberg, Maria Demidik +7
Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very…
Towards quantum computing for clinical trial design and optimization: A perspective on new opportunities and challenges
Hakan Doga, M. Emre Sahin, Joao Bettencourt-Silva +10
Clinical trials are pivotal in the drug discovery process to determine the safety and efficacy of a drug candidate. The high failure rates of these trials are attributed to deficie…
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…