Quantum Computing for Multi-omics Analyses (ISMB 2026)#
ISMB 2026 Tutorial — Washington, DC (July 12, 2026)
An in-depth, hands-on tutorial exploring how quantum computing enables advanced multi-omics analysis and hybrid ML workflows.
Overview#
Join us for an interactive full-day tutorial covering the foundations and applications of quantum computing (QC) in multi-omics data analysis.
Participants will:
Learn how to preprocess and encode biological data for quantum algorithms
Explore quantum machine learning (QML) and hybrid models
Understand data complexity measures to assess when QC can outperform classical approaches
Work hands-on with real-world datasets and QBioCode
Instructors#
Aritra Bose — Staff Research Scientist, IBM Profile
Filippo Utro — Senior Research Scientist, IBM Profile
Laxmi Parida — IBM Fellow Profile
Learning Objectives#
Participants will:
Understand quantum computing fundamentals (states, circuits, gates)
Learn preprocessing of multi-omics data for QML
Analyze data complexity and ML limitations
Apply quantum ML and hybrid pipelines
Benchmark quantum vs classical models
Prerequisites#
Create an account: IBM Quantum
(Optional) Qiskit Summer School QML
Basic ML and multi-omics knowledge
Review QBioCode
Agenda#
Format
Expand each item to access description, slides, and hands-on material.
Session I — Foundations (09:00–10:45)#
1. Introduction (09:00–09:15)
2. Quantum computing fundamentals with Qiskit (09:15–09:45)
Introduction to: - quantum states, gates, circuits - basic Qiskit workflows
Includes short hands-on demo.
Materials
- 📄 Slides:
💻 Notebook: Intro to Qiskit
📘 Background: Quantum Machine Learning
3. Data complexity measures & learning algorithms (09:45–10:15)
Understanding intrinsic dataset complexity:
correlations
intrinsic dimension
limits of classical ML
Materials
📄 Slides: Characterizing Data Complexity in Machine Learning
📘 Background: Data complexity documentation
☕ Coffee Break (10:45–11:00)
Session II — QBioCode Applications (11:00–13:00)#
1. Qprofiler in multi-omics data (11:00–11:45)
Hands-on session using QProfiler for biological datasets.
Materials
💻 Notebook: Tutorial QProfiler
📄 Slides: QBC
- 📘 Documentation:
2. QSage (11:45–12:15)
Meta-learning approach for automated model selection.
Materials
💻 Notebook: Tutorial QSage
📄 Slides: QBC
📘 Documentation: QSage
🍽️ Lunch Break (13:00–14:00)
Session III — Hybrid learning for biological networks and multi-omics (14:00–16:00)#
1. Hybrid quantum-classical graph learning (14:00–14:45)
Graph-based learning approaches using hybrid QC pipelines.
Materials - 📄 Slides: TBD
2. QuVINE notebook (14:45–15:00)
Demonstration of the QuVINE workflow.
Materials - 💻 Notebook: TBD
☕ Coffee Break (16:00–16:15)
Session IV — Hands-on + Discussion (16:15–18:00)#
1. Continue implementation & results analysis (16:15–17:15)
Extend experiments and interpret results.
Materials - 💻 Notebook: TBD
2. Interactive Q&A (17:15–17:45)
Discussion, feedback, and open questions.
Materials#
Slides: TBD
Notebooks: TBD
GitHub: QBioCode
Audience#
Computational biologists
Bioinformaticians
Data scientists in life sciences
Clinicians and practitioners
Suitable for early- to senior-level researchers