Tutorials
The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.
Tutorial on
Better development with QuantumOps
Instructor
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Vlad Stirbu
University of Jyvaeskylae
Finland
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Brief Bio
Vlad Stirbu is a Senior Researcher in the Quantum Information and Computing (QIC) team at the University of Jyväskylä, Finland. He leads the Enhanced Middleware for Quantum Software (EM4QS) project and is the lead contributor to Qubernetes. He has extensive industry experience in product and research organizations at Nokia, where he has contributed to the development of augmented/virtual reality solutions, web-based smart spaces, and medical software. He holds a Ph.D. in Software Engineering from Tampere University of Technology, Finland and a M.Sc in Electrical Engineering and Computer Science from Politehnica University of Bucharest, Romania.
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Abstract
As quantum software projects grow in complexity, so does the need for reliable development workflows, reproducibility, and informed decision-making. This tutorial introduces QuantumOps, a lightweight and adaptable approach for tracking, managing, and improving quantum algorithm development. Drawing inspiration from DevOps principles, participants will learn how to track experiments, log results, and build feedback loops that accelerate progress and reduce wasted effort. The hands-on session will walk through practical tools and techniques that can be integrated into any existing workflow using popular quantum SDKs such as Qiskit and PennyLane.
Keywords
DevOps, experiment tracking, quantum software development
Aims and Learning Objectives
By the end of this tutorial, participants will be able to:
- Track quantum algorithm development experiments in a structured, reproducible way.
- Analyze performance metrics across different algorithm variants and runs.
- Integrate experiment tracking into their existing workflows with minimal overhead.
- Make informed decisions based on logged results to guide algorithmic design and optimization.
Target Audience
Quantum software developers and researchers working on algorithm design and implementation, especially those involved in iterative experimentation and performance tuning.
Prerequisite Knowledge of Audience
- Familiarity with at least one quantum SDK (e.g., Qiskit or PennyLane)
- Basic experience with JupyterLab or other notebook-based environments
- (Optional) Some understanding of experiment tracking or ML Ops principles
Detailed Outline
- Introduction (10 min)
- Motivation: Why QuantumOps?
- DevOps vs QuantumOps: Similarities and differences
- Overview of tools used in the tutorial
- Setting up the Environment (10 min)
- Installing and configuring the required packages
- Overview of the tutorial notebook
- Tracking Experiments (20 min)
- Logging parameters, results, and metadata
- Using simple logging tools (e.g., MLflow)
- Best practices for reproducibility in quantum experimentation
- Analyzing and Comparing Results (20 min)
- Structuring results for comparison
- Visualizing performance trends
- Interpreting experiment logs for optimization insights
- Integrating with Quantum SDKs (20 min)
- Example: Using QuantumOps with Qiskit
- Example: Using QuantumOps with PennyLane
- Tips for adapting to other toolkits
- Wrap-Up and Q&A (10 min)
- Key takeaways
- Open discussion: how can QuantumOps improve your work?
- Pointers to further resources and community tools
Tutorial on
The Mind in the Machine: A Quantum Theory of Software
Instructor
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Iaakov Exman
School of Computer Science, HIT = Holon Institute of Technology
Israel
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Brief Bio
Prof. Iaakov Exman, has a PhD in Physical Chemistry from the Hebrew University of Jerusalem, Israel, and has done post-doctoral research at Stanford University, California, USA, in the area of Artificial Intelligence-based discovery of potentially novel medication structures for the pharmaceutical industries. Back in Israel, he had ten years of experience at the Aerospace Industries and has founded and worked in parallel computing software start-ups. Currently he is a professor of Software Engineering at the School of Computer Science of the Holon Institute of Technology. His research interests and publications are centered on Theory of Software based upon linear algebraic methods and more recently on Quantum Software Theory equally applicable to quantum, classical and hybrid software systems. He is a coeditor of the book “Quantum Software – Aspects of Theory and System Design” (2024) published by Springer.
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Abstract
Software is the Mind of the Machine because Software is independent of any machine.
We view Software as a scientific discipline on its own. As such it is natural to expect it to be based upon a well-formulated mathematical theory. This tutorial overviews and explains why the suitable theoretical framework is a Quantum Theory of Software.
The cornerstone of the Quantum Software Theory is a careful choice of four axioms stated in terms of the Density Matrix, to represent any software system – be it quantum, classical or hybrid. The axioms enable all the essential Software system features.
The Density Matrix expresses Modularity, Structure-Behavior Duality and Class Encapsulation. Quantum evolution means Runnability, and quantum measurement enables Debugging.
How to generate the Density Matrix? Start from Natural Language Concepts, through a Bipartite Graph of Structors and Functionals, obtaining a Laplacian Matrix. A normalized Laplacian is the desired Density Matrix.
Since Artificial Intelligence (AI) is a higher abstraction level Software, for all purposes, the Quantum Software Theory (QST) is extensible to AI. Taking into account a recently published Quantization Hypothesis, one meets higher-level Bipartite Graphs, this time of Structors and Skills. Generating the Density Matrix completes the QST extension to AI.
Keywords
Quantum Software Theory; Density Matrix; Bipartite Graphs; Artificial Intelligence; Mind;
Aims and Learning Objectives
Learning the principles of Quantum Software Theory.
Target Audience
Participants of IQSoft-2025
Prerequisite Knowledge of Audience
Experience with Classical Software.
Detailed Outline
see abstract