About the Group
The Quantum Software Engineering (QSE) group is part of the High Assurance Software Laboratory (HASLab) at INESC TEC. We are part of the University of Minho where several of our researchers are faculty members at the Department of Informatics and contribute to teaching, supervision, and research leadership.
We pursue a coherent program in open-source quantum software engineering: algorithms with provable guarantees, learning and optimization with both near-term and hybrid workflows, formal reasoning, and scalable simulation through novel distributed HPC-based techniques.
Research focus
We bring quantum algorithms, learning, and formal methods together under a single software engineering view of quantum computing.
- Quantum algorithms & computational complexity
- Quantum machine learning & optimization
- Formal methods for quantum software
People & collaborations
We connect students, faculty, and industry around reproducible quantum software: from formal foundations to learning and optimization workflows and HPC-scale simulation.
- Joint supervision and co-authorship across INESC TEC • UMinho • INL/QLOC
- Open-source software, benchmark suites, University courses and reproducible experiment pipelines from notebooks to HPC jobs.
- Building bridges from prototypes and case studies to technology transfer: validated implementations, performance reports, and deployable workflows
HPC & infrastructure
We run quantum workloads at scale with emphasis on performance, scalability, and reproducibility.
- HPC-scale distributed quantum simulators using EuroHPC Deucalion as a primary platform
- Distributed workflows spanning multiple simulator backends (state-vector, tensor, Feynman path)
- Hybrid quantum–HPC pipelines: job orchestration, reproducible benchmarking, CPU/GPU execution
Research Areas
Our work sits at the intersection of theory and engineering: we develop algorithms with clear guarantees, build learning and optimization workflows that are experimentally robust, and design foundations for correctness—while benchmarking and deploying quantum workloads at scale on HPC infrastructure.
Quantum Algorithms, Circuits & Complexity
Design and analysis of quantum algorithms with complexity-theoretic guarantees, including provable speedups, separations from classical models, and resource-aware algorithm engineering.
- Phase-estimation & Quantum Monte-Carlo
- Quantum walks
- Adaptive VQE
- Unconditional quantum advantages from shallow circuits
Quantum Machine Learning & Optimization
Quantum-enhanced and quantum-inspired learning and decision-making: generative models, reinforcement learning agents, and optimization problem solvers, grounded in a clear view of trainability, classical simulability, and performance utility.
- Quantum generative models from Born machines
- Quantum reinforcement learning agents
- Quantum Kernel methods
- Quantum optimization
- Trainability vs classical simulability vs utility trade-offs
Formal methods for quantum software
Quantum software engineering foundations: semantic structures and algorithmic calculi for systematic derivation of quantum programs in a compositional way.
- Semantic frameworks unifying classical control and quantum data
- Algorithmic calculi for compositional derivation of quantum programs
- Dynamic logics and contracts for quantum algorithms and their verification
- Compositional coordination of distributed quantum systems and networks
- ZX-Calculus
HPC–Quantum Simulation
Large-scale simulation and performance engineering for quantum distributed workloads on HPC systems, enabling reproducible experiments, simulator benchmarking, and hybrid quantum–HPC pipelines.
- Quantum State-vector/tensor/Feynman/Pauli propagation simulation at scale
- Benchmarking across backends and architectures
- Distributed quantum machine learning workloads
- Hybrid workflows and tooling for HPC deployment
Software & Projects
We develop open-source software and participate in research and industry projects spanning quantum algorithms, formal methods, and HPC-scale simulation.
HPC-Quantum Tutorials
Tutorials • HPC deployment & benchmarks
Hands-on material for running large-scale quantum simulations on HPC systems (job scripts, benchmarking, and best practices) and guidelines to use the EuroHPC Deucalion supercomputer also managed by INESC TEC.
GitHubQWAK
Open-source • Quantum walks
A software toolkit for quantum walk experiments and related algorithmic exploration.
GitHubAn Interpreter for a Concurrent Quantum Language
Open-source • Languages & semantics
Interpreter and tooling for a concurrent quantum language, supporting research at the interface of quantum programming models and formal foundations.
GitHubTrainability of PQC Softmax Policies
Open-source • QML / RL experiments
Code and experiments studying trainability, concentration, and variance behaviour of PQC-based softmax policies (with benchmarking utilities).
GitHubQuantum Tree Search
Open-source • Quantum algorithms
Implementation of a Quantum Tree Search (QTS) library. Grover-based approach for searching unstructured trees with quantum speedup. Qiskit-based implementation with examples.
GitHubQuantum Agent Software Package (QASP)
Software • To be announced
Research software for quantum agents (RL / generative) and hybrid training pipelines.
(Repository link to be added when public.)
BANKSY
FCT Project · 2025–2028
New non-classical logics and reasoning tools for real-world settings with incomplete, uncertain, or even contradictory information—bridging formal foundations to practical applications.
Project newsQuantELM
FCT Project · 2023–2024
Quantum Extreme Learning Machines - FCT exploratory project - Joint work with Center for Applied Photonics.
Project pagePeople
We build reliable quantum software end-to-end: from foundations (semantics, verification, complexity) to scalable implementations (simulation on HPC and near-term algorithms).
Luís Soares Barbosa
Foundations for reliable software—semantics, calculi, and algebraic/logic methods—bridging to quantum computing and quantum software engineering.
Luís Paulo Santos
Scalable quantum computation from state vector to Feynman path simulators and applications. Quantum optimization and Quantum Monte Carlo methods for numerical integration and computer graphics.
José Nuno Oliveira
Program calculation and algebraic methods for software design and verification—tools that also inform how we reason about correctness and structure in quantum software.
Renato Neves
Quantitative methods for programming and reasoning (e.g., metrics, probabilities, resource-aware calculi), intersecting with cyber-physical systems and emerging quantum programming models.
André Sequeira
Quantum and quantum-inpired algorithms for machine learning and optimization. Trainability/simulability/utility trade-off in parameterized quantum circuits.
Teaching
Many INESC TEC researchers are also professors at the University of Minho. Indeed, INESC TEC is co-responsible for the MSc in Quantum Information, part of the Physics Engineering program at UMinho. Below are the ongoing courses.
Publications
Browse our publications by year, or search by author, title, venue, or keyword.
Seminar series
The Quantum Software Group seminar series features talks on quantum algorithms, quantum machine learning, quantum–HPC workflows, and formal methods for quantum software, by internal members and invited guests.
Latest seminar
Wednesday, 28 January 2026 · 15:00 (WET)
Speaker: Antonio Lorenzin
Title: Categories of abstract and noncommutative measurable spaces
Abstract: In quantum probability, the category of $$C^$$-algebras and completely positive unital (cpu) maps is a well-established setting. However, its classical side, given by commutative $$C^$$-algebras, does not contain all regular conditional probabilities, a central concept in classical probability. This shortcoming is due to the inherent topological nature of $$C^*$$-algebras, which ties them to continuous rather than measurable structures. To address this, we investigate what noncommutative (or quantum) measurable spaces should be, by establishing adjunctions and equivalences between special classes of $$C^*$$-algebras and cpu maps and categories of measurable spaces and Markov kernels. On the classical side, these can be equivalently described by Boolean σ-algebras (also known as abstract measurable spaces). Moreover, our operator-algebraic analysis equips these structures with a generalized notion of Markov kernels, realized as POVMs. Ultimately, we obtain a categorical framework in which both classical and quantum probability coexist, and the classical side has regular conditional probabilities.
Opportunities
We are always interested in motivated people who want to work on quantum software engineering, from foundations and algorithms to HPC-scale simulation and applications.
MSc & PhD Supervision · Research Stays
We supervise thesis projects and host research visits, with the University of Minho as the main degree-granting institution.
If you are interested in an MSc / PhD thesis or a research stay with the group, please send a short CV and a brief description of your interests to luis.p.santos@inesctec.pt.
Consulting & Industry Collaboration
Companies and institutions interested in exploring quantum algorithms, quantum-inspired optimization, and HPC-based quantum simulation. This includes feasibility studies, proof-of-concept implementations, and benchmarking of quantum and classical solvers.
For consulting or collaborative projects, please get in touch with andre.m.sequeira@inesctec.pt.
Contact & Links
Contact
- HASLab – High Assurance Software Lab, INESC TEC
- Informatics department, building 7, University of Minho
- Braga, Portugal
- Additional information? Reach out to andre.m.sequeira@inesctec.pt