Master Thesis: Quantum geometry as a diagnostic framework for quantum kernel classifiers

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About this opportunity:
Ericsson is looking for a Master's thesis student with a strong background in quantum computing, physics, computer science, and machine learning to work on quantum machine learning (QML) and quantum geometry. This thesis explores how the geometric structure of quantum circuits - captured by the Quantum Geometric Tensor (QGT) - can be used to understand, optimize, and characterize quantum kernel classifiers running on real quantum hardware. You will investigate three interconnected research questions: when and why a quantum kernel outperforms a classical one; how variational quantum circuits can be trained more efficiently using geometry-aware optimization; and how hardware noise on a real quantum processor distorts the geometric structure of a quantum classifier. The work combines quantum information theory, differential geometry, and applied machine learning, with a concrete application in enterprise IT security. IBM Q hardware access is available for experimental validation.

What you will do: 
In this thesis you will develop a geometric framework for quantifying quantum kernel advantage over classical kernels using the Fubini–Study metric and the Quantum Fisher Information Matrix. You will implement and compare geometry-aware optimization (quantum natural gradient) against standard optimizers for variational quantum circuit training and investigate how hardware noise distorts the geometric structure of quantum circuits by comparing multiple open-system QGT constructions (Bures/Uhlmann, Sjöqvist, covariant-derivative) as noise diagnostics on real IBM Q hardware. You will apply the framework to an enterprise security classification task (anomaly detection under limited-data conditions) and benchmark against classical machine learning baselines.

The skills you bring:
• Enrolled in the final year of an MSc programme in Physics, Engineering Physics, Computer Science, Applied Mathematics, or a closely related field.
• Proficient in spoken and written English.
• Strong Python programming skills; experience with NumPy, pandas, and scikit-learn, or a clear willingness to learn.
• Solid understanding of quantum computing fundamentals: qubits, quantum gates, circuits, measurement, and entanglement.
• Understanding of supervised machine learning: classification, kernel methods (SVM), train/test splits, and standard evaluation metrics.
• Solid linear algebra and calculus: matrix operations, eigendecomposition, gradients, and partial derivatives.
• Ability to work independently, manage a 20-week research project, and communicate findings clearly in writing and presentation.
• Familiarity with quantum feature maps, quantum kernel estimation, or quantum support vector machines is a strong advantage.
• Understanding of the Quantum Geometric Tensor, quantum metric (Fubini–Study metric), Berry curvature, or related concepts from quantum geometry or quantum information is a strong advantage.
• Knowledge of open-system quantum mechanics - density matrices, decoherence, and noise models -  is a strong advantage.
• Experience with Qiskit, Qiskit Machine Learning, Qiskit Aer, or IBM Q hardware is a practical advantage.
• Introductory knowledge of differential geometry or information geometry (Riemannian metrics, geodesics) is useful but not required from day one.

Why join Ericsson?
At Ericsson, you will have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what is possible. To build solutions never seen before to some of the world’s toughest problems. You will be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

Encouraging a diverse and inclusive organization is core to our values at Ericsson, that' is why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer.