The Job

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

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Science · Software Engineering

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In short

Ericsson is seeking a Master's thesis student to explore quantum geometry as a diagnostic framework for quantum kernel classifiers. This role involves investigating the Quantum Geometric Tensor (QGT) for understanding, optimizing, and characterizing quantum kernel classifiers on real quantum hardware, with applications in enterprise IT security.

Responsibilities

  • Develop a geometric framework for quantifying quantum kernel advantage using the Fubini–Study metric and Quantum Fisher Information Matrix.
  • Implement and compare geometry-aware optimization (quantum natural gradient) against standard optimizers for variational quantum circuit training.
  • Investigate how hardware noise distorts the geometric structure of quantum circuits by comparing multiple open-system QGT constructions.
  • Apply the framework to an enterprise security classification task (anomaly detection) and benchmark against classical machine learning baselines.
  • Manage a 20-week research project and communicate findings clearly in writing and presentation.

Requirements

  • 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.

Desired Qualifications

  • 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.

Benefits

  • Outstanding opportunity to use skills and imagination to push the boundaries of what is possible.
  • Chance to build solutions never seen before to some of the world’s toughest problems.
  • Challenging environment within a team of diverse innovators.
  • IBM Q hardware access is available for experimental validation.
#Quantum Computing#Quantum Geometry#Quantum Kernel Classifiers#Machine Learning#Quantum Circuits#Quantum Geometric Tensor#Quantum Natural Gradient#Hardware Noise#Enterprise IT Security#Python#IBM Q Hardware
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Company

Ericsson

Job Posted

1 day ago

Employment Type

Internship

WorkMode

On Site

Experience Level

Student

Locations

Stockholm, Sweden

Qualification

Master

Applicants

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