Benchmarking Quantum Algorithms Using Real-World Datasets

Quantum kernel methods are a way to use quantum computers for pattern recognition and data analysis. They map data into a high-dimensional quantum feature space, where classical and quantum similarities can be compared. Benchmarking these kernels helps researchers understand when quantum approaches outperform traditional machine-learning kernels such as the radial basis function (RBF) kernel.

In this project, students will use Qiskit, an open-source software development platform for building and testing quantum circuits, to benchmark quantum kernels using generated a real-world data sets.  Interested students should have experience with quantum information and/or Qiskit/Python and have completed a course in linear algebra.  Students will work directly with Prof. Hoffman and his graduate students.