Computer Science Education

The improvement of computer science education is a continuing problem, with many opportunities for work. This is a broad research area that can employ almost any other area of computer science.

Research in this area includes:

• Autograder development and evaluation
• Novel teaching systems
• Novel student evaluation systems
• Course prerequisite knowledge evaluation and education

Quantum Machine Learning

Quantum Machine Learning (QML) is an application of quantum computing to any machine learning task, whether the data is classical or quantum in nature. Most classical research topics in machine learning have some counterpart in QML research.

Example research topics in this area include:

• Apply a quantum variational classifier to classical datasets in an effort to achieve better or quicker results
• Implement an advanced machine learning model using quantum computing, such as a quantum CNN or quantum reinforcement learning
• Work on improving the QML framework, such as data encoding and ansatz selection
• Apply protoype quantum computing features to QML to demonstrate value of new advances in the field

Quantum Algorithms

There are a variety of quantum algorithms available to solve certain problems, some of which provide a speedup over classical approaches. Perhaps best known is Shor’s algorithm, which provides an exponential speedup in factoring. However, Shor’s algorithm cannot be applied to large numbers in the near term, due to limitations of quantum hardware. There are many other algorithms which can be implemented on such hardware, though.

Research in this area may include the implementation of a quantum algorithm in order to solve a problem and demonstrate its value, especially in other domains. Novel quantum algorithms can be found in research papers. Some of these algorithms are implemented and shared in the IBM Qiskit Textbook.

Quantum Optimization

Many problems in Computer Science are too difficult to be solved directly. Instead, many problems are solved using optimization, such as the Travelling Salesman Problem. Quantum computing offers many opportunities to find new solutions to such problems, including through quantum optimization routines.

An example of research in this area is converting a classical optimization problem to a quadratic unconstrained binary optimization (QUBO) problem and using a quantum solution, such as a Variational Quantum Eigensolver (VQE) to solve the problem.

Quantum Simulation and Chemistry

Quantum computing has proven a valuable asset as a tool for simulating, especially in (quantum) chemistry. Using quantum simulation, molecules can be simulated and information about those molecules extracted. Such simulations can be used to determine information including the energy required for a reaction to occur and other chemical and physical properties of molecules and materials.

Research in this area includes:

• Intelligent ansatz selection, such as in ADAPT-VQE. Related papers by Prof. Sophia Economou can be found here.
• Simulate/develop new molecules or drugs

Many other current areas in quantum chemistry can be found here.