Projects

QCC: Quantum Computer Compilers

The ultimate goal of this project is to develop high-level programming languages and compilers for the quantum computers of the future. Our initial focus is to develop effective algorithms that can be used in quantum computer compilers to produce efficient quantum circuits for different quantum technologies from high-level specifications of quantum computations.

Aho, Svore

Learning Theory

Professor Servedio is interested in the connection between quantum computing and computational learning theory. He has related known lower bound techniques in these two areas and has used tools from cryptography to exhibit a general class of learning problems which are much more efficiently solvable in a quantum framework than in the corresponding classical framework. A goal for future work is to more fully understand how quantum computing power can be used to obtain efficient learning algorithms for well-studied learning problems.

Continuous Algorithms & Complexity

Many applications in science and engineering have continuous mathematical models. Examples of such models are high dimensional integration, path integration, partial differential equations, and continuous optimization. Such problems are usually solved numerically; they can only be solved to within an uncertainty e.

We seek new algorithms for quantum computers for important continuous applications. There is a belief that there are a number of killer applications which will enjoy exponential speed-ups over classical computation while requiring only a small or modest number of qubits or qunats. Preliminary investigations suggest that path integrals and high-dimensional finite integrals can be big winners on quantum computers. Click here for more indormation.

Traub, Wozniakowski, Papageorgiou, Kwas, Bessen

Last modified: October 27, 2003