Current quantum computers cannot outperform conventional supercomputers on real-world problems. Future quantum computers will be able to solve problems important to materials science and chemistry, to code-breaking and to machine learning. Which features of a problem make it a good target for a quantum computer, and what resources quantum computers use to solve problems faster, are difficult and poorly understood questions. This project will make possible the hire of a theoretical computer scientist working in quantum information at Tufts University. This hire will contribute to research in quantum information science and to training the next generation of quantum information scientists who will go on to achieve the goals of the National Quantum Initiative (NQI) act.
The new Faculty hire in Computer Science at Tufts University will work on questions in quantum algorithms and the use of existing, near-term and far-future quantum computers. The search for quantum algorithms, distinct quantum complexity classes, and other theoretical computer science topics are central to quantum information. Currently algorithms are known in the areas of number theory, computational algebra, quantum simulation from quantum chemistry to high energy physics, quantum optimization and machine learning. Questions of current interest span the range of future quantum computers from near term to far future. They include the search for algorithms that can provide a quantum advantage on near-term machines with 50-100 qubits but which lack error correction, the so-called Noisy Intermediate Scale Quantum (NISQ) devices. They also include the search for completely new quantum algorithms for large-scale quantum computers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
