Quantum and Hybrid Quantum-Classical Algorithms
The quantum and hybrid quantum-classical algorithms group develops theory and algorithms to effectively run noisy intermediate-scale quantum devices and tackle practical problems through hybridization of quantum and classical hardware. The long-term goal is to investigate the limits and push the boundaries of novel quantum hardware usage through such techniques as effective domain decomposition, parameter optimization and learning, adaptive quantum circuit generation, and development of quantum error correcting codes for realistic channel models.
Such challenges as the qubit connectivity limitations, the high level of noise, the overhead of full error-correction, and concerns about scalability raise questions about the ability of near-term quantum hardware to effectively incorporate a larger number of qubits and deliver the theoretical speedups promised by many algorithms developed since the 1990s. Hybridization of quantum and classical algorithms is one of the expedient answers that researchers suggest today to tackle real-life applications with existing quantum hardware. These hybrid algorithms combine both classical and quantum computers in an attempt to take advantage of “the best of both worlds”, leveraging the power of quantum computation while using a classical machine to address the limitations of existing noisy intermediate scale quantum computers. In our group we develop, study limitations, and try to achieve an advantage over classical computing in areas such as optimization, machine learning, and simulation.
We are also interested in developing algorithms for scalable quantum simulators that are extremely important for quantum algorithm development and verification while existing quantum hardware remains expensive. For example, we work on solving optimization problems related to simulation of the Quantum Approximate Optimization Algorithm that is one of the most studied quantum optimization algorithms and is considered to be the prime candidate for demonstrating quantum advantage. There is a worldwide race underway amongst top quantum information science researchers to find combinatorial optimization problems and their instances that run efficiently and faster on quantum devices rather than on classical computers. One of the critical bottlenecks is to find circuit parameters faster on a classical computer in order to accelerate variational quantum-classical frameworks. The specialized quantum simulators speed up research on finding circuit parameters and quantum advantage algorithms.
Participating Faculty: Safro, Todorov, Garcia-Frias, Ghandehari, Plechac, Peng