(Quantum Accelerator) Coreset Quantum Computing- Addressing Large Data Sets with Small Quantum Computers
Abstract
Quantum Information Sciences. Our research objective is to apply near-term quantum computers with just tens of noisy qubits to solve optimization problems involving thousands or millions of variables. Our work will extend our recent work on quantum computation with coresets, which are compact summaries of large classical data sets. Thus far, we have demonstrated how noisy quantum computers with a small number of qubits could address k-means clustering on very large classical data sets spanning several GB. While our initial results are already promising, they are a lower bound on the potential of quantum computing with coresets. In particular, our previous technique employs the quantum computer just once in a non-interactive fashion. We now propose to instead construct the coreset iteratively through an interactive quantum-classical hybrid algorithm. We anticipate better results from this interactive algorithm, which works even when the quantum computer only provides approximate samples from a low-temperature optimization state. In addition to performing simulations, we will optimize the experimental realization of our algorithms and test them on hardware. This will involve the compilation of the underlying algorithms down to pulse-level hardware primitives and noise-aware qubit mapping; both are research areas where we have pioneered work. The potential impact of this research is to extend the reach of quantum speedups to large data sets, whereas most prior work has focused of problems bounded in size by the number of qubits.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 07, 2023
- Source ID
- FA95502110033
Entities
People
- Frederic Chong
Organizations
- Air Force Office of Scientific Research
- United States Navy
- University of Chicago