Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer
Abstract
Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on trapped-ion quantum computer. We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY-QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 13, 2022
- Source ID
- 10.1038/s41598-022-20853-w
Entities
People
- Dylan Herman
- Marco Pistoia
- Pierre Minssen
- Pradeep Niroula
- Romina Yalovetzky
- Ruslan Shaydulin
- Shaohan Hu
Organizations
- Air Force Office of Scientific Research
- Defense Advanced Research Projects Agency
- National Science Foundation
- United States Department of Energy