Abstract
In the near-term, hybrid quantum-classical algorithms hold great potential for outperforming classical approaches. Understanding how these two computing paradigms work in tandem is critical for identifying areas where such hybrid algorithms could provide a quantum advantage. In this work, we study a QAOA-based quantum optimization approach by implementing the Variational Quantum Factoring (VQF) algorithm. We execute experimental demonstrations using a superconducting quantum processor, and investigate the trade off between quantum resources (number of qubits and circuit depth) and the probability that a given biprime is successfully factored. In our experiments, the integers 1099551473989, 3127, and 6557 are factored with 3, 4, and 5 qubits, respectively, using a QAOA ansatz with up to 8 layers and we are able to identify the optimal number of circuit layers for a given instance to maximize success probability. Furthermore, we demonstrate the impact of different noise sources on the performance of QAOA, and reveal the coherent error caused by the residual ZZ-coupling between qubits as a dominant source of error in a near-term superconducting quantum processor.
Highlights
While near-term quantum devices are approaching the limits of classical tractability[1,2], they are limited in the number of physical qubits, and can only execute finite-depth circuits with sufficient fidelity to be useful in applications[3]
The variational quantum factoring (VQF) algorithm maps the problem of factoring into an optimization problem[12]
We analyzed the performance of VQF on a fixedfrequency transmon superconducting quantum processor and investigated several kinds of classical and quantum resource tradeoffs
Summary
While near-term quantum devices are approaching the limits of classical tractability[1,2], they are limited in the number of physical qubits, and can only execute finite-depth circuits with sufficient fidelity to be useful in applications[3]. VQF is an algorithm for tackling an NP problem with a tunable trade off between classical and quantum computing resources, and is feasible for near-term devices. VQF uses classical preprocessing to reduce integer factoring to a combinatorial optimization problem which can be solved using the quantum approximate optimization algorithm (QAOA)[11].
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