EQC : Ensembled Quantum Computing for Variational Quantum Algorithms

• URL: http://arxiv.org/abs/2111.14940v1
• Date: Mon, 29 Nov 2021 20:38:18 GMT
• Title: EQC : Ensembled Quantum Computing for Variational Quantum Algorithms
• Authors: Samuel Stein, Yufei Ding, Nathan Wiebe, Bo Peng, Karol Kowalski, Nathan Baker, James Ang and Ang Li
• Abstract summary: Variational quantum algorithm (VQANIS) is one of the most promising approaches for harvesting the power of quantum computers. VQAs face considerable system and time-dependant noise and prohibitively slow training speeds. In this paper, we propose a way of building up a quantum backend for variational quantum algorithms.
• Score: 17.451674516812172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational quantum algorithm (VQA), which is comprised of a classical optimizer and a parameterized quantum circuit, emerges as one of the most promising approaches for harvesting the power of quantum computers in the noisy intermediate scale quantum (NISQ) era. However, the deployment of VQAs on contemporary NISQ devices often faces considerable system and time-dependant noise and prohibitively slow training speeds. On the other hand, the expensive supporting resources and infrastructure make quantum computers extremely keen on high utilization. In this paper, we propose a virtualized way of building up a quantum backend for variational quantum algorithms: rather than relying on a single physical device which tends to introduce temporal-dependant device-specific noise with worsening performance as time-since-calibration grows, we propose to constitute a quantum ensemble, which dynamically distributes quantum tasks asynchronously across a set of physical devices, and adjusting the ensemble configuration with respect to machine status. In addition to reduced machine-dependant noise, the ensemble can provide significant speedups for VQA training. With this idea, we build a novel VQA training framework called EQC that comprises: (i) a system architecture for asynchronous parallel VQA cooperative training; (ii) an analytic model for assessing the quality of the returned VQA gradient over a particular device concerning its architecture, transpilation, and runtime conditions; (iii) a weighting mechanism to adjust the quantum ensemble's computational contribution according to the systems' current performance. Evaluations comprising 500K circuit evaluations across 10 IBMQ devices using a VQE and a QAOA applications demonstrate that EQC can attain error rates close to the most performant device of the ensemble, while boosting the training speed by 10.5x on average (up to 86x and at least 5.2x).

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