Qoncord: A Multi-Device Job Scheduling Framework for Variational Quantum Algorithms

TL;DR

Qoncord is an adaptive job scheduling framework for Variational Quantum Algorithms that optimizes resource utilization and reduces execution time by intelligently dividing training into exploratory and fine-tuning phases across different quantum devices.

Contribution

It introduces a novel scheduling approach that differentiates between training phases and device fidelities to improve efficiency and solution quality for VQAs in cloud environments.

Findings

Qoncord achieves 17.4x faster execution than baseline methods.

It provides 13.3% better solutions within the same time budget.

The framework effectively balances device fidelity and queueing delays.

Abstract

Quantum computers face challenges due to limited resources, particularly in cloud environments. Despite these obstacles, Variational Quantum Algorithms (VQAs) are considered promising applications for present-day Noisy Intermediate-Scale Quantum (NISQ) systems. VQAs require multiple optimization iterations to converge on a globally optimal solution. Moreover, these optimizations, known as restarts, need to be repeated from different points to mitigate the impact of noise. Unfortunately, the job scheduling policies for each VQA task in the cloud are heavily unoptimized. Notably, each VQA execution instance is typically scheduled on a single NISQ device. Given the variety of devices in the cloud, users often prefer higher-fidelity devices to ensure higher-quality solutions. However, this preference leads to increased queueing delays and unbalanced resource utilization. We propose Qoncord, an automated job scheduling framework to address these cloud-centric challenges for VQAs. Qoncordleverages the insight that not all training iterations and restarts are equal, Qoncord strategically divides the training process into exploratory and fine-tuning phases. Early exploratory iterations, more resilient to noise, are executed on less busy machines, while fine-tuning occurs on high-fidelity machines. This adaptive approach mitigates the impact of noise and optimizes resource usage and queuing delays in cloud environments. Qoncord also significantly reduces execution time and minimizes restart overheads by eliminating low-performance iterations. Thus, Qoncord offers similar solutions 17.4x faster. Similarly, it can offer 13.3% better solutions for the same time budget as the baseline.