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

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.

This paper has been accepted at the 2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO)

References in corpus (17)

• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• A variational eigenvalue solver on a quantum processor
• Barren plateaus in quantum neural network training landscapes
• The theory of variational hybrid quantum-classical algorithms
• Error mitigation for short-depth quantum circuits
• Randomized Benchmarking of Quantum Gates
• An adaptive variational algorithm for exact molecular simulations on a quantum computer
• Validating quantum computers using randomized model circuits
• Theory of variational quantum simulation
• Mitigating measurement errors in multi-qubit experiments
• Symmetrised Characterisation of Noisy Quantum Processes
• Mitigation of readout noise in near-term quantum devices by classical post-processing based on detector tomography
• CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations
• Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
• QAOAKit: A Toolkit for Reproducible Study, Application, and Verification of the QAOA
• Red-QAOA: Efficient Variational Optimization through Circuit Reduction

Cited by in corpus (2)

• Accelerating Simulation of Quantum Circuits under Noise via Computational Reuse
• Qonductor: A Cloud Orchestrator for Quantum Computing