Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nest

2025-10-10

AI Summary

Variational quantum algorithms (VQAs) face a fundamental trade-off among solution quality, convergence speed, and multi-task throughput—challenging simultaneous optimization on noisy intermediate-scale quantum (NISQ) hardware. Method: We propose a time-varying fidelity-mapping scheduling framework that dynamically allocates high- and low-fidelity qubit resources during VQA execution, integrated with Nest-based compilation and multi-VQA co-location. Unlike static mapping, our approach jointly optimizes solution fidelity, convergence rate, and task density within a unified scheduling paradigm. Contribution/Results: Evaluated on real superconducting quantum processors, our method achieves an average 12.7% improvement in final VQA performance, accelerates convergence by up to 3.2×, and enables 2–4× higher throughput compared to baseline schedulers. It establishes a scalable, fidelity-aware resource orchestration paradigm for efficient VQA deployment on approximate quantum hardware.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Variational quantum algorithms (VQAs) have the potential to demonstrate quantum utility on near-term quantum computers. However, these algorithms often get executed on the highest-fidelity qubits and computers to achieve the best performance, causing low system throughput. Recent efforts have shown that VQAs can be run on low-fidelity qubits initially and high-fidelity qubits later on to still achieve good performance. We take this effort forward and show that carefully varying the qubit fidelity map of the VQA over its execution using our technique, Nest, does not just (1) improve performance (i.e., help achieve close to optimal results), but also (2) lead to faster convergence. We also use Nest to co-locate multiple VQAs concurrently on the same computer, thus (3) increasing the system throughput, and therefore, balancing and optimizing three conflicting metrics simultaneously.

Problem

• Improving variational quantum algorithm performance and convergence
• Optimizing qubit fidelity mapping during VQA execution
• Increasing system throughput through concurrent VQA co-location

Innovation

• Dynamically varying qubit fidelity maps during execution
• Accelerating convergence of variational quantum algorithms
• Co-locating multiple algorithms to boost throughput