[Paper Review] Navigating the dynamic noise landscape of variational quantum algorithms with QISMET

Gokul Subramanian Ravi, Kaitlin N. Smith+6 more|arXiv (Cornell University)|Sep 25, 2022

Quantum Computing Algorithms and Architecture4 citations

TL;DR

This paper proposes QISMET, a runtime controller that mitigates transient noise in variational quantum algorithms (VQAs) by skipping iterations with high predicted error impact. By estimating transient errors and avoiding problematic execution windows, QISMET improves VQA fidelity by 1.3x–3x over baselines and 1.6x–2.4x over alternatives across multiple quantum hardware platforms.

ABSTRACT

Transient errors from the dynamic NISQ noise landscape are challenging to comprehend and are especially detrimental to classes of applications that are iterative and/or long-running, and therefore their timely mitigation is important for quantum advantage in real-world applications. The most popular examples of iterative long-running quantum applications are variational quantum algorithms (VQAs). Iteratively, VQA's classical optimizer evaluates circuit candidates on an objective function and picks the best circuits towards achieving the application's target. Noise fluctuation can cause a significant transient impact on the objective function estimation of the VQA iterations / tuning candidates. This can severely affect VQA tuning and, by extension, its accuracy and convergence. This paper proposes QISMET: Quantum Iteration Skipping to Mitigate Error Transients, to navigate the dynamic noise landscape of VQAs. QISMET actively avoids instances of high fluctuating noise which are predicted to have a significant transient error impact on specific VQA iterations. To achieve this, QISMET estimates transient error in VQA iterations and designs a controller to keep the VQA tuning faithful to the transient-free scenario. By doing so, QISMET efficiently mitigates a large portion of the transient noise impact on VQAs and is able to improve the fidelity by 1.3x-3x over a traditional VQA baseline, with 1.6-2.4x improvement over alternative approaches, across different applications and machines. Further, to diligently analyze the effects of transients, this work also builds transient noise models for target VQA applications from observing real machine transients. These are then integrated with the Qiskit simulator.

Motivation & Objective

• To address the challenge of dynamic, transient noise in Noisy Intermediate-Scale Quantum (NISQ) devices that disrupts iterative quantum applications.
• To improve the fidelity and convergence of variational quantum algorithms (VQAs) affected by fluctuating noise sources.
• To design a runtime controller that proactively avoids execution instances with high transient error impact.
• To enable more accurate and reliable VQA optimization in real-world quantum computing scenarios.
• To reduce the impact of transient errors without requiring prior knowledge of device-specific noise patterns.

Proposed method

• QISMET estimates transient error in real time by analyzing the deviation of current VQA iteration gradients from their predicted transient-free values.
• It employs a controller that compares estimated transient error against a threshold to decide whether to skip an iteration.
• The method uses a feedback loop to maintain the direction of gradient updates faithful to the ideal, transient-free scenario.
• QISMET runs each VQA iteration alongside a re-execution of the previous iteration to enable error estimation and skip decisions.
• It leverages statistical analysis of circuit execution outcomes to detect sudden shifts in output distribution indicative of transient noise.
• The controller operates at runtime, making skip decisions per iteration based on real-time noise assessment.

Experimental results

Research questions

• RQ1 How do transient noise fluctuations in NISQ devices impact the convergence and accuracy of variational quantum algorithms?
• RQ2 Can real-time detection and avoidance of high-transient-error iterations improve VQA fidelity?
• RQ3 How does QISMET’s dynamic skip strategy compare to static error mitigation and classical filtering techniques?
• RQ4 What is the performance overhead and practical feasibility of integrating QISMET into existing VQA workloads?
• RQ5 To what extent can QISMET reduce the impact of transient errors without prior knowledge of device-specific noise behavior?

Key findings

• QISMET improves VQA fidelity by 1.3x to 3x compared to a traditional VQA baseline across multiple quantum hardware platforms.
• It achieves 1.6x to 2.4x higher fidelity than alternative error mitigation techniques, including Kalman filtering and classical filtering.
• The method effectively mitigates transient noise without requiring labeled training data or prior characterization of device noise.
• Overheads are minimal in practice, as most devices experience only a small number of egregious transient events, and QISMET reduces overall execution cost by avoiding failed iterations.
• QISMET outperforms classical filtering methods like Kalman and CFAR, which cannot proactively avoid high-impact transient events.
• The controller’s skip decisions are robust, with negligible performance loss even under adversarial noise patterns, due to the low likelihood of such patterns in real NISQ devices.