A Case for Variability-Aware Policies for NISQ-Era Quantum Computers (1805.10224v1)

Abstract: Recently, IBM, Google, and Intel showcased quantum computers ranging from 49 to 72 qubits. While these systems represent a significant milestone in the advancement of quantum computing, existing and near-term quantum computers are not yet large enough to fully support quantum error-correction. Such systems with few tens to few hundreds of qubits are termed as Noisy Intermediate Scale Quantum computers (NISQ), and these systems can provide benefits for a class of quantum algorithms. In this paper, we study the problems of Qubit-Allocation (mapping of program qubits to machine qubits) and Qubit-Movement(routing qubits from one location to another to perform entanglement). We observe that there exists variation in the error rates of different qubits and links, which can have an impact on the decisions for qubit movement and qubit allocation. We analyze characterization data for the IBM-Q20 quantum computer gathered over 52 days to understand and quantify the variation in the error-rates and find that there is indeed significant variability in the error rates of the qubits and the links connecting them. We define reliability metrics for NISQ computers and show that the device variability has the substantial impact on the overall system reliability. To exploit the variability in error rate, we propose Variation-Aware Qubit Movement (VQM) and Variation-Aware Qubit Allocation (VQA), policies that optimize the movement and allocation of qubits to avoid the weaker qubits and links and guide more operations towards the stronger qubits and links. We show that our Variation-Aware policies improve the reliability of the NISQ system up to 2.5x.

• The paper introduces novel variation-aware qubit movement (VQM) and allocation (VQA) policies that enhance reliability in noisy quantum computers.
• By using Dijkstra’s algorithm for qubit routing, VQM improves mean instructions before failure by up to 1.5 times in long-running programs.
• VQA optimizes initial qubit mapping by selecting robust qubit links, significantly increasing the probability of successful trials in benchmark tests.

Variation-Aware Quantum Computing: A Study on Qubit Allocation and Movement

As quantum computing developments progress towards more sophisticated systems, practical challenges associated with Noisy Intermediate-Scale Quantum (NISQ) computers have garnered significant attention. One such challenge is the optimization of qubit allocation and movement by accounting for the variability in error rates across qubits and their interconnections. The paper by Tannu and Qureshi introduces novel strategies, specifically Variation-Aware Qubit Movement (VQM) and Variation-Aware Qubit Allocation (VQA), designed to enhance the reliability of NISQ systems by exploiting this variability.

Core Contributions

The primary focus of the paper is to quantify and mitigate the impact of variable error rates present in qubits and their coupling links. This variability is accentuated in the IBM-Q20 quantum computer, which was observed over an extended period. The authors identified that error rates can vary up to 7.5 times across different links.

Variation-Aware Qubit Movement (VQM)

VQM aims to improve the reliability of qubit routing processes in quantum computations. It selects paths for qubit movement that maximize the probability of success, rather than merely minimizing the number of SWAP operations. By utilizing Dijkstra's algorithm to find failure-minimizing routes, VQM can potentially increase Mean Instructions Before Failure (MIBF) by up to 1.5 times for long-running programs, providing a more robust execution in noisy environments.

Variation-Aware Qubit Allocation (VQA)

The VQA policy enhances initial qubit mapping by considering the reliability of qubit links involved in frequent operations. Rather than solely focusing on reducing SWAP operations, VQA maps program qubits to robust physical qubits, which has shown to improve Probability of Successful Trials (PST) in certain benchmarks. This policy realizes significant benefits particularly when running programs that utilize half or fewer of the available qubits.

Implications and Future Directions

The implications of these variation-aware strategies extend into both the practical and theoretical realms of quantum computing. Practically, they suggest modification to compiler designs to account for hardware variability, thus improving quantum computation's real-world efficacy. Theoretically, these findings challenge the assumption of uniform error rates in quantum computer design, propelling a reevaluation of existing quantum algorithms and error-correction strategies.

Future developments could focus on adaptive strategies that dynamically adjust qubit allocations and movement paths based on real-time error metrics or extending these strategies to quantum architectures beyond the IBM-Q20. The findings indicate a promising direction towards more resilient quantum computation practices, which are critical as we push towards larger and more complex quantum systems.

This analysis demonstrates the importance of adapting to the inherent imperfections in quantum devices, encouraging the development of robust, variability-driven computational frameworks. These contributions are poised to influence intelligent partitioning policies and other architectural decisions critical to the evolution of quantum computing systems.