Not All Qubits Are Created Equal

TLDR

Noisy Intermediate‑Scale Quantum (NISQ) devices, with only tens to hundreds of qubits, lack fault tolerance but can still benefit certain quantum algorithms. The authors aim to study qubit‑allocation and qubit‑movement problems and introduce variation‑aware policies that exploit device error‑rate heterogeneity. They analyze IBM‑Q20 characterization data, develop Variation‑Aware Qubit Movement (VQM) and Variation‑Aware Qubit Allocation (VQA) policies, and evaluate them using a simulation‑based IBM‑Q20 model. The policies reduce the impact of qubit and link variability, improving system reliability by up to 1.7× on IBM‑Q20 and 1.9× on IBM‑Q5.

Abstract

Existing and near-term quantum computers are not yet large enough to support fault-tolerance. 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 for entanglement). We observe that there can be variation in the error rates of different qubits and links, which can impact the decisions for qubit movement and qubit allocation. We analyze publicly available characterization data for the IBM-Q20 to quantify the variation and show that there is indeed significant variability in the error rates of the qubits and the links connecting them. We show that the device variability has a significant 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. Our evaluations, with a simulation-based model of IBM-Q20, show that Variation-Aware policies can improve the system reliability by up to 1.7x. We also evaluate our policies on the IBM-Q5 machine and demonstrate that our proposal significantly improves the reliability of real systems (up to 1.9X).