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arXiv:1805.10224

A Case for Variability-Aware Policies for NISQ-Era Quantum Computers

Swamit Tannu, Moinuddin K. Qureshi

Published May 25, 2018• 49 citations
arXivHTMLPDF

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.

DOI
10.48550/arxiv.1805.10224

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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.
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).
Quantum computing promises exponential speedups for an important class of problems.While quantum computers with few dozens of qubits have been demonstrated, these machines suffer from a high rate of gate errors.Such machines are operated in the Noisy Intermediate Scale Quantum (NISQ) mode of computing where the output of the machine can be erroneous.In this talk, I will discuss some of our recent work that aims to improve the reliability of NISQ computers by developing software techniques to mitigate hardware errors.Our first work exploits the variability in the error rates of qubits to steer more operations towards qubits with lower error rates and avoid qubits that are error-prone.Our second work looks at executing different versions of the programs each crafted to cause diverse mistakes so that the machine becomes less vulnerable to correlated errors.Our third work looks at exploiting the state-dependent bias in measurement errors (state 1 is more error-prone than state 0) and dynamically flips the state of the qubit to perform the measurement in the stronger state.We perform our evaluations on real quantum machines from IBM and demonstrate significant improvement in the overall system reliability.Finally, I will also briefly discuss the hardware aspect of designing largescale quantum computers, including cryogenic processor and cryogenic memory system.
A massive gap exists between current quantum computing (QC) prototypes, and the size and scale required for many proposed QC algorithms. Current QC implementations are prone to noise and variability which affect their reliability, and yet with less than 80 quantum bits (qubits) total, they are too resource-constrained to implement error correction. The term Noisy Intermediate-Scale Quantum (NISQ) refers to these current and near-term systems of 1000 qubits or less. Given NISQ's severe resource constraints, low reliability, and high variability in physical characteristics such as coherence time or error rates, it is of pressing importance to map computations onto them in ways that use resources efficiently and maximize the likelihood of successful runs. This paper proposes and evaluates backend compiler approaches to map and optimize high-level QC programs to execute with high reliability on NISQ systems with diverse hardware characteristics. Our techniques all start from an LLVM intermediate representation of the quantum program (such as would be generated from high-level QC languages like Scaffold) and generate QC executables runnable on the IBM Q public QC machine. We then use this framework to implement and evaluate several optimal and heuristic mapping methods. These methods vary in how they account for the availability of dynamic machine calibration data, the relative importance of various noise parameters, the different possible routing strategies, and the relative importance of compile-time scalability versus runtime success. Using real-system measurements, we show that fine grained spatial and temporal variations in hardware parameters can be exploited to obtain an average 2.9x (and up to 18x) improvement in program success rate over the industry standard IBM Qiskit compiler. Despite small qubit counts, NISQ systems will soon be large enough to demonstrate "quantum supremacy", i.e., an advantage over classical computing. Tools like ours provide significant improvements in program reliability and execution time, and offer high leverage in accelerating progress towards quantum supremacy.
Concerted efforts by the academia and the industries e.g., IBM, Google and Intel have brought us to the era of Noisy Intermediate-Scale Quantum (NISQ) computers. Qubits, the basic elements of quantum computer, have been proven extremely susceptible to different noises. Recent experiments have exhibited spatial variations among the qubits in NISQ hardware. Therefore, conventional mapping of qubit done without quality awareness results in significant loss of fidelity for a given workload. In this paper, we have analyzed the effects of various noise sources on the overall fidelity of the given workload for a real NISQ hardware. We have also presented novel optimization technique namely, Qubit Re-allocation (QURE) to maximize the sequence fidelity of a given workload. QURE is scalable and can be applied to future large scale quantum computers. QURE can improve the fidelity of a quantum workload up to 1.54X (1.39X on average) in simulation and up to 1.7X in real device compared to variation oblivious qubit allocation without incurring any physical overhead.
Quantum computing is a quickly growing field with great potential for future technology. Quantum computers in the current noisy intermediate-scale quantum (NISQ) era face two major limitations - qubit count and error vulnerability. Although quantum error correction (QEC) methods exist, they are not applicable to the current size of computers, requiring thousands of qubits, while current NISQ systems have hundreds at most. It is therefore imperative to improve the reliability of the circuits as much as possible to make them robust to the errors that will occur. One common approach is to adjust the compilation process of a circuit to create a final circuit with improved reliability. However, there are many decisions to be made when compiling that affect the final performance of the circuit, two of the most critical ones being the mapping of logical to physical qubits (the qubit allocation problem) and the movement of qubits to satisfy two-qubit gate adjacency requirements (the qubit routing problem). We focus on solving the qubit allocation problem and identifying initial layouts that reduce error. To identify these layouts, we combine reinforcement learning with a graph neural network (GNN)-based Q-network for analyzing both the connections and error rates of the graph-like backend of superconducting quantum computers to make mapping decisions, creating a Graph Neural Network Assisted Compilation (GNAQC) strategy. We provide both the circuit and the properties of the target backend as input to guide the decision-making process. We work with the IBM Qiskit API to compile and simulate our quantum circuits. We train the architecture using a set of four backends and six circuits and find that GNAQC generally outperforms pre-existing qubit allocation algorithms, increasing final relative output fidelity by roughly 12.7%.
In the era of noisy-intermediate-scale quantum computers, we expect to see quantum devices with increasing numbers of qubits emerge in the foreseeable future. To practically run quantum programs, logical qubits have to be mapped to the physical qubits by a qubit allocation algorithm. However, on present day devices, qubits differ by their error rate and connectivity. Here, we establish and demonstrate on current experimental devices a new allocation algorithm that combines the simulated annealing method with local search of the solution space using Dijkstra's algorithm. Our algorithm takes into account the weighted connectivity constraints of both the quantum hardware and the quantum program being compiled. New quantum programs will enable unprecedented developments in physics, chemistry, and materials science and our work offers an important new pathway toward optimizing compilers for quantum programs.