Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers

Abstract

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 realsystem 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.

Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers

“…Even with 1000 gates, the compile times are under 15 minutes. These execution times can be easily improved with known optimizations for SMT compilers [43,44]. This evaluation gives us confidence that our methods will be practical even on large NISQ-era workloads.…”

“…Our contributions include the following. First, it is known that device characteristics affect compilation quality and program reliability [43]. However, measuring all device characteristics (akin to measuring the full process map) is an intractable problem due to exponential scaling.…”

Noise resilience of variational quantum compiling

“…Clearly, error mitigation techniques will be necessary to make use of NISQ devices. Several promising error mitigation strategies have recently emerged, including zero-noise extrapolation [2], quasi-probability decomposition [2], post-selection [3,4], noise-aware compiling [5], and machine learning for circuit-depth compression [6]. Let us consider two other strategies for error mitigation in what follows.…”

t|ket⟩: a retargetable compiler for NISQ devices

“…A range of gate-level circuit-optimisation techniques have been explored, including the use of phase polynomials [38] and constraint programming [39]. There are also promising results for using information on the noise characteristics and fidelities of the target device to assist compilation [40,41,42]. Meanwhile at the level of machine control there have been efforts to optimise the implementation of variational algorithms using automatic differentiation and interleaving compilation with execution [43,44].…”