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

2019/01/30 by Prakash Murali, Murali, Prakash, Jonathan M. Baker +7 · 28 citations
Computer Science · #Quantum Computing Algorithms and Architecture #Parallel Computing and Optimization Techniques #Quantum Information and Cryptography

paper · pdf · doi:10.48550/arxiv.1901.11054

Abstract

A massive gap exists between current quantum computing (QC) prototypes, and\nthe size and scale required for many proposed QC algorithms. Current QC\nimplementations are prone to noise and variability which affect their\nreliability, and yet with less than 80 quantum bits (qubits) total, they are\ntoo resource-constrained to implement error correction. The term Noisy\nIntermediate-Scale Quantum (NISQ) refers to these current and near-term systems\nof 1000 qubits or less. Given NISQ's severe resource constraints, low\nreliability, and high variability in physical characteristics such as coherence\ntime or error rates, it is of pressing importance to map computations onto them\nin ways that use resources efficiently and maximize the likelihood of\nsuccessful runs.\n This paper proposes and evaluates backend compiler approaches to map and\noptimize high-level QC programs to execute with high reliability on NISQ\nsystems with diverse hardware characteristics. Our techniques all start from an\nLLVM intermediate representation of the quantum program (such as would be\ngenerated from high-level QC languages like Scaffold) and generate QC\nexecutables runnable on the IBM Q public QC machine. We then use this framework\nto implement and evaluate several optimal and heuristic mapping methods. These\nmethods vary in how they account for the availability of dynamic machine\ncalibration data, the relative importance of various noise parameters, the\ndifferent possible routing strategies, and the relative importance of\ncompile-time scalability versus runtime success. Using real-system\nmeasurements, we show that fine grained spatial and temporal variations in\nhardware parameters can be exploited to obtain an average 2.9x (and up to\n18x) improvement in program success rate over the industry standard IBM\nQiskit compiler.\n

Citations

• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

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