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

arXiv: https://arxiv.org/abs/1901.11054 · HTML: https://mlsanity.com/paper/1901.11054/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

## Built on by (most cited first, 22 of 22)

- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning](https://mlsanity.com/paper/2301.09138/) (2023)
- [Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow](https://mlsanity.com/paper/2109.03430/) (2021)
- ['Eliv'agar: Efficient Quantum Circuit Search for Classification](https://mlsanity.com/paper/2401.09393/) (2024)
- [Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects](https://mlsanity.com/paper/2407.00736/) (2024)
- [AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search](https://mlsanity.com/paper/2410.05115/) (2024)
- [Improving Quantum Circuit Synthesis with Machine Learning](https://mlsanity.com/paper/2306.05622/) (2023)
- [FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes](https://mlsanity.com/paper/2210.17037/) (2022)
- [RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training](https://mlsanity.com/paper/2311.16035/) (2023)
- [Graph Neural Network-Based Predictor for Optimal Quantum Hardware Selection](https://mlsanity.com/paper/2507.19093/) (2025)
- [AI-Powered Algorithm-Centric Quantum Processor Topology Design](https://mlsanity.com/paper/2412.13805/) (2024)
- [Quantum Implicit Neural Representations for Novel View Synthesis](https://mlsanity.com/paper/2601.05250/) (2026)
- [A Synergistic Compilation Workflow for Tackling Crosstalk in Quantum Machines](https://mlsanity.com/paper/2207.05751/) (2022)
- [Skipper: Improving the Reach and Fidelity of Quantum Annealers by Skipping Long Chains](https://mlsanity.com/paper/2312.00264/) (2023)
- [Profiling quantum circuits for their efficient execution on single- and multi-core architectures](https://mlsanity.com/paper/2407.12640/) (2024)
- [OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization](https://mlsanity.com/paper/2507.09682/) (2025)
- [AI Methods for Permutation Circuit Synthesis Across Generic Topologies](https://mlsanity.com/paper/2509.16020/) (2025)
- [Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nest](https://mlsanity.com/paper/2510.09578/) (2025)
- [Quantifying Gate Contribution in Quantum Feature Maps for Scalable Circuit Optimization](https://mlsanity.com/paper/2603.19805/) (2026)
- [QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning](https://mlsanity.com/paper/2605.12365/) (2026)
- [Hardware-aware Low-latency Quantum Compilation with Data-driven Lightweight Error Detection for Early Fault-Tolerant Systems](https://mlsanity.com/paper/2606.07666/) (2026)
- [Predicting Resource Efficient Hamiltonian Decomposition for Continuous-Time Quantum Walk Simulations](https://mlsanity.com/paper/2608.20660/) (2026)

## Abstract (arXiv)

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.