# Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation

arXiv: https://arxiv.org/abs/1903.10963 · HTML: https://mlsanity.com/paper/1903.10963/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

## Built on by (most cited first, 9 of 9)

- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes](https://mlsanity.com/paper/2210.17037/) (2022)
- [Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks](https://mlsanity.com/paper/2303.17523/) (2023)
- [AI-Powered Algorithm-Centric Quantum Processor Topology Design](https://mlsanity.com/paper/2412.13805/) (2024)
- [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)
- [Computational Performance Bounds Prediction in Quantum Computing with Unstable Noise](https://mlsanity.com/paper/2507.17043/) (2025)
- [Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing](https://mlsanity.com/paper/2606.12816/) (2026)

## Abstract (arXiv)

NISQ (Noisy, Intermediate-Scale Quantum) computing requires error mitigation to achieve meaningful computation. Our compilation tool development focuses on the fact that the error rates of individual qubits are not equal, with a goal of maximizing the success probability of real-world subroutines such as an adder circuit. We begin by establishing a metric for choosing among possible paths and circuit alternatives for executing gates between variables placed far apart within the processor, and test our approach on two IBM 20-qubit systems named Tokyo and Poughkeepsie. We find that a single-number metric describing the fidelity of individual gates is a useful but imperfect guide. Our compiler uses this subsystem and maps complete circuits onto the machine using a beam search-based heuristic that will scale as processor and program sizes grow. To evaluate the whole compilation process, we compiled and executed adder circuits, then calculated the KL-divergence (a measure of the distance between two probability distributions). For a circuit within the capabilities of the hardware, our compilation increases estimated success probability and reduces KL-divergence relative to an error-oblivious placement.