# Compilation, Optimization, Error Mitigation, and Machine Learning in Quantum Algorithms

arXiv: https://arxiv.org/abs/2506.15760 · HTML: https://mlsanity.com/paper/2506.15760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [Trotter error bounds and dynamic multi-product formulas for Hamiltonian simulation](https://mlsanity.com/paper/2306.12569/) (2023)
- [A Hybrid Classical-Quantum HPC Workload](https://mlsanity.com/paper/2312.04933/) (2023)

## Abstract (arXiv)

This paper discusses the compilation, optimization, and error mitigation of quantum algorithms, essential steps to execute real-world quantum algorithms. Quantum algorithms running on a hybrid platform with QPU and CPU/GPU take advantage of existing high-performance computing power with quantum-enabled exponential speedups. The proposed approximate quantum Fourier transform (AQFT) for quantum algorithm optimization improves the circuit execution on top of an exponential speed-ups the quantum Fourier transform has provided.