# Quantum Medical Imaging Algorithms

arXiv: https://arxiv.org/abs/2004.02036 · HTML: https://mlsanity.com/paper/2004.02036/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Learning quantum data with the quantum Earth Mover's distance](https://mlsanity.com/paper/2101.03037/) (2021)
- [Benchmarking MedMNIST dataset on real quantum hardware](https://mlsanity.com/paper/2502.13056/) (2025)
- [Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations](https://mlsanity.com/paper/2212.01312/) (2022)
- [Domain-Aware Quantum Circuit for QML](https://mlsanity.com/paper/2512.17800/) (2025)
- [Quantum CT via Dynamic Interval Encoding and Prior-Balanced QUBO Reconstruction](https://mlsanity.com/paper/2606.24561/) (2026)

## Abstract (arXiv)

A central task in medical imaging is the reconstruction of an image or function from data collected by medical devices (e.g., CT, MRI, and PET scanners). We provide quantum algorithms for image reconstruction with exponential speedup over classical counterparts when data is input as a quantum state. Since outputs of our algorithms are stored in quantum states, individual pixels of reconstructed images may not be efficiently accessed classically; instead, we discuss various methods to extract information from outputs using a variety of quantum post-processing algorithms.