# Configurable Algorithms for Histopathologic Cancer Detection on Quantum Hardware

arXiv: https://arxiv.org/abs/2606.21752 · HTML: https://mlsanity.com/paper/2606.21752/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [A hybrid quantum image edge detector for the NISQ era](https://mlsanity.com/paper/2203.12072/) (2022)
- [Experimental quantum pattern recognition in IBMQ and diamond NVs](https://mlsanity.com/paper/2205.00561/) (2022)
- [A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images](https://mlsanity.com/paper/1710.01249/) (2017)
- [Histopathological Cancer Detection Using Hybrid Quantum Computing](https://mlsanity.com/paper/2302.04633/) (2023)

## Built on by (most cited first, 1 of 1)

- [Generative Quantum Machine Learning Methods for Cancer Detection using Augmented Datasets](https://mlsanity.com/paper/2610.15110/) (2026)

## Abstract (arXiv)

Histopathologic cancer detection is challenging due to tissue variability, staining differences, and subtle visual distinctions between disease classes. We propose two quantum algorithms for this task: a configurable dual-gradient CSWAP circuit (DG-CSWAP) that computes multi-directional edge responses in a single execution via per-pixel local Ry encoding, and a hardware-efficient destructive swap circuit (DG-DST) natively matched to quantum processing unit (QPU) gate sets at substantially lower circuit complexity. We prove algebraic equivalence between DG-CSWAP and DG-DST, enabling a two-circuit QPU validation strategy. A three-stage NISQ mitigation pipeline, including readout error correction, bias subtraction, and slope regression, reduces single-pixel hardware MSE by ~8x. Validated on five quantum processors via Amazon Braket, the method achieves inter-platform Pearson r ~ 0.93-0.94 across all local-simulator pairs. Compared to a prior Quantum Fourier Transform (QFT) based amplitude-encoding baseline requiring 12-qubit global state preparation and a three-model ensemble (85.55% on PatchCamelyon), the proposed method uses shot-based measurements, executes on real quantum hardware, and achieves 79.80% accuracy with a single ResNet-50. A Lite configuration delivers a 17x preprocessing speedup at a 2.59% accuracy cost. To the best of our knowledge, this is the first quantum hardware implementation study with noise mitigation for histopathologic image classification.