Configurable Algorithms for Histopathologic Cancer Detection on Quantum Hardware

2026-06-19

AI Summary

This work addresses the challenges in histopathological cancer classification arising from tissue heterogeneity, staining variability, and subtle lesion features by proposing two configurable quantum algorithms: a dual-gradient CSWAP circuit with pixel-wise Ry encoding (DG-CSWAP) and its hardware-efficient equivalent, DG-DST. Integrating a three-stage NISQ noise mitigation strategy—comprising readout error correction, bias subtraction, and slope regression—the study achieves the first demonstration of noise-mitigated pathological image classification on real quantum hardware (Amazon Braket). Experimental results confirm the algebraic equivalence of both circuits, with a single-model accuracy of 79.80%, substantially outperforming a QFT-based baseline. A lightweight configuration further attains a 17× preprocessing speedup at the cost of only 2.59% accuracy loss, demonstrating robust cross-platform consistency with Pearson correlation coefficients of 0.93–0.94 across five quantum processors.

Abstract

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.

Problem

• histopathologic cancer detection
• tissue variability
• staining differences
• visual distinctions
• image classification

Innovation

• quantum algorithms
• NISQ mitigation
• histopathologic image classification
• hardware-efficient quantum circuits
• edge response encoding