Quantum Medical Imaging Algorithms
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
5 papers.
- 2021Learning quantum data with the quantum Earth Mover's distancecited by 13
- 2025Benchmarking MedMNIST dataset on real quantum hardwarecited by 6
- 2022Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitationscited by 2
- 2025Domain-Aware Quantum Circuit for QMLcited by 0
- 2026Quantum CT via Dynamic Interval Encoding and Prior-Balanced QUBO Reconstructioncited by 0
- 2026Quantum CT via Dynamic Interval Encoding and Prior-Balanced QUBO Reconstruction23 Jun 2026
- 2025Domain-Aware Quantum Circuit for QML19 Dec 2025
- 2025Benchmarking MedMNIST dataset on real quantum hardware18 Feb 2025
- 2022Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations2 Dec 2022
- 2021Learning quantum data with the quantum Earth Mover's distance8 Jan 2021