X-ray projection radiography is a non-invasive imaging technique used in medical diagnostics and industrial inspection. The simulation of X-ray projections is commonly used to optimise acquisition protocols and improve image quality before performing costly scans. Classical photon transport simulations that include realistic X-ray scattering physics are computationally expensive because they require the sampling of a large number of distinct scattering paths. This limits the practical exploration of parameter spaces such as beam energy. Quantum computing offers the potential to solve high-dimensional problems faster by making use of quantum properties such as superposition. This work introduces a discrete-time quantum walk algorithm that simulates the transport of X-ray photons through heterogeneous volumes. It approximates the physics of X-ray projection radiography, including processes such as photoelectric absorption and higher-order scattering, including Compton and Rayleigh scattering. The quantum walk encodes all admissible photon paths into a single quantum state, enabling all scattering histories to be propagated simultaneously via the superposition principle. This quantum state representation enables flexible readout of various imaging modalities, including the primary, i.e., unscattered, image, or images exclusively containing Rayleigh and Compton scattering of specified orders. A quantitative comparison with classically computed reference simulations shows that the proposed quantum walk accurately reproduces radiographic projections, given the limitations of the underlying physical model. These results indicate that quantum circuits for X-ray transport can produce accurate radiographic images and imply that, as quantum hardware scales up, these algorithms could outperform classical Monte Carlo-based approaches in large-scale, scatter-aware virtual imaging studies.
X-ray projection radiography is a non-invasive imaging technique used in medical diagnostics and industrial inspection. The simulation of X-ray projections is commonly used to optimise acquisition protocols and improve image quality before performing costly scans. Classical photon transport simulations that include realistic X-ray scattering physics are computationally expensive because they require the sampling of a large number of distinct scattering paths. This limits the practical exploration of parameter spaces such as beam energy. Quantum computing offers the potential to solve high-dimensional problems faster by making use of quantum properties such as superposition. This work introduces a discrete-time quantum walk algorithm that simulates the transport of X-ray photons through heterogeneous volumes. It approximates the physics of X-ray projection radiography, including processes such as photoelectric absorption and higher-order scattering, including Compton and Rayleigh scattering. The quantum walk encodes all admissible photon paths into a single quantum state, enabling all scattering histories to be propagated simultaneously via the superposition principle. This quantum state representation enables flexible readout of various imaging modalities, including the primary, i.e., unscattered, image, or images exclusively containing Rayleigh and Compton scattering of specified orders. A quantitative comparison with classically computed reference simulations shows that the proposed quantum walk accurately reproduces radiographic projections, given the limitations of the underlying physical model. These results indicate that quantum circuits for X-ray transport can produce accurate radiographic images and imply that, as quantum hardware scales up, these algorithms could outperform classical Monte Carlo-based approaches in large-scale, scatter-aware virtual imaging studies.
X-ray scattering is foundational to imaging and spectroscopy across medical diagnostics, crystallography, and materials science. Classical simulation frameworks, while effective for bulk photon transport, typically rely on stochastic sampling and precomputed cross-sections that do not directly capture quantum coherence, interference, or entanglement effects. In this work, we investigate the feasibility of modeling X-ray photon– electron interactions using quantum computation. We develop a voxel-local interaction unit-cell Hamiltonian that captures both Rayleigh (elastic) and Compton-like (inelastic) scattering channels within a minimal 4-dimensional Hilbert space. The system is simulated on Noisy Intermediate-Scale Quantum (NISQ) hardware using Variational Quantum Eigensolvers (VQE) to obtain stationary energy states and Trotterized time evolution to model scattering dynamics. Using a compact 3-qubit encoding implemented in Qiskit, we analyze photon energy redistribution and angular dependence, and qualitatively compare the resulting trends with theoretical predictions such as the Klein–Nishina relation. The results demonstrate that even small-scale quantum processors can reproduce core qualitative features of X-ray scattering, though full physical fidelity remains limited by Hilbert space dimension, decoherence, and noise. We discuss scalability pathways toward multi-voxel simulation, model generalization to higher photon modes and electron states, and the prospective integration of quantum-native scattering modules into spectral CT and phase-sensitive imaging frameworks.
High-energy physics simulations traditionally rely on classical Monte Carlo methods to model complex particle interactions, often incurring significant computational costs. In this paper, we introduce a novel quantum-enhanced simulation framework that integrates discrete-time quantum walks with quantum amplitude estimation to model photon interaction cross sections. By mapping the probabilistic transport process of 10 MeV photons in a water medium onto a quantum circuit and focusing on Compton scattering as the dominant attenuation mechanism, we demonstrate that our approach reproduces classical probability distributions with high fidelity. Simulation results obtained via the IBM Qiskit quantum simulator reveal a quadratic speedup in amplitude estimation compared to conventional Monte Carlo methods. Our framework not only validates the feasibility of employing quantum algorithms for high-energy physics simulations but also offers a scalable pathway toward incorporating multiple interaction channels and secondary particle production. These findings underscore the potential of quantum-enhanced methods to overcome the computational bottlenecks inherent in large-scale particle physics simulations.
Quantum Computing (QC) technology has made tremendous progress recently. Today, first real QC systems are operational. The Fraunhofer Institute of Integrated Circuits IIS, Division Development Center X-ray Technology is participating in the project BayQS, which aims at identifying and evaluating potential applications of quantum computing in the field of non-destructive testing by means of X-ray imaging. Computed tomography (CT) is a well-established method of 3-D imaging in medicine and industry since several decades. The quality of the resulting volumetric datasets depends essentially on a set of around 20 physical parameters controlling the set-up of X-ray source, X-ray digital detector array, movable mechanical components, sample rotation, and image processing. Hence, we started investigating the potential use of a quantum-based optimization method to optimize CT data acquisition. As a first approach, we focused on the task to minimize the number of projection images required to achieve unaltered image quality when compared to an acquisition over the full 4π solid angle. Minimizing involves a cost function. There are several ways to define this cost function. In order to transfer the task to a QC system, the problem is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO). A decisive feature of the QUBO is, that is can be solved by a hybrid implementation on quantum devices, which combines a classical part to iteratively find the maximum of the cost function and a quantum circuit to evaluate the respective cost function for each combination of projections. This procedure is called quantum approximate optimization algorithm (QAOA). The experiments were performed via the qiskit script language on an IBM Quantum System One located in Ehingen, Germany. This QC system offers 27 qubits and enables first real world experiments with small test datasets.
Deep learning has transformed radiology, but the computational cost of training and deploying modern models is outpacing what classical hardware can support. Quantum computing has been proposed as one response to this bottleneck: an n-qubit system can represent 2n states simultaneously, although practical speedups require efficient algorithms, data encoding, and measurement strategies rather than superposition alone. This review introduces radiologists to five core quantum concepts, qubits, superposition, entanglement, quantum gates, and quantum circuits, and to the superconducting, trapped-ion, and photonic hardware platforms that define the current noisy intermediate-scale quantum (NISQ) era. We then summarize the quantum machine learning architectures most often applied to medical imaging (quantum support vector machines, quantum neural networks, variational quantum circuits, and quantum convolutional neural networks) and quantum image representations such as the flexible representation of quantum images (FRQI) and the novel enhanced quantum representation (NEQR). Reported applications include brain tumor classification and segmentation on MRI, pneumonia detection on chest radiographs, skin and breast lesion classification, CT and PET reconstruction, and ultrasound denoising. Nearly all published systems are hybrid quantum-classical pipelines evaluated on small, downsampled benchmark datasets or simulators, and clinical deployment is not yet feasible. We conclude with current limitations and translational priorities for radiology.
Imaging with X-rays poses fundamental limits due to radiation damage of the highly energetic photons. This becomes problematic for sensitive biological systems such as subcellular structures. Lowering the radiation dose, without sacrificing the signal-to-noise ratio, would be desirable for any kind of imaging modalities involving X-rays. To achieve this goal, quantum imaging with entangled X-ray photons constitutes a promising route. Production of biphotons have been demonstrated in the X-ray regime by the process of Spontaneous Parametric Down-Conversion (SPDC). However, compared to SPDC in the regime of visible light, the production rate for X-ray biphotons is extremely low. With the introduction of new high average brightness X-ray sources, such as 4th generation synchrotrons and high repetition rate Free-Electron X-ray Lasers (XFEL), quantum imaging may become practical. We introduce a ray tracing approach using Monte-Carlo sampling, specifically designed for quantum imaging with entangled X-ray photons generated by SPDC. By simulation, the superior image quality of quantum over classical imaging methods is demonstrated using realistic experimental conditions available at high repetition rate XFELs. With these simulations, we can efficiently assist the design of future experiments at beam lines, which can substantially accelerate the advancement of X-ray quantum imaging and reduce costs.
The use of quantum computers in computer graphics has gained interest in recent years, especially for the application to rendering. The current state of the art in quantum rendering relies on Grover’s search for finding ray intersections in for M primitives. This quantum approach is faster than the naive approach of O(M) but slower than O(log M) of modern ray tracing with an acceleration data structure. Furthermore, this quantum ray tracing method is fundamentally limited to casting one ray at a time, leaving quantum rendering scales for the number of rays the same as non-quantum algorithms. We present a new quantum rendering method, quantum ray marching, based on the reformulation of ray marching as a quantum random walk. Our work is the first complete quantum rendering pipeline capable of light transport simulation and remains asymptotically faster than non-quantum counterparts. Our quantum ray marching can trace an exponential number of paths with polynomial cost, and it leverages quantum numerical integration to converge in O(1/N) for N estimates as opposed to non-quantum . These properties led to first quantum rendering that is asymptotically faster than non-quantum Monte Carlo rendering. We numerically tested our algorithm by rendering 2D and 3D scenes.