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.
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-enhanced probability distribution algorithms represent a significant advancement in computational modeling, particularly for complex probabilistic scenarios. This paper explores the potential of leveraging quantum mechanics to dramatically improve the accuracy and speed of Monte Carlo simulations, addressing the inherent limitations of classical approaches. The core claim is to develop a novel algorithm that utilizes quantum superposition and entanglement to efficiently explore the space of possible distributions, thereby reducing computational cost and enhancing performance. The proposed mechanism centers around exploiting quantum phenomena to overcome the computational bottlenecks encountered in traditional Monte Carlo methods, offering a paradigm shift in probabilistic modeling. This work will detail the theoretical foundations of the proposed algorithm, its implementation and testing with illustrative examples, and its potential impact on various applications.
High-fidelity dose calculation is a still computationally intensive task in radiation therapy planning. While the Monte Carlo method is considered to be most accurate, its clinical application is often hindered by long computation times. Quantum computing offers a potential pathway to accelerate these simulations. We applied Quantum Monte Carlo integration (QMCI) as a proof-of-concept for radiotherapy dose calculation. Our QMCI framework consists of two main components: State preparation using quantum generative adversarial network (qGAN) and integration by applying quantum amplitude estimation (QAE) algorithm. We calculate the depth-dose distribution for a 6 MeV photon beam by focusing on the dominant firstorder Compton scattering. Our QMCI results closely reproduce the key features of the analytical and Geant4 reference depthdose curves, validating the framework’s potential as a scalable foundation for future, clinically relevant quantum applications in radiotherapy.
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.
The efficient simulation of accurate collider data at modern collider experiments will be crucial for the detection of Beyond the Standard Model signatures in the search for new physics. Simulation of collision processes at hadron colliders like the LHC are carried out by Monte Carlo event generators which simulate the evolution from an initial hard scattering event, downwards in energy scales, towards the formation of long lived particles. However, as we enter a new era of higher luminosity experiments, accurate simulations of these pseudo-event generated data will become further computationally intensive with the vast amounts of data to generate. With the rapid development of quantum computing hardware, quantum computation offers itself as an alternative computing paradigm that may be harnessed to provide a natural framework to model several physical processes in high-energy physics, where the inherent quantum features of the device may be exploited to provide speedups or enhance current simulations. In this thesis, we present general and extendable quantum algorithms for two crucial parts of event generation: the calculation of matrix elements for the hard interaction and the QCD parton shower stage. First, a novel algorithm is proposed for the calculation of helicity amplitudes by outlining a proposal of constructing helicity spinors directly on a quantum circuit and manipulating the spinors to compute helicity amplitudes. This was used to calculate multiple helicity amplitudes for simple tree level scattering processes simultaneously as a proof-of-principle demonstration. The second algorithm outlines a proposal for a Monte Carlo-inspired parton shower algorithm which was used to simulate two shower steps of a simplified QCD model. The final algorithm extends the quantum parton shower algorithm onto a quantum walk framework which demonstrates significant scaling improvements, simulating more realistic shower depths. These algorithms utilise the quantum computers’ ability to remain in a quantum state throughout the computation and represents a first step towards a quantum computing algorithm describing full collision events at the LHC.
Quantum computing (QC) has the potential to revolutionise the future of scientific simulations. To harness the capabilities that QC offers, we can integrate it into hybrid quantum-classical simulations, which can boost the capabilities of supercomputing by leveraging quantum modules that offer speedups over classical counterparts. One example is quantum Monte Carlo integration, which is theorised to achieve a quadratic speedup over classical Monte Carlo, making it suitable for high-energy physics, strong-field QED, and multiple scientific and industrial applications. In this paper, we demonstrate that quantum Monte Carlo can be used to predict the number of pairs created when two photon beams collide head-on, a problem relevant to high-energy physics and intense laser-matter interactions. The results from the quantum simulations demonstrate high accuracy relative to theoretical predictions. The accuracy of the simulations is only constrained by the approximations required to embed polynomials and to initialise the quantum state. We also demonstrate that our algorithm can be used in current quantum hardware, providing up to 90 % accuracy relative to theoretical predictions. Furthermore, we propose pathways towards integrations with classical simulation codes.
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.