Quantum computing forms an emerging technology with unique advantages, both in theoretical and practical considerations.For the practical aspects, several types of quantum computing devices exist today which may be general purpose or are optimized with respect to different properties, e.g., for solving optimization problems.This work explores the application of different quantum computing devices for deriving an optimal computed tomography data acquisition strategy.While solving the problem for a limited dataset size is possible on either device, the quantum annealing technology seems most promising for real-life applications, both in terms of the quality of the results as well as considering scaling issues.We use several examples to demonstrate the use of quantum computers in the planning and optimization of the data acquisition in computed tomography.
Quantum computing forms an emerging technology with unique advantages, both in theoretical and practical considerations.For the practical aspects, several types of quantum computing devices exist today which may be general purpose or are optimized with respect to different properties, e.g., for solving optimization problems.This work explores the application of different quantum computing devices for deriving an optimal computed tomography data acquisition strategy.While solving the problem for a limited dataset size is possible on either device, the quantum annealing technology seems most promising for real-life applications, both in terms of the quality of the results as well as considering scaling issues.We use several examples to demonstrate the use of quantum computers in the planning and optimization of the data acquisition in computed tomography.
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.
Quantum optimization has emerged as a promising frontier of quantum computing, providing novel numerical approaches to mathematical optimization problems. The main goal of this paper is to facilitate interdisciplinary research between the Operations Research (OR) and quantum computing communities by helping OR scientists to build initial intuition for-, and offering them a hands-on gateway to quantum-powered methods in the context of discrete optimization. To this end, we consider three quantum-powered optimization approaches that make use of different types of quantum hardware available on the market. To illustrate these approaches, we solve three classical optimization problems: the Traveling Salesperson Problem, Weighted Maximum Cut, and Maximum Independent Set. With a general OR audience in mind, we attempt to provide an intuition behind each approach along with key references, describe the corresponding high-level workflow, and highlight crucial practical considerations. In particular, we emphasize the importance of problem formulations and device-specific configurations, and their impact on the amount of resources required for computation (where we focus on the number of qubits). These points are illustrated with a series of experiments on three types of quantum computers: a neutral atom machine from QuEra , a quantum annealer from D-Wave , and gate-based devices from IBM .
The trend of building larger and more complex imaging satellite constellations leads to the challenge in managing multiple acquisition requests of the Earth surface. Optimally planning these acquisitions is an intractable optimization problem, and heuristic algorithms are used today for finding sub-optimal solutions. Recently, quantum algorithms have been considered for this purpose, due to the potential breakthroughs that they can bring in optimization, expecting either a speedup or an increase in the solution quality. Hybrid quantum-classical methods have been considered as a short-term solution for taking advantage of small quantum machines. In this paper, we propose reverse quantum annealing as a method for improving the acquisition plan obtained by a classical optimizer. We investigate the benefits of the method with different annealing schedules and different problem sizes. The obtained results provide guidelines on designing a larger hybrid quantum-classical framework based on reverse quantum annealing for this application.
Quantum computing (QC) is considered as a rising star of computing technologies with very promising possibilities towards novel solutions of even more complex computing tasks than those which are tackled using today’s supercomputers. However, direct use in everyday applications is lacking both: large scale quantum computers and quantum algorithms, i.e. software. We are carrying out the first project on a road towards QC enabled Computed Tomography (CT). In our paper we present this project after its first out of five years duration and share the first steps we made with the community. It is worth to mention that the hardware for QC is still in a phase of development, which implies that most of the software research is in a phase of becoming ready for productional use cases.
One of the primary difficulties in computed tomography (CT) is reconstructing cross-sectional images from measured projections of a physical object. There exist several classical methods for this task of generating a digital representation of the object, including filtered backprojection or simultaneous algebraic reconstruction technique. Our research aims to explore the potential of quantum computing in the field of industrial X-ray transmission tomography. Specifically, this work focuses on the application of a method similar to that proposed by Nau et al. (2023) on real CT data to demonstrate the feasibility of quadratic-unconstrained-binary-optimization-based tomographic reconstruction. Starting with simulated phantoms, results with simulated annealing as well as real annealing hardware are shown, leading to the application on measured cone-beam CT data. The results demonstrate that tomographic reconstruction using quantum annealing is feasible for both simulated and real-world applications. Yet, current limitations—involving the maximum processable size and bit depth of voxel values of the images, both correlated with the number of densely connected qubits within the annealing hardware—imply the need of future research to further improve the results. This approach, despite its early stage, has the potential to enable more sophisticated reconstructions, providing an alternative to traditional classical methods.
Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore’s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this article, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. To demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave’s quantum annealer and IBM’s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitude larger than the current quantum hardware size, and we observe results comparable to state-of-the-art solvers in terms of quality of the solution. Reproducibility : Our code and data are available at Reference [1].