Quantum Computing and Computed Tomography: A Roadmap towards QuantumCT
Richard Schielein, Melanie Basting, Kilian Dremel, Markus Firsching, Theobald O. J. Fuchs, Jonas Graetz, Stefan Kasperl, Dimitri Prjamkov, Simon Semmler, Daniel Suth, Mareike Weule
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.
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.
Quantum computing as emerging technology raises high expectations to accelerate complicated calculations, superseding capabilities of presently available supercomputers and to provide a solution to the ever-increasing challenge of processing big amount of data. Besides theoretical promises, quantum computers have successfully been realized in experimental settings during the past few years, and can now also be accessed commercially. Given this availability of quantum computers, their application potential can be scrutinized. Promised to lead to disruptive changes in addressing and solving problems from the domains of simulation, optimization and machine learning, quantum computers have been researched in context of many application scenarios in industrial and academic setting in the recent years. However, it turns out that currently available quantum computers are still to small in size and limited in quality to fulfill the high hopes set in them at the moment. This review reflects on the current state of the technology coming from the perspective of the application, and indicates the developments required on hardware, software and algorithmic side to eventually make quantum computing beneficial for applications.
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits. Nevertheless, there are significant outstanding challenges in quantum hardware, fabrication, software architecture, and algorithms on the path towards a full-stack scalable quantum computing technology. Here, we provide a comprehensive review of these scaling challenges. We show how to facilitate scaling by adopting existing semiconductor technology to build much higher-quality qubits, employing systems engineering approaches, and performing distributed heterogeneous quantum-classical computing. We provide a detailed resource and sensitivity analysis for quantum applications on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. We provide comprehensive resource estimates for several utility-scale applications including quantum chemistry calculations, catalyst design, NMR spectroscopy, and Fermi-Hubbard simulation. We show that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration. Furthermore, we introduce high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical optimization, machine learning, and quantum simulation tasks in a cost-effective manner.
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.
As the scientific community is increasing the coverage of the quantum computing domain ranging from theoretical physics to financial applications, so does the number of quantum computing applications being developed for quantum computing hardware that is available today. As this hardware evolves towards increased capacity in terms of qubit number and increased fidelity, so does the complexity of quantum computing applications that implement the state-of-the-art of (hybrid) quantum algorithms. With this comes an increased demand for quantum software engineering practices where testing is a crucial part of the development process. Hence, a robust approach that addresses certain challenges and peculiarities related to the quantum computing domain is needed for the testing process. This paper proposes a standard-ready testing pipeline that covers the requirements of testing in a continuous development cycle as part of a modern quantum software engineering lifecycle.
Quantum Computing has inspired scientists as well as hardware and software developers and is fundamentally changing the landscape of information technology. Quantum computing systems are built from the bottom up reaching the limits of what can be classically simulated. The IBM Quantum Development Roadmap describes our vision of creating a quantum computing ecosystem delivering quantum applications through the cloud. This requires developing the entire quantum computing stack starting from the qubit and quantum processor technology, control electronics to software, algorithms and applications for quantum computing, implemented in the cloud and integrated with high performance computing. Though today's noisy near-term quantum computing systems are still limited, the roadmap targets to achieve an inflection point benefiting of continuous improvements across the stack driving quantum performance described by more qubits, higher quantum volume and speed.
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.