Quantum computers promise considerable speedups over classical approaches, which has raised interest from many disciplines. Since any currently available implementations suffer from noise and imperfections, achieving concrete speedups for meaningful problem sizes remains a major challenge. Yet, imperfections and noise may remain present in quantum computing for a long while. Such limitations play no role in classical software computing, and software engineers are typically not well accustomed to considering such imperfections, albeit they substantially influence core properties of software and systems.In this paper, we show how to model imperfections with an approach tailored to (quantum) software engineers. We intuitively illustrate, using numerical simulations, how imperfections influence core properties of quantum algorithms on NISQ systems, and show possible options for tailoring future NISQ machines to improve system performance in a co-design approach.Our results are obtained from a software framework that we provide in form of an easy-to-use reproduction package. It does not require computer scientists to acquire deep physical knowledge on noise, yet provide tangible and intuitively accessible means of interpreting the influence of noise on common software quality and performance indicators.
Quantum computers promise considerable speedups over classical approaches, which has raised interest from many disciplines. Since any currently available implementations suffer from noise and imperfections, achieving concrete speedups for meaningful problem sizes remains a major challenge. Yet, imperfections and noise may remain present in quantum computing for a long while. Such limitations play no role in classical software computing, and software engineers are typically not well accustomed to considering such imperfections, albeit they substantially influence core properties of software and systems.In this paper, we show how to model imperfections with an approach tailored to (quantum) software engineers. We intuitively illustrate, using numerical simulations, how imperfections influence core properties of quantum algorithms on NISQ systems, and show possible options for tailoring future NISQ machines to improve system performance in a co-design approach.Our results are obtained from a software framework that we provide in form of an easy-to-use reproduction package. It does not require computer scientists to acquire deep physical knowledge on noise, yet provide tangible and intuitively accessible means of interpreting the influence of noise on common software quality and performance indicators.
Noise and imperfections are among the prevalent challenges in quantum software engineering for current NISQ systems. They will remain important in the post-NISQ area, as logical, error-corrected qubits will be based on software mechanisms. As real quantum hardware is still limited in size and accessibility, noise models for classical simulation-that in some cases can exceed dimensions of actual systems-play a critical role in obtaining insights into quantum algorithm performance, and the properties of mechanisms for error correction and mitigation. We present, implement and validate a tunable noise model building on the Kraus channel formalism on a large scale quantum simulator system (Qaptiva). We use empirical noise measurements from IBM quantum (IBMQ) systems to calibrate the model and create a realistic simulation environment. Experimental evaluation of our approach with Greenberger-Horne-Zeilinger (GHZ) state preparation and QAOA applied to an industrial usecase validate our approach, and demonstrate accurate simulation of hardware behaviour at reasonable computational cost. We devise and utilise a method that allows for determining the quality of noise models for larger problem instances than is possible with existing metrics in the literature. To identify potentials of future quantum software and algorithms, we extrapolate the noise model to future partially fault-tolerant systems, and give insights into the interplay between hardware-specific noise modelling and hardware-aware algorithm development.
The quantum approximate optimisation algorithm (QAOA) and its variants are at the core of many scenarios that aim at combining the power of quantum computers (QC) and classical high-performance computing (HPC) appliances for combinatorial optimisation. Several obstacles challenge concrete benefits now and in the foreseeable future: Imperfections quickly degrade algorithmic performance below practical utility; over-heads arising from alternating between classical and quantum primitives can counter any advantage; and the choice of parameters or algorithmic variant can substantially influence runtime and result quality. Selecting the appropriate combination is a non-trivial issue, as it not only depends on end-user requirements, but also on details of the hardware and software stack. Appropriate automation can alleviate the burden of choosing optimal combinations for end-users: They should not be required to understand technicalities like detail differences between QAOA variants, required number of QAOA layers, or necessary measurement samples. Yet, they should receive the best possible satisfaction of their non-functional requirements, be it performance or other. We determine factors that influence approximation quality and temporal behaviour of four QAOA variants using comprehensive density-matrix-based numerical simulations targeting three widely studied optimisation problems. Our simulations consider ideal quantum computation, and a continuum of scenarios troubled by realistic imperfections. Our quantitative results, accompanied by a comprehensive reproduction package, show strong performance differences between QAOA variants that can be pinpointed to narrow and specific effects. We identify influential co-variables and relevant non-functional quality goals that, as we argue, mark the rele-vant ingredients for designing appropriate software engineering abstraction mechanisms and automated tool-chains for devising quantum solutions from higher-level problem specifications.
Despite rapid progress in quantum computing in the last decade, the limited usability of quantum computers remains a major roadblock toward its wider adoption. Current noisy intermediate-scale quantum (NISQ) computers produce highly erroneous program outputs for quantum-advantage-proven algorithms - that is, algorithms that are infeasible or orders of magnitude slower on classical supercomputing and high-performance computing (HPC) clusters. Unfortunately, currently, quantum computing programmers lack robust system software tools and methods to make meaningful use of erroneous program executions on quantum computers. This lack of capability is the core motivation behind the fundamental question this dissertation poses: "Can we build system software tools for programmers to make the quantum program execution and output meaningful on NISQ machines?" This dissertation answers this question in the affirmative - experimentally demonstrating on real-system quantum computers that it is possible to extract near-accurate program output from noisy executions on today's erroneous quantum computers, ironically using classical HPC resources and knowledge. This dissertation demonstrates how to achieve this goal without requiring user intervention, domain knowledge about quantum algorithms, or additional quantum hardware support. Unfortunately, as this dissertation uncovers, progressing toward making quantum computers usable is a double-edged sword. In the near future, only a few entities in the world may have access to powerful quantum computers, and these quantum computers will be used to solve previously-unsolved large-scale optimization problems, possibly without an explicit trust model between the service provider and the customer. Therefore, this dissertation envisions that the solutions to such large-scale optimization problems will be considered sensitive and will need to be protected. This dissertation takes the first few steps toward preparing us for that future - by developing a novel method that intelligently obfuscates near-accurate program output and quantum circuit structure to preserve a customer's privacy under a specified computation model and resource availability. The approaches introduced in this dissertation open up new research avenues for hybrid quantum-classical computing. This dissertation also aims to lower the barrier to entry for quantum computing research for the experimental computer systems and HPC community by open-sourcing multiple novel datasets and software frameworks implemented for real-system quantum computers.--Author's abstract
Abstract Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of Python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the QC software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of key features including a flexible optimization toolkit, engineering-inspired filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.
Quantum computing is rapidly emerging as a transformative force poised to redefine the foundations of software engineering. By leveraging quantum mechanical phenomena such as superposition, entanglement, and interference, quantum computers offer the potential to solve complex problems far beyond the reach of classical systems. This paper explores the multifaceted impact of quantum computing on software engineering, focusing on key areas including quantum algorithms, cryptography, programming paradigms, and application domains. It examines how quantum algorithms like Shor's and Grover's promise significant computational speedups, threatening classical encryption schemes and prompting the rise of post-quantum cryptography. The paper also analyzes the challenges of quantum programming, including steep learning curves, limited hardware access, and the need for new development tools and testing frameworks. These challenges highlight the current limitations of quantum technology, such as hardware instability and scalability issues. As quantum hardware evolves, the integration of hybrid quantum-classical systems is identified as a practical approach for near-term applications in areas such as drug discovery, finance, and optimization. The paper further discusses ethical considerations, such as equitable access to quantum resources and privacy concerns, alongside potential societal impacts to ensure responsible innovation. It underscores the need for future advancements in education, standardization, and hybrid systems to bridge the gap between current limitations and quantum computing’s potential. The paper concludes by emphasizing the importance of educational reform, standardization, and ethical considerations in preparing the software engineering community for a quantum-driven future.
Quantum computers represent a radical technological breakthrough in information processing by leveraging the principles of quantum mechanics to solve highly complex problems beyond the reach of classical systems. However, in the current NISQ era (noisy intermediate-scale quantum devices), the available hardware presents several limitations, such as a limited number of qubits, high error rates, and short coherence times. Efficient management of quantum resources, both physical and logical, is especially relevant in the design and deployment of quantum algorithms. In this paper, we analyze the role of resources in current uses of NISQ devices, identifying their relevance and implications for quantum software engineering. With this contribution, we aim to strengthen the field of Quantum Resource Estimation (QRE) and move toward scalable and reliable quantum software development