Context: Quantum computing is an emerging technology with the potential to address problems that are unsolvable for classical systems.However, adapting quantum software to current hardware constraints remains a significant challenge, particularly when aiming for efficient and sustainable executions.Objective: This study explores how two strategies applied during the preparation and execution stages of quantum software development can affect the results and energy consumption of the executions of a simple quantum circuit.Method: We analyse the impact of (1) the explicit selection of physical qubits within a quantum device, and (2) the number of individual executions per measurement.For each strategy, 12 executions are performed.From each execution, the outcomes of the measurements (to calculate the success rate of each case), and the execution times are collected.Results: Our analysis shows that the selection of physical qubits has a significant effect, showing a 20% drop in success rate and an increase in execution time (and therefore energy consumption) between a suboptimal qubit allocation versus a better allocation.Furthermore, the number of shots influences the quality of the outcomes, the results suggest that there might be an optimal number of shots for each circuit.Conclusions: The strategies disclosed in this study seem to affect the final outcomes and runtime required to execute our simple circuit.These strategies should be analysed for more complex circuits, and we should also check how their combination might affect the final results.
Context: Quantum computing is an emerging technology with the potential to address problems that are unsolvable for classical systems.However, adapting quantum software to current hardware constraints remains a significant challenge, particularly when aiming for efficient and sustainable executions.Objective: This study explores how two strategies applied during the preparation and execution stages of quantum software development can affect the results and energy consumption of the executions of a simple quantum circuit.Method: We analyse the impact of (1) the explicit selection of physical qubits within a quantum device, and (2) the number of individual executions per measurement.For each strategy, 12 executions are performed.From each execution, the outcomes of the measurements (to calculate the success rate of each case), and the execution times are collected.Results: Our analysis shows that the selection of physical qubits has a significant effect, showing a 20% drop in success rate and an increase in execution time (and therefore energy consumption) between a suboptimal qubit allocation versus a better allocation.Furthermore, the number of shots influences the quality of the outcomes, the results suggest that there might be an optimal number of shots for each circuit.Conclusions: The strategies disclosed in this study seem to affect the final outcomes and runtime required to execute our simple circuit.These strategies should be analysed for more complex circuits, and we should also check how their combination might affect the final results.
Quantum computing has the potential to outperform classical computing in specific problem domains, but current systems rely heavily on classical resources for tasks such as quantum circuit compilation. As quantum hardware scales, the energy consumption of these supporting classical processes becomes increasingly important. This thesis investigates the energy consumption of quantum circuit compilation by analyzing existing literature and benchmarking results for different quantum software frameworks. The study focuses on comparing the performance of widely used quantum compilers. Energy consumption is estimated based on processor power models and execution time. Benchmark data from multiplier and quantum Fourier transform circuits are used to evaluate how compiler performance scales with increasing qubit counts and different hardware connectivity constraints. The results show that no single compiler is optimal for all tasks, but significant differences exist in runtime and energy efficiency. Compilers with better scalability, such as Qiskit in many cases, tend to consume less energy for larger circuits, while others exhibit rapidly increasing costs. Additionally, restricted hardware connectivity increases compilation time and energy consumption across most frameworks. The thesis concludes that energy consumption is an important, but unexplored aspect of quantum circuit compilation. As quantum systems grow in size, the classical overhead of compilation may become a limiting factor. Future work should focus on direct measurement of energy consumption and the development of energy-aware compilation techniques.
Context: Quantum computing is rapidly evolving, offering new opportunities for solving problems in optimization, cryptography, and simulation. However, the limited availability of quantum resources makes efficient utilization of quantum hardware a current challenge. Today’s paradigms often lead to under-utilization of qubits, increased costs, and execution delays, especially in the NISQ era. Objective: This work aims to improve the utilization of quantum hardware by introducing an execution model that integrates multiprogramming at circuit level with quantum shot-wise distribution in a single policy-driven pipeline. Methods: An architecture has been implemented that combines circuit scheduling and shot distribution techniques to aggregate multiple circuits and distribute their shots across heterogeneous QPUs. The approach was empirically validated on actual IBM Quantum devices using a diverse set of reference circuits. Results: The proposal achieved a reduction in cost of 95% and a reduction in tasks 92%. Moreover, the fidelity analysis of the results showed an increase in noise, with an average increase of approximately 20% using different statistical distances. Conclusions: This research provides a usable and extensible solution to increase the efficiency, cost effectiveness, and resilience of quantum workload execution in heterogeneous and dynamic cloud environments. These results obtained suggest that users should weigh the implications of fidelity versus cost (and time) savings based on the application requirements and their goals.
Abstract Quantum computing is a promising field that can solve complex problems beyond traditional computers’ capabilities. Developing high-quality quantum software applications, called quantum software engineering, has recently gained attention. However, quantum software development faces challenges related to code quality. A recent study found that many open-source quantum programs are affected by quantum-specific code smells, with long circuit being the most common. While the study provided relevant insights into the prevalence of code smells in quantum circuits, it did not explore the potential effect of transpilation, a necessary step for executing quantum computer programs, on the emergence of code smells. Indeed, transpilation might alter those characteristics employed to detect the presence of a smell on a circuit. To address this limitation, we present a new study investigating the impact of transpilation on quantum-specific code smells and how different target gate sets affect the results. We conducted experiments on 17 open-source quantum programs alongside a set of 100 synthetic circuits. We found that transpilation can significantly alter the metrics that are used to detect code smells, even into previously smell-free circuits, with the long circuit smell being the most susceptible to transpilation. Furthermore, the choice of the gate set significantly influences the presence and severity of code smells in transpiled circuits, highlighting the need for careful gate set selection to mitigate their impact. These findings have implications for circuit optimization and high-quality quantum software development. Further research is needed to understand the consequences of code smells and their potential impact on quantum computations, considering the characteristics and constraints of different gate sets and hardware platforms.
Quantum computing is progressing at a rapid pace, though still constrained by the limitations of Noisy Intermediate-Scale Quantum (NISQ) devices, such as restricted qubit counts and high susceptibility to noise. To address these constraints, researchers have begun adapting classical software engineering principles to the quantum realm, giving rise to the field of Quantum Software Engineering. Within this context, techniques like circuit cutting and shot- wise distribution have emerged as promising approaches to divide quantum circuits into smaller fragments and to improve the scalability and reliability of quantum computations. In this paper, we present our Cut&Shoot pipeline, which supports the cutting of quantum circuits and the flexible distribution of their shots across multiple quantum processing units (QPUs), in a unified execution strategy. We compare different policies to determine the number of shots for which each fragment should be executed on each QPU. Through 107,000+ experiments on simulated NISQ devices, we assess how circuit size, shot allocation, and QPU availability affect execution times and error rates. Our findings indicate that combining circuit cutting and shot- wise distribution provides a useful trade-off of execution times and error rates, while featuring high-qubit quantum computations as well as increased resilience and adaptiveness.
Summary Computing methodologies with rapid proliferation and need for greater concurrency have evolved over the years from parallel and distributed computing to latest technologies like cloud computing. A paradigm shift in computing methodologies has been witnessed in the form of exponential speed up, optimization of problem solutions, and solvability of few classically unsolvable problems with emergence of quantum computing. The study in this article presents observations on executing basic quantum operations that play a vital role in quantum computing. The results and analysis demonstrate how parameters of time, deviation, and shots have a significant effect on outcomes of quantum circuits executed which can be a reference point for the community in general engaged in designing of quantum circuits, protocols, algorithms, and quantum hardware to leverage quantum characteristics and properties. The study by means of experimental use cases provides a rigorous review of qubit behavior, standard, and entangled qubit measurement with practical results over a simulator and real quantum hardware for variable qubit configurations using a selected platform of IBM Q Experience. With the paradigm shift from classical to quantum computing, this article provides basic but very vital observations which can be brought to use by anyone intending to build quantum applications.
The continuous growth in computational demands has led to the exploration of innovative technologies for enhancing circuit efficiency. Quantum-inspired algorithms have garnered attention due to their potential to improve optimization, processing, and energy consumption in conventional circuit technologies. Traditional circuit designs, primarily based on classical computation, face limitations in handling large-scale problems due to inefficient algorithms and hardware constraints. These challenges prompt the need for alternative solutions capable of providing scalable, high-performance computation in complex environments. This research proposes integrating quantum- inspired algorithms into circuit technologies to address these challenges. Quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, offer substantial benefits over classical counterparts, especially in solving NP-hard problems, simulating quantum systems, and optimizing complex functions. The integration of such algorithms into circuit design can potentially reduce computational complexity, improve data throughput, and optimize energy efficiency, offering a more sustainable approach in circuit development. The study investigates the adaptation of quantum-inspired algorithms into field-effect transistors (FETs) and other circuit components, focusing on optimizing power consumption and operational speed. A hybrid approach combining classical circuit elements with quantum-inspired strategies is implemented to evaluate its impact on both performance and scalability. The results show a notable reduction in energy consumption and improvement in processing speed, validating the promise of quantum-inspired solutions in enhancing computational efficiency.