The quantum challenge

Muhammad Azeem Akbar, Arif Ali Khan, Saima Rafi

Abstract Quantum computing systems harness the power of quantum mechanics to execute computationally demanding tasks more effectively than their classical counterparts. This has led to the emergence of Quantum Software Engineering (QSE), which focuses on unlocking the full potential of quantum computing systems. As QSE gains prominence, it seeks to address the evolving challenges of quantum software development by offering comprehensive concepts, principles, and guidelines. This paper aims to identify, prioritize, and develop a systematic decision-making framework of the challenging factors associated with QSE process execution. We conducted a literature survey to identify the challenging factors associated with QSE process and mapped them into 7 core categories. Additionally, we used a questionnaire survey to collect insights from practitioners regarding these challenges. To examine the relationships between core categories of challenging factors, we applied Interpretive Structure Modeling (ISM). Lastly, we applied fuzzy TOPSIS to rank the identified challenging factors concerning to their criticality for QSE process. We have identified 22 challenging factors of QSE process and mapped them to 7 core categories. The ISM results indicate that the ‘resources’ category has the most decisive influence on the other six core categories of the identified challenging factors. Moreover, the fuzzy TOPSIS indicates that ‘complex programming’, ‘limited software libraries’, ‘maintenance complexity’, ‘lack of training and workshops’, and ‘data encoding issues’ are the highest priority challenging factor for QSE process execution. Organizations using QSE could consider the identified challenging factors and their prioritization to improve their QSE process.

Jozef Gruska

Barry R. Clarke

John Chapman, Zeki Can Seskir, Sana Belguith +1 more

Quantum technologies are likely to raise significant benefits but also challenges to the continued development of smart cities. This paper looks at one of the more operationally ready of the quantum technologies, namely quantum sensing, and considers some of the opportunities and challenges facing city planners if they wish to introduce the capabilities of quantum sensing. The paper briefly outlines the evolution of quantum technologies and where quantum sensing fits in the quantum portfolio. It then considers two quantum sensing capabilities in more detail, namely the use of quantum sensing in navigation and in detection. The paper then describes the attributes of smart cities and discusses the opportunities and challenges of the introduction of these two quantum sensing capabilities, and outlines potential mitigation strategies to optimize the benefits while addressing the challenges.

Sascha Vongehr

Observed violations of Bell type inequalities exclude all relativistic micro causal ("local"), counterfactual definite ("real") hidden variable models of nature. This further relativization of our concept of reality triggers a growing pseudoscientific resistance against quantum mechanics (QM). I define Didactic Randi Challenges (DRC) via five characteristics. These are challenges which, according to the laws of nature, are impossible to meet. They effectively refute pseudoscientific claims according to which the challenge could easily be met. DRC work by being known to exist while never having been overcome, despite the large rewards which would follow from meeting the challenge. Pseudoscience exploits well meaning engagement in argument to create the appearance of an expert dispute (sowing doubt). DRC decline to discuss "until the challenge is met", without solidifying the perception of establishment conspiracy. This requires transparency, thus DRC are efficient didactic tools. The Quantum Randi Challenge (QRC) is a DRC designed to reject hidden variable models by simply teaching QM; there is no bet or interaction with challengers. The QRC is a computer game that anybody can modify. The present version includes a simulation of true QM behavior that violates Bell 99% of the time, hidden variables that violate the Bell and CHSH inequality with 50% probability, and ones which violate Bell 85% of the time when missing 13% anti-correlation. The DRC challenge is to modify the hidden variables so that the predicted QM behavior arises, including anti-correlation. If such were possible, the presented programs would make it trivial to meet the challenge. This fact and the whole QRC can be taught to a wide audience via the presented heuristics. Demanding anti-correlation is argued to be superior to employing CHSH.