Previous studies have identified four potential issues related to the popularisation of quantum science and technology. These include framing quantum science and technology as spooky and enigmatic,... Show morePrevious studies have identified four potential issues related to the popularisation of quantum science and technology. These include framing quantum science and technology as spooky and enigmatic, a lack of explaining underlying quantum concepts of quantum 2.0 technology, framing quantum technology narrowly in terms of public good and having a strong focus on quantum computing. Before assessing the effect of these potential issues on public perceptions, it is important to first determine whether these issues are actually present in popular communication. To this end, we conducted a content analysis in which we investigated how quantum science and technology are framed in a corpus of 501 TEDx talks. We also examined to what extent quantum experts, such as quantum scientists and leaders at organisations in quantum science and technology, communicate about quantum science and technology differently from non-experts, such as scientists from other disciplines and artists. Results showed that: (1) about a quarter of the talks framed quantum science and technology as spooky/enigmatic; (2) about half of the talks explained at least one underlying quantum concept (superposition, entanglement or contextuality) of quantum 2.0 technology; (3) quantum technology is narrowly framed in terms of public good as we found six times more talks mentioning benefits than risks; and (4) the main focus is on quantum computing at the expense of other quantum technologies. In addition, experts and non-experts differ on three out of four issues (only the fourth issue is similar for both). Our findings thus show that these potential issues related to the popularisation of quantum science and technology are present but not predominant in TEDx talks. Further research should explore their effect on public perceptions of quantum science and technology. Show less
The Quantum Approximate Optimization Algorithm (QAOA) constitutes one of the often mentioned candidates expected to yield a quantum boost in the era of near-term quantum computing. In practice,... Show moreThe Quantum Approximate Optimization Algorithm (QAOA) constitutes one of the often mentioned candidates expected to yield a quantum boost in the era of near-term quantum computing. In practice, quantum optimization will have to compete with cheaper classical heuristic methods, which have the advantage of decades of empirical domain-specific enhancements. Consequently, to achieve optimal performance we will face the issue of algorithm selection, well-studied in practical computing. Here we introduce this problem to the quantum optimization domain.Specifically, we study the problem of detecting those problem instances of where QAOA is most likely to yield an advantage over a conventional algorithm. As our case study, we compare QAOA against the well-understood approximation algorithm of Goemans and Williamson (GW) on the Max-Cut problem. As exactly predicting the performance of algorithms can be intractable, we utilize machine learning to identify when to resort to the quantum algorithm. We achieve cross-validated accuracy well over 96\%, which would yield a substantial practical advantage. In the process, we highlight a number of features of instances rendering them better suited for QAOA. While we work with simulated idealised algorithms, the flexibility of ML methods we employed provides confidence that our methods will be equally applicable to broader classes of classical heuristics, and to QAOA running on real-world noisy devices. Show less
