Quantum Humanities: A First Use Case for Quantum-ML in Media Science

Johanna Barzen

James Der Derian, Alexander E. Wendt

Undertaking a collaborative inquiry into a convergence of quantum theory, science, and technology, the chapter makes the case for a new human science for International Relations (IR). As new scientific discoveries and technological applications suggest large-scale quantum phenomena, near-simultaneous interconnectivity creates global entanglements, and ubiquitous media produce profound observer effects of uncertainty, IR theory needs to “quantize” if it is to retain relevancy in the twenty-first century. The central concepts of quantum mechanics that inform a quantized IR are introduced, and the heuristic advantages of quantum over traditional approaches are presented.

Johanna Barzen, Frank Leymann

Richard Y. Li, Sharvari Gujja, Sweta R. Bajaj +5 more

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired by recent advances in physical quantum processors, we evaluated several unconventional machine-learning (ML) strategies on actual human tumor data, namely "Ising-type" methods, whose objective function is formulated identical to simulated annealing and quantum annealing. We show the efficacy of multiple Ising-type ML algorithms for classification of multi-omics human cancer data from The Cancer Genome Atlas, comparing these classifiers to a variety of standard ML methods. Our results indicate that Ising-type ML offers superior classification performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences.

Francesco Galofaro, Zeno Toffano

Since 2014 the authors have been developing an algorithm which exports to the field of information retrieval some concepts and models from quantum theory: entanglement and correlation. In this article we explore the linguistic and semiotic implications of a quantum-like semantic theory, reconstructing the relationships between our work and a wide field of study borrowing statistical instruments from quantum theory and applying them to humanities and social sciences. Finally, as an example, we will show how our algorithm works on a case study: religious discourse in new media. In particular, the application of quantum instruments is helpful to understand how the quotation of a Twitter message in the reply modifies its original semantic profile.