Quantum Computing: Rethinking Computation Beyond Classical Limits

Aida Todri‐Sanial

Devki Nandan Jha, Jonte R. Hance, Tomasz Szydło +3 more

Quantum computing represents a fundamental shift in computation, promising to solve problems currently intractable for classical computers. However, misconceptions persist; quantum computers are not universally faster, nor do they replace classical systems. Current Noisy Intermediate-Scale Quantum (NISQ) devices face significant challenges, including decoherence, high error rates, and limited scalability. Additionally, leveraging quantum systems requires rethinking traditional computing principles, adopting different programming models, and addressing the complexities of integrating quantum and classical workflows, making their practical use in computer science both demanding and non-trivial. This article provides a system-level perspective on quantum computing for computer scientists, software engineers, and IT professionals, examining its current capabilities, key architectural and software challenges, and potential paths toward quantum advantage in real-world applications within hybrid quantum-classical environments.

Jonas Jäger, Philipp Elsässer, Elham Torabian

Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum feature maps, which embed classical data into the state space of qubits. We introduce Quantum Feature-Map Learning via Analytic Iterative Reconstructions (Q-FLAIR), an algorithm that reduces quantum resource overhead in iterative feature-map circuit construction. It shifts workloads to a classical computer via partial analytic reconstructions of the quantum model, using only a few evaluations. For each probed gate addition to the ansatz, the simultaneous selection and optimization of the data feature and weight parameter is then entirely classical. Integrated into quantum neural network and quantum kernel support vector classifiers, Q-FLAIR shows state-of-the-art benchmark performance. Since resource overhead decouples from feature dimension, we train a quantum model on a real IBM device in only 4 h, surpassing 90 % accuracy on the full-resolution MNIST dataset (784 features, digits 3 versus 5). Such results were previously unattainable, as the feature dimension prohibitively drives hardware demands for fixed and search costs for adaptive ansätze. Furthermore, Q-FLAIR demonstrates dequantization robustness against direct classical modeling, satisfying a benchmark rare in the literature and a necessary condition for potential quantum advantage. By rethinking feature-map learning beyond black-box optimization, this work takes a concrete step toward enabling quantum machine learning for real-world problems and near-term quantum computers.

Andreas H. Trabesinger

Dumitru Moldovan

Abstract Quantum physics requires a rethinking of basic postulates of economic science. The article presents the fundamental ideas of the hypothesis theory “The economy of creative imagination”. Also, it identifies the reevaluation methods of mechanistic economic paradigms that were formulated through analogy with classical mechanics. The paper shows that the economic process is performed at two levels - first in individual imagination, and then in traditional workshops and factories. In individual’s imagination, real goods are “truly created” at the subatomic level. As determinism principle dominates big material objects and the uncertainty principle operates at the quantum level, the author believes that rethinking the economic science refers primarily to the discovery of laws that enable us to predict the evolution of economic phenomena within the limitations imposed by the uncertainty principle.

Craig S. Lent