A quantum mechanical prototype

Александра Владимировна Власенко, Michael Evsyukov, M. Putyato +1 more

This is the final part of the series of articles devoted to the development of a decision support system for choosing the optimal post-quantum key encapsulation mechanism. Efficiency of the methodology proposed for choosing the optimal implementation of the post-quantum cryptographic algorithm is evaluated and substantiated. The economic feasibility of approach is shown and its positive impact on the quality of the cryptographic information protection subsystem is demonstrated. Efficiency comparison of the prototype of the decision support system with existing software products aimed at supporting decision-making in the field of information security is carried out. The consistency of prototype’s recommendations with the conclusions of analytical studies in the field of post-quantum cryptography is checked.

Alexander Engelsberger, M. Psenickova, Thomas Villmann

The superposition principle in quantum mechanics enables the encoding of an entire solution space within a single quantum state. By employing quantum routines such as amplitude amplification or the Quantum Approximate Optimization Algorithm (QAOA), this solution space can be explored in a computationally efficient manner to identify optimal or near-optimal solutions. In this article, we propose quantum circuits that operate on binary data representations to address a central task in prototype-based classification and representation learning, namely the so-called winner determination, which realizes the nearest prototype principle. We investigate quantum search algorithms to identify the closest prototype during prediction, as well as quantum optimization schemes for prototype selection in the training phase. For these algorithms, we design oracles based on arithmetic circuits that leverage quantum parallelism to apply mathematical operations simultaneously to multiple inputs. Furthermore, we introduce an oracle for prototype selection, integrated into a learning routine, which obviates the need for formulating the task as a binary optimization problem and thereby reduces the number of required auxiliary variables. All proposed oracles are implemented using the Python 3-based quantum machine learning framework PennyLane and empirically validated on synthetic benchmark datasets.

David Luong, Bhashyam Balaji

Recent experimental results have demonstrated gains in sensing capability using novel possibilities offered by quantum mechanics. In particular, a prototype radar which uses quantum techniques to enhance detection ability has been built in a laboratory, showing that quantum radars at RF frequencies are feasible. This prototype is called a quantum two-mode squeezing radar (QTMS radar). In this paper, we use the QTMS radar as a springboard to review the concept of quantum squeezing. We find that Josephson parametric amplifiers (JPAs), one of which was used in the QTMS radar prototype, can be employed to produce two-mode and one-mode squeezed states at RF frequencies. We then briefly discuss some of the possible applications of such states to the field of radar engineering.

Fabien Gatti, Benjamin Lasorne, Hans‐Dieter Meyer +1 more