Quantum Information and Quantum Signal Processing

Akindemowo Olayiwola Ayorinde

Aim: To examine the strengthening of cyber defense using Quantum Signal Processing. Significance of Study: There is need for the application of Quantum Signal Processing in cyber security due to the vulnerable attack. This paper addresses the relevant issues on the use of Quantum Signal Processing in strengthening cyber defense. Problem Statement: The development of cyber space and its inestimable contribution in information dissemination has led to its wide patronage and usage. Thus, third party are intruding to hijack and hack secretive information to suite their own purpose. Discussion: The concepts of Quantum Signal Processing (QSP) and cyber defense were examined. It was revealed that the measurement of Quantum Signal Processing is strongly involved in signal processing of algorithms design with significance to QSP framework such that in this framework, the processing of signals occur via the utilization of some Quantum Signal Processing measurement variables or subjecting them to a Quantum Signal Processing measurement. Also, the QSP framework is primarily assigned with the quantum physics constraints, QSP measurement design, a quantum measurement, borrowing from the principles, and axioms. Nonetheless, the measurement consistency principle, measurement concept and quantization of the measurement output principle were identified as the three interconnected fundamental principles of quantum mechanics in Quantum Signal Processing. The existing correlation between QSP framework and quantum physics together with relevant key results were also examined. was also discussed with major Considerations were given to modification of known algorithms, measurement parameters utilization, probabilistic mappings, imposition of inner product constraints and oblique projections as the algorithm design in quantum signal processing framework. Conclusion: In conclusion, Quantum Signal Processing is applicable in strengthening cyber defense.

康弘 都倉

Joseph M. Landsberg

Yuan Liu, John M. Martyn, Jasmine Sinanan-Singh +3 more

Signal processing stands as a pillar of classical computation and modern information technology, applicable to both analog and digital signals. Recently, advancements in quantum information science have suggested that quantum signal processing (QSP) can enable more powerful signal processing capabilities. However, the developments in QSP have primarily leverageddigitalquantum resources, such as discrete-variable (DV) systems like qubits, rather thananalogquantum resources, such as continuous-variable (CV) systems like quantum oscillators. Consequently, there remains a gap in understanding how signal processing can be performed on hybrid CV-DV quantum computers. Here we address this gap by developing a new paradigm of mixed analog-digital QSP. We demonstrate the utility of this paradigm by showcasing how it naturally enables analog-digital conversion of quantum signals— specifically, the transfer of states between DV and CV quantum systems. We then show that such quantum analog-digital conversion enables new implementations of quantum algorithms on CV-DV hardware. This is exemplified by realizing the quantum Fourier transform of a state encoded on qubits via the free-evolution of a quantum oscillator, albeit with a runtime exponential in the number of qubits due to information theoretic arguments. Collectively, this work marks a significant step forward in hybrid CV-DV quantum computation, providing a foundation for scalable analog-digital signal processing on quantum processors.

gerarano utilisateurs

A. Karthik, V Asha, Ginni Nijhawan +3 more

Quantum Signal Processing (QSP) emerges as a groundbreaking paradigm, exploiting the principles of quantum mechanics to revolutionize the analysis, manipulation, and interpretation of signals. This paper introduces a novel framework for QSP, delineating its theoretical foundations and potential to surpass classical signal processing capabilities. The research delves into the development of quantum algorithms that exhibit superior efficiency in the frequency domain analysis and the processing of temporally entangled signal structures. A pivotal aspect of this work is the introduction of quantum Fourier transforms as a mechanism to achieve exponential speed-ups in signal decomposition. Furthermore, the paper explores the implementation of quantum error correction techniques to enhance the robustness of signal processing in the presence of quantum noise and decoherence. The practicality of QSP is demonstrated through simulated quantum circuits, providing a blueprint for future quantum computing hardware applications. The implications of this research are profound, suggesting a transformative impact on fields ranging from secure communications to biomedical imaging. By harnessing the entanglement and superposition properties inherent to quantum systems, QSP is poised to redefine the limits of what is computationally feasible within information processing.