Computational Techniques

Aitor Gómez-Goiri, Iñaki Seco-Aguirre, Óscar Lage +1 more

Privacy-Preserving Computation (PPC) comprises the techniques, schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis. Most of the current PPC techniques rely on the complexity of cryptographic operations, which are expected to be efficiently solved by quantum computers soon. This review explores how PPC can be built on top of quantum computing itself to alleviate these future threats. We analyze quantum proposals for Secure Multi-party Computation, Oblivious Transfer and Homomorphic Encryption from the last decade focusing on their maturity and the challenges they currently face. Our findings show a strong focus on purely theoretical works, but a rise on the experimental consideration of these techniques in the last 5 years. The applicability of these techniques to actual use cases is an underexplored aspect which could lead to the practical assessment of these techniques.

Malte Henkel

T N Manjunath, Adarsh Hiremath, Mudligiriyappa Niranjanamurthy +3 more

Over the course of the last seven decades the world of computing has taken revolutionary reformations in the scope of classical computing. Classical computers manipulate ones and zeroes to crunch through operations given to them by users. An innovative strategy known as quantum computing leverages the concepts of quantum physics to solve issues that are too complex for conventional computers to handle. Two of the domains of science that are now progressing the fastest are theoretical machine learning and quantum computing theory. In recent years, researchers have begun examining how classic machine learning techniques may be improved by quantum computing. Hybrid techniques that integrate conventional and quantum algorithms are part of quantum machine learning. Instead of using standard data, quantum techniques may be utilized to examine quantum states. However, quantum algorithms have the potential to greatly expand current data science techniques. In this paper we review the contribution carried out by various researchers in the field of Quantum Machine Learning and later we look at certain techniques associated with it for its use.

Setu Kumar Chaturvedi

In present scenario where various diseases are here and out of them the cancer is one of them most devastating disease. Presently the detection procedure is very much time consuming and the results obtain by them are not so fast, so here in this respect there is a need of more accurate, fast and efficient method through computing technologies. This research work incorporates Quantum computing with the clustering algorithm i.e. Shor Algorithm of Quantum Computing with various Hierarchical Clustering Technique. The Hierarchical Clustering Technique helps in clustering of results to obtain an approach for Cancer Disease Detection. Now accurate results are obtained, when adopting Shor Algorithm of quantum computing in disease detection technique with different Hierarchical Clustering. Here the Shor Algorithm helps to increase the efficiency in term of accuracy and Hierarchical Clustering Technique helps to detect the stages of Cancer. So, the proposed system gives a new approach in the field of cancer disease diagnosis. The results obtained by different Hierarchical Clustering Techniques are discussed.

Masanori Ohya, Igor Vasil'evich Volovich