Algorithm runs faster on quantum computer

Mr. K. Suresh Babu Dr. T. P. Anish Dr. Cornelius K

Jonathan I. Katz, Yehuda Lindell

So far in this book (cf. Section 3.1.2 ), we have equated the notion of “efficient adversaries” with adversarial algorithms running in (probabilistic) polynomial-time on a classical computer . Thus, when evaluating the security of our schemes we only considered efficient classical attacks. We did not, however, consider the potential impact of quantum computers —that is, computers that rely in an essential way on the principles of quantum mechanics. As we will see here, quantum algorithms can in some cases be faster than classical algorithms—possibly much faster—and thus quantum computers can have a dramatic impact on the security of cryptosystems.

Author 1 - Mr. K. Suresh Babu Author 2 : Dr. T. P. Anish Author 3 : Dr. Cornelius K

Andy Guo

The computers that exist throughout the world today, rely on two values, 0 and 1 to work and run. Quantum computers, on the other hand, uses qubits which are quantum objects that can be in either a 0 or 1 state, or even a superposition of 0 and 1 state. Quantum computers do not necessarily make traditional computer algorithms run faster, but rather lets us come up with new algorithms specifically for quantum computers that will be faster than traditional computers. Quantum computer algorithms are designed to make full use of superpositional states. These quantum computers will be able to break commonly used public key cryptosystems such as RSA, DSA, and other practical ones as well. Some physicists predict that quantum computers will become powerful enough in the next 10 years to break these existing public key cryptosystems. As a result, a new field of research in post quantum cryptography is on the rise. One of the most well known and researched post-quantum cryptography schemes are ones that revolved around lattices.

Andreas Bärtschi

Kamil Ravilevich Khadiev, Liliya Safina

In this work, we consider the performance of using a quantum algorithm to predict the result of a binary classification problem when a machine learning model is an ensemble of any simple classifiers. This approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let N be the number of classifiers from an ensemble model and O(T) be the running time of prediction of one classifier. In classical case, the final result is obtained by ”averaging” outcomes of all ensemble model’s classifiers. The running time in classical case is O (N · T). We propose an algorithm that works in O (√N · T ).