Quantum Computing

Quantum Computing Workshop

Analysis of Quantum Algorithm for Decision Tree Construction Implementation

Liliya Safina, Ilnaz Mannapov, Kamil Khadiev, Analysis of Quantum Algorithm for Decision Tree Construction Implementation

The Classical and Quantum Improvements of Decision Tree Constructing Algorithms C5.0 and CART

The Classical and Quantum Improvements of Decision Tree Constructing Algorithms C5.0 and CART

Liliia Safina, Ilnaz Mannapov and Kamil Khadiev. The Classical and Quantum Improvements of Decision Tree Constructing Algorithms C5.0 and CART

Classical and Quantum Improvements of Generic Decision Tree Constructing Algorithm for Classification Problem

Classical and Quantum Improvements of Generic Decision Tree Constructing Algorithm for Classification Problem

Ilnaz Mannapov, Kamil Khadiev and Liliya Safina. Classical and Quantum Improvements of Generic Decision Tree Constructing Algorithm for Classification Problem

Classical and Quantum Improvements of Generic Decision Tree Constructing Algorithm for Classification Problem

Ilnaz Mannapov, Liliya Safina and Kamil Khadiev, Classical and Quantum Improvements of Generic Decision Tree Constructing Algorithm for Classification Problem

The Quantum Version Of Classification Decision Tree Constructing Algorithm C5.0

In the paper, we focus on complexity of C5.0 algorithm for constructing decision tree classifier that is the models for the classification problem from machine learning. In classical case the decision tree is constructed in O(hd(NM+N log N)) running time, where M is a number of classes, N is the

The Quantum Version Of Classification Decision Tree Mannapov I., Safina L., Khadiev K., Constructing Algorithm C5.0

The Quantum Version Of Classification Decision Tree Constructing Algorithms

We consider decision tree as a model for the classification problem from machine learning. We focus on complexity of decision tree constructing algorithms as C5.0, CART. In classical case the decision tree is constructed in O(hd(NM+NlogN)) running time, where M is a number of classes, N is the size

Lilia Safina, Ilnaz Mannapov, Kamil Khadiev "The Quantum Version Of Classification Decision Tree Constructing Algorithms"

Quantum Online Algorithms with Advice Bits and Restricted Memory

In this paper, we consider online algorithms. Typically the model is investigated with respect to competitive ratio. We consider algorithms with restricted memory (space) and explore their power. We focus on quantum and classical online algorithms with advice. We consider special problem $P_k$. This

Kamil Khadiev, Ilnaz Mannapov and Mansur Ziatdinov "Quantum Online Algorithms with Advice Bits and Restricted Memory"

Quantum Online Streaming Algorithms with Advice

A streaming algorithm is a well-known computational model that has a relationship with automata and branching programs models. We consider a quantum version of streaming algorithms for solving online minimization problems. We call the model quantum online streaming algorithm. Our Results: -a quant

"Quantum Online Streaming Algorithms with Advice"

Quantum online algorithms with restricted memory

An online algorithm is a well-known computational model for solving optimization problems. The defining property of this model is following. An algorithm reads an input piece by piece and should return output variables after some of the input variables immediately, even if the answer depends on the

Quantum online algorithms with restricted memory

On Quantum Online Algorithms with Restricted Memory and Advice

Quantum Online Algorithm with Shared Advice Bits

Mannapov I., Khadiev K. "Quantum Online Algorithm with Shared Advice Bits"

Quantum Streaming Algorithms for Online Minimization Problems and Quantum Online Algorithms with Advice

A streaming algorithm is a well-known computational model that has a relationship with automata and branching programs models. We consider a quantum version of streaming algorithms for solving online minimization problems (OMP). It is the first time when quantum streaming algorithms are explored for

Quantum Streaming Algorithms for Online Minimization Problems and Quantum Online Algorithms with Advice

Quantum versus Classical Online Algorithms with Advice and Restricted Memory

In this paper, we consider online algorithms. Typically the model is investigated with respect to competitive ratio. We consider algorithms with restricted memory (space) and explore their power. We focus on quantum and classical online algorithms. We show that there are problems that can be better

Kamil Khadiev, Aliya Khadieva, Dmitry Kravchenko, Alexander Rivosh and Ilnaz Mannapov, "Quantum versus Classical Online Algorithms with Advice and Restricted Memory"