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Saba Arife Bozpolat

Publications and source records attributed to Saba Arife Bozpolat.

2 recordsLinked to original sources

General Quantum Protocol for Communication via Maximally Entangled $n$-Qubit States

This study presents a generalized n--bit superdense coding protocol that enables the transmission of n classical bits of information using an entangled n--qubit quantum system and the transmission of $n-1$ qubits. The protocol involves creating a maximally entangled n--qubit state, encoding the classical message with Pauli--Z and Pauli--X gates, and then transmitting and decoding the message via quantum communication, quantum operations, and measurements. The key novelty of this work is an explicit, scalable n--bit encoding routine, realized on the GHZ channel with a low--depth circuit using only single--qubit Pauli--X and Pauli--Z operations. The protocol was tested on real quantum hardware using Qiskit 2.0 and the IBM--Fez quantum computer for message lengths of 4, 6, 8, and 10 bits. Results show that success rates decrease as message length, circuit depth, and gate count increase, largely due to increased Pauli--X gate usage for messages with higher Hamming weight. Strategies to improve performance include sending messages in shorter segments and advances in qubit coherence and gate fidelity. This work offers a practical and easily scalable quantum communication protocol with potential applications in quantum networks and communication systems.

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Searching for Possible Spin Configurations of Ferrum Chain via Quantum Approximate Optimization Algorithm

Calculating the expected spin configuration of the chain consisting of Ferrum atoms interacting with each other through exchange interaction is fundamentally a configuration optimization problem. Quantum Approximate Optimization Algorithm is a suitable candidate to configure such systems on a quantum device. In this work we have considered Ferrum chains of three different lengths and calculated their most-probable spin configurations using Quantum Approximate Optimization Algorithm. We employed a Quantum Feed Forward Neural Network as the optimizer of Quantum Approximate Optimization Algorithm. We have successfully obtained the expected spin configuration for the longest Ferrum Chain.

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