Golam Dastegir Al Quaderi

arXiv2026

Three-Qubit Quantum Energy Teleportation Protocol for Significantly High Energy Efficiency Utilizing Superconducting Qubits

Quantum Energy Teleportation (QET) exploits quantum entanglement and local operations with classical communication (LOCC) to transfer energy between distant locations without physically transporting the energy carrier. Previous demonstrations on superconducting hardware employed a two-qubit architecture and achieved a work extraction efficiency of approximately 11.7%. In this work, we propose a three-qubit QET protocol based on a novel Ising-model Hamiltonian satisfying the zero-mean-energy condition and the commutation and anti-commutation constraints required for QET. We investigate two complementary protocols. In the Single-Input Multiple-Output (SIMO) configuration, a single sender injects energy while two receivers jointly extract negative energy. Retaining all interaction terms, including the inter-receiver coupling, yields an honest work extraction efficiency of approximately 8--10%, comparable to the two-qubit implementation while distributing the extracted energy across two receivers. In the Multiple-Input Single-Output (MISO) configuration, two senders jointly inject energy through a single entangle-then-measure operation, and one receiver extracts the teleported energy. After subtracting the energy deposited directly into the receiver by the entangling operation, the net teleportation efficiency reaches 34--42%, substantially exceeding the two-qubit protocol while remaining consistent with energy conservation. These results show that extending QET to a three-qubit many-body system can significantly improve teleportation efficiency and provide a framework for studying energy transport, negative-energy distributions, and more complex quantum many-body dynamics.

quant-ph↗

arXiv2018

Predicting the Stock Price of Frontier Markets Using Modified Black-Scholes Option Pricing Model and Machine Learning

The Black-Scholes Option pricing model (BSOPM) has long been in use for valuation of equity options to find the prices of stocks. In this work, using BSOPM, we have come up with a comparative analytical approach and numerical technique to find the price of call option and put option and considered these two prices as buying price and selling price of stocks of frontier markets so that we can predict the stock price (close price). Changes have been made to the model to find the parameters strike price and the time of expiration for calculating stock price of frontier markets. To verify the result obtained using modified BSOPM we have used machine learning approach using the software Rapidminer, where we have adopted different algorithms like the decision tree, ensemble learning method and neural network. It has been observed that, the prediction of close price using machine learning is very similar to the one obtained using BSOPM. Machine learning approach stands out to be a better predictor over BSOPM, because Black-Scholes-Merton equation includes risk and dividend parameter, which changes continuously. We have also numerically calculated volatility. As the prices of the stocks goes high due to overpricing, volatility increases at a tremendous rate and when volatility becomes very high market tends to fall, which can be observed and determined using our modified BSOPM. The proposed modified BSOPM has also been explained based on the analogy of Schrodinger equation (and heat equation) of quantum physics.

q-fin.ST↗