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Md Shoyib Hassan

Publications and source records attributed to Md Shoyib Hassan.

2 recordsLinked to original sources

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↗

Robotic Arm Manipulation with Inverse Reinforcement Learning & TD-MPC

One unresolved issue is how to scale model-based inverse reinforcement learning (IRL) to actual robotic manipulation tasks with unpredictable dynamics. The ability to learn from both visual and proprioceptive examples, creating algorithms that scale to high-dimensional state-spaces, and mastering strong dynamics models are the main obstacles. In this work, we provide a gradient-based inverse reinforcement learning framework that learns cost functions purely from visual human demonstrations. The shown behavior and the trajectory is then optimized using TD visual model predictive control(MPC) and the learned cost functions. We test our system using fundamental object manipulation tasks on hardware.

cs.RO↗