Parameterized Quantum Circuits for Learning Cooperative Quantum Teleportation. Issue 9 (10th July 2022)

• Record Type: Journal Article
• Authors: Feng, Yan‐Y.; Zhou, Jian; Zhang, Dan‐B.; Shi, Jin‐J.
• Is Part Of: Advanced quantum technologies. Volume 5:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-10
• Subjects: cooperative quantum teleportation -- hybrid quantum‐classical algorithms -- parameterized quantum circuits -- quantum communication; Quantum theory -- Periodicals; Quantum computing -- Periodicals; Quantum chemistry -- Periodicals; Quantum electronics -- Periodicals; 537.5
• DOI: 10.1002/qute.202200040
• Languages: English
• ISSNs: 2511-9044

Abstract

Abstract: Quantum teleportation is an elemental process in quantum communication and many variants have been widely investigated theoretically and experimentally. Motivated by cooperation in classical communications, cooperative quantum teleportation (CQT) are developed with features of the cooperation referring to allocation of resources and operations among participants. Parameterized quantum circuit (PQC) are employed to learn the CQT protocols on account of different training scenarios with controls of gate parameters among shared entangled states, measurement, and recovery operations. Numerical results show the CQT protocols can be discovered with unit fidelity regardless of ideal or nonideal channels by training the PQCs with hybrid quantum‐classical (HQC) optimization under full cooperations of the participants. Further, the influence of quantum channel noise on the teleportation performance is explored by parameterizing the entangled states with noise and entanglement parameters under adjustable recovery operations. Simulation results indicate the trained PQC can improve the system performance, which implies the potential denoising capability of the HQC algorithms. The suggested CQT protocols satisfy the underlying properties of universality, randomness, locality and cooperation, and discussions indicate the designed quantum circuits can be optimized in view of quantum and circuit costs by appropriate optimization algorithms for achieving higher state fidelity in the future work. Abstract : Enabling quantum computers with machine learning, parameterized quantum circuits (PQCs) are employed to learn the cooperative quantum teleportation protocols on account of different training scenarios with controls of gate parameters among shared entangled states, measurement operations, and recovery operations in hybrid quantum‐classical systems. Additionally, the denoising ability of the trained PQCs are observed under noisy quantum channel.