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Model of a quantum processing device and a quantum programming framework

Andrey Urmanchev, Alexander Vasiliev, Danila Fetisov, Alexandr Terentyev, Farid Mansurovich Ablayev

Published Jan 1, 2017• 0 citations
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In this paper we propose a model of a programmable quantum processing device realizable with existing nano-photonic technologies. It can be viewed as a basis for new high performance hardware architectures. Protocols for physical implementation of device on the controlled photon transfer and atomic transitions are presented. These protocols are designed for executing basic single-qubit and multi-qubit gates forming a universal set. We analyze the possible operation of this quantum computer scheme. Then we formalize the physical architecture by a mathematical model of a Quantum Processing Unit (QPU), which we use as a basis for the Quantum Programming Framework. This framework makes it possible to perform universal quantum computations in a multitasking environment.
We propose a model of a programmable quantum processing device realizable with existing nanophotonic technologies and which can be viewed as a basis for new high performance hardware architectures. We present protocols and their physical implementation on the controlled photon transfer for executing basic single-qubit and multi-qubit gates. The possible operation of this quantum computer scheme is analyzed. The physical architecture is then formalized by a mathematical model of the Quantum Processing Unit (QPU), which is used as a basis for the Quantum Programming Framework that makes it possible to perform universal quantum computations in a multitasking environment.
Quantum state tomography (QST) is a crucial technique for characterizing quantum states, vital for verifying quantum device performance and implementing quantum information processing. However, traditional QST methods are notoriously computationally demanding, requiring extensive experimental data acquisition and subsequent processing, often leading to high resource consumption. This work presents a novel framework for QST utilizing probabilistic programming. The core idea is to model the quantum state as a probability distribution and employ Bayesian inference, coupled with variational methods, to efficiently infer the density matrix from limited measurement data. We demonstrate the efficacy of this approach, showcasing its potential to significantly reduce experimental costs and improve the accuracy of QST. The framework's automation and optimization capabilities represent a substantial advancement in the field, paving the way for more accessible and efficient quantum state characterization.