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arXiv:2203.07816 · Journal of Physics A Mathematical and Theoretical

The best approximation of a given qubit state with the limited pure-state set

Li‐qiang Zhang, Deng-hui Yu, Chang‐shui Yu

Published Jan 18, 2021• 2 citations
arXivHTMLPDF

Abstract

Abstract The preparation of quantum states lies at the foundation in the quantum information processing. The convex mixing of some existing quantum states is one of the effective candidate. In this paper, we mainly study how a target quantum state can be optimally prepared by not more than three given pure states. The analytic optimal distance based on the fidelity is found. We also show that the preparation with more than four states can be essentially converted to the case with not more than four states, which can be similarly solved as the case with three states. The validity is illustrated by the comparison of our analytical and numerical results.

DOI
10.1088/1751-8121/abdcd0

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Abstract The preparation of quantum states lies at the foundation in the quantum information processing. The convex mixing of some existing quantum states is one of the effective candidate. In this paper, we mainly study how a target quantum state can be optimally prepared by not more than three given pure states. The analytic optimal distance based on the fidelity is found. We also show that the preparation with more than four states can be essentially converted to the case with not more than four states, which can be similarly solved as the case with three states. The validity is illustrated by the comparison of our analytical and numerical results.
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Abstract We investigate the optimal convex approximation, optimally approximating a desired and unavailable qubit state by the convex mixing of a given set of available states. When the available states are the eigenvectors of three Pauli matrices, we present the complete exact solution for the optimal convex approximation of an arbitrary qubit state based on the fidelity distance. By the comparison of optimal states based on fidelity and trace norm, the advantages and disadvantages of the optimal convex approximation are identified. Several examples are provided to support this. We also analyze the geometrical properties of the target states which can be completely represented by a set of available states. Using the feature of convex combination, we derive the maximum range of completely represented states and clearly illustrate that any qubit state can be optimally prepared by at most three available states in known useable states.
In order to execute quantum algorithms on bosonic quantum computers, it is necessary to encode their inputs into a desired target quantum state, with a process called state preparation. In real settings, state preparation methods operate in stochastic scenarios, where the prepared state is represented as a mixed state, that is a probabilistic ensemble of pure states. Therefore, existing state preparation methods optimize a variational ansatz by averaging over all possible sequences of photon loss events contained in the prepared ensemble, yielding an output state which approximates the desired target with a certain average fidelity. Such an approach, however, does not take into account directly the distribution of fidelities across states in the ensemble. In this work, we aim to maximize the fidelity of only a subset of pure states sampled from the prepared ensemble, at the cost of reducing the average fidelity overall. This trade-off is relevant when the goal is to obtain high-fidelity states with a certain probability, rather than maximizing the average fidelity of the prepared ensemble. We borrow and adapt objective functions from risk-sensitive optimization, a framework that optimizes specific characteristics of a distribution rather than only its expectation, and that has been successfully applied in Reinforcement Learning. In the considered experimental settings, our results show that the infidelities of some pure states in the prepared ensemble can be reduced by up to four orders of magnitude when using risk-seeking objectives, compared to those achieved using the standard optimization of the average fidelity. Furthermore, we show that it is possible to control the trade-off between the probability of obtaining high-fidelity states and the lowest infidelity threshold reached, providing the flexibility to select the operating point that best suits a certain application.
Abstract Utilizing the l 1 norm as the quantum state distance metric, we consider the Pauli distance of arbitrary target quantum states, that is, the optimal convex approximation of any qubit mixed states relative to the available state of pairs of eigenstates in three Pauli matrices. We get the complete analytical expressions for B 1 -distance, B 2 -distance and B 3 -distance. We determine the parameter range for the optimal approximation of each B 2 -distance in eight distinct scenarios. Furthermore, some trade-off relations about B 2 -distances are also studied.
Abstract Building upon the theoretical foundation of utilizing the eigenvectors of Pauli matrices for single-qubit quantum state preparation, we employ a set of easily preparable quantum states constructed from the eigenvectors of Pauli matrices and arbitrary real quantum logic gates. Using fidelity and trace norm as tools, we achieve the approximation of single-qubit quantum states. For different scenarios, we present complete exact solutions and analytical expressions for the optimal approximation. Furthermore, during the research, we investigated the comparative effectiveness of different distance metrics within the set B α Z for quantum state approximation. It was found that trace norm can approximate target state more easily than fidelity. This discovery significantly aids in the selection of measurement tools for quantum state approximation tasks.