| Literature DB >> 29335489 |
Yosep Kim1, Yong-Su Kim2, Sang-Yun Lee2, Sang-Wook Han2, Sung Moon2, Yoon-Ho Kim3, Young-Wook Cho4.
Abstract
The weak value concept has enabled fundamental studies of quantum measurement and, recently, found potential applications in quantum and classical metrology. However, most weak value experiments reported to date do not require quantum mechanical descriptions, as they only exploit the classical wave nature of the physical systems. In this work, we demonstrate measurement of the sequential weak value of two incompatible observables by making use of two-photon quantum interference so that the results can only be explained quantum physically. We then demonstrate that the sequential weak value measurement can be used to perform direct quantum process tomography of a qubit channel. Our work not only demonstrates the quantum nature of weak values but also presents potential new applications of weak values in analyzing quantum channels and operations.Entities:
Year: 2018 PMID: 29335489 PMCID: PMC5768737 DOI: 10.1038/s41467-017-02511-2
Source DB: PubMed Journal: Nat Commun ISSN: 2041-1723 Impact factor: 14.919
Fig. 1Schematic of experimental setup. The system and meter qubits are encoded in the polarization state of single-photons. The ancilla qubit is encoded on the path mode of the single-photon carrying the system qubit. Measurement of the observables and are sequentially applied to the system qubit |ψ〉s. Projection measurement of the observable , arbitrarily set by the angle θA of HWP, is accomplished by interacting it with the ancilla qubit |0〉a. Likewise, weak measurement of the observable , arbitrarily set by the angle θB of HWP, is accomplished by interacting the system and ancilla qubits with the meter qubit |0〉m. The weak measurement strength is parameterized by g = 4θ where θ is the angle of HWP. The sequential weak value is obtained from the expectation values of the meter qubit conditioned on the post-selective projection measurements on the system and ancilla qubits and . To perform direct quantum process tomography of a quantum channel with sequential weak values, an arbitrary quantum operation is inserted between the observables and . BD (beam displacer), PBS (polarizing beam splitter), PPBS (partially polarizing beam splitter), QWP (quarter wave plate), SPCM (single-photon counting module)
Fig. 2Extracting sequential weak values. The data points indicate the measured expectation values for the meter photon as a function of the weak measurement strength g. The system qubit is initially prepared in and the final post-selective projection measurement is defined by . For a, the two non-commuting observables are and . For b, and are exchanged. The black solid lines are the exact theoretical curves. The dashed lines are the first-order dependence of g obtained from the polynomial fit to the data from which the sequential weak value is extracted. The shaded regions represent simulated errors assuming the phase instability of ±π/36 radians in the BD interferometer. Note that shaded regions for a are too narrow to be visible. The measured sequential weak values are in good agreement with the theoretical values and . Error bars represent one standard deviation due to Poissonian counting statistics
Fig. 3Direct-QPT via sequential weak values. The quantum operation inserted between the observables and can be characterized by measuring the sequential weak values. a is the Hadamard operation implemented by HWP set at 22.5°. b is the -gate operation implemented by QWP set at 45°, the polarization rotating operation along x-axis. c is the PPBS operation. The directly measured raw process matrices in Dirac basis are shown at left. The corresponding process matrices in the standard Pauli basis are shown at right, where the process matrices are reconstructed to be physical matrices via the maximum likelihood estimation technique. Solid (empty) bars represent experimental (theoretical) results. The fidelities between the measured and the ideal are a , b and c . The errors in are obtained by performing 500 Monte–Carlo simulation runs by taking into account of the statistical errors in measured weak values