2018/05/18 by J. L. E. Silva, Scott Glancy, S. Glancy +1
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Bin #Computer science #Direct-conversion receiver #Discretization #Fidelity #Histogram #Homodyne detection #Image (mathematics) #Mathematical analysis #Mathematics #Optics #Physics #Quadrature (astronomy) #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum computer #Quantum information #Quantum mechanics #Quantum state #Quantum tomography #Statistical physics #Telecommunications #Tomography #quant-ph
paper · pdf · doi:10.1103/physreva.98.022325
published as Phys. Rev. A 98, 022325 (2018) · 10 pages, 8 figures
arxiv created 2018/05/18 · openalex publication_date 2018/08/22 · arxiv updated 2018/08/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Quantum state tomography aims to determine the quantum state of a system from measured data and is an essential tool for quantum information science. When dealing with continuous variable quantum states of light, tomography is often done by measuring the field amplitudes at different optical phases using homodyne detection. The quadrature-phase homodyne measurement outputs a continuous variable, so to reduce the computational cost of tomography, researchers often discretize the measurements. We show that this can be done without significantly degrading the fidelity between the estimated state and the true state. This paper studies different strategies for determining the histogram bin widths. We show that computation time can be significantly reduced with little loss in the fidelity of the estimated state when the measurement operators corresponding to each histogram bin are integrated over the bin width.