All Publications

  • Single-mode squeezed-light generation and tomography with an integrated optical parametric oscillator. Science advances Park, T., Stokowski, H., Ansari, V., Gyger, S., Multani, K. K., Celik, O. T., Hwang, A. Y., Dean, D. J., Mayor, F., McKenna, T. P., Fejer, M. M., Safavi-Naeini, A. 2024; 10 (11): eadl1814


    Quantum optical technologies promise advances in sensing, computing, and communication. A key resource is squeezed light, where quantum noise is redistributed between optical quadratures. We introduce a monolithic, chip-scale platform that exploits the χ(2) nonlinearity of a thin-film lithium niobate (TFLN) resonator device to efficiently generate squeezed states of light. Our system integrates all essential components-except for the laser and two detectors-on a single chip with an area of one square centimeter, reducing the size, operational complexity, and power consumption associated with conventional setups. Using the balanced homodyne measurement subsystem that we implemented on the same chip, we measure a squeezing of 0.55 decibels and an anti-squeezing of 1.55 decibels. We use 20 milliwatts of input power to generate the parametric oscillator pump field by using second harmonic generation on the same chip. Our work represents a step toward compact and efficient quantum optical systems posed to leverage the rapid advances in integrated nonlinear and quantum photonics.

    View details for DOI 10.1126/sciadv.adl1814

    View details for PubMedID 38478618

  • Platform-agnostic waveguide integration of high-speed photodetectors with evaporated tellurium thin films OPTICA Ahn, G., White, A. D., Kim, H., Higashitarumizu, N., Mayor, F. M., Herrmann, J. F., Jiang, W., Multani, K. S., Safavi-Naeini, A. H., Javey, A., Vuckovic, J. 2023; 10 (3): 349-355
  • Deep Learning With Functional Inputs JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS Thind, B., Multani, K., Cao, J. 2022
  • High-bandwidth CMOS-voltage-level electro-optic modulation of 780 nm light in thin-film lithium niobate OPTICS EXPRESS Celik, O., Sarabalis, C. J., Mayor, F. M., Stokowski, H. S., Herrmann, J. F., McKenna, T. P., Lee, N. A., Jiang, W., Multani, K. S., Safavi-Naeini, A. H. 2022; 30 (13): 23177-23186

    View details for DOI 10.1364/OE.460119

    View details for Web of Science ID 000813479600073

  • The impact of methodological choices when developing predictive models using urinary metabolite data. Statistics in medicine Krstic, N., Multani, K., Wishart, D. S., Blydt-Hansen, T., Cohen Freue, G. V. 2022


    The continuous evolution of metabolomics over the past two decades has stimulated the search for metabolic biomarkers of many diseases. Metabolomic data measured from urinary samples can provide rich information of the biological events triggered by organ rejection in pediatric kidney transplant recipients. With additional validation, metabolic markers can be used to build clinically useful diagnostic tools. However, there are many methodological steps ranging from data processing to modeling that can influence the performance of the resulting metabolomic classifiers. In this study we focus on the comparison of various classification methods that can handle the complex structure of metabolomic data, including regularized classifiers, partial least squares discriminant analysis, and nonlinear classification models. We also examine the effectiveness of a physiological normalization technique widely used in the clinical and biochemical literature but not extensively analyzed and compared in urine metabolomic studies. While the main objective of this work is to interrogate metabolomic data of pediatric kidney transplant recipients to improve the diagnosis of T cell-mediated rejection (TCMR), we also analyze three independent datasets from other disease conditions to investigate the generalizability of our findings.

    View details for DOI 10.1002/sim.9431

    View details for PubMedID 35567357

  • Superconducting on-chip tunable mm-wave resonator Das, D., Naji, A., Multani, K. S., Safavi-Naeini, A. H., Nanni, E. A., IEEE IEEE. 2022
  • Development of a Millimeter-Wave Transducer for Quantum Networks Multani, K. S., Stokowski, H., Snively, E., Patel, R., Jiang, W., Lee, N., Welander, P. B., Nanni, E. A., Safavi-Naeini, A. H., IEEE IEEE. 2020