Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

2023 Abstracts

Quantum Optical Microscopy of Biomolecules near Interfaces and Surfaces (QuOMBIS)


Simeon Bogdanov1, Paul Kwiat1, Virginia Lorenz1, Samarthya Bhagia2, Mitchel Doktycz2, Ali Passian2, and Mikael Backlund1* ([email protected])


1University of Illinois Urbana–Champaign; and 2Oak Ridge National Laboratory


Researchers will develop three complementary microscopy techniques that exploit quantum correlations in light: Hong-Ou-Mandel interferometric tomography, g(2) correlation function imaging, and passive and active transverse mode sorting. Upon initial demonstration, the team will incorporate these methods into a single platform for tracking and imaging individual and few fluorescently labeled biomolecules, including cellulases, in the context of nearby biological interfaces and surfaces in order to unravel the fundamental processes involved in the conversion of lignocellulosic biomass into renewable fuels.


Since the publication of Hooke’s Micrographia in 1665, the scientific disciplines of light microscopy and (sub)cellular biology have progressed in lockstep with one another. Advances in the spatial and temporal resolution, specificity, and sensitivity of optical methods have continually led to new capabilities and insights in biological imaging. The pace of this evolution has quickened in the past century, as a mastery of the physics of light according to Maxwell’s equations has been wielded to more fully exploit classical effects like interference and diffraction. As the classical limits of light microscopy near saturation, however, sustained improvement in bioimaging technology is ultimately untenable without a more fundamental shift in research direction. Just as the field of quantum computing has gained prominence in anticipation of the inevitable breakdown of Moore’s Law, quantum-enabled light microscopy will likely provide the path forward for (sub)cellular biological imaging.

The team aims to help lead this effort by developing three complementary quantum microscopy modalities that each address a different challenge inherent to (sub)cellular microscopy:

  1. Hong-Ou-Mandel Interference Microscopy to enable loss- and noise-tolerant depth imaging with exquisite resolution;
  2. g(2) Microscopy to facilitate orders-of-magnitude sensitivity improvement in focusing and tracking single quantum emitters atop oppressive classical backgrounds at reduced excitation powers; and
  3. Transverse Mode Sorting Microscopy to enable super-resolution microscopy at low excitation powers and high temporal resolution.

Preliminary results demonstrate progress in developing these constituent techniques. The team will ultimately incorporate them into a common imaging platform that can provide access to the many scales of interest in energy-relevant plant and microbial biology. The combined technique, Quantum Optical Microscopy of Biomolecules near Interfaces and Surfaces (QuOMBIS), will be especially powerful for tracking and imaging individual and few fluorescently labeled biomolecules in the context of nearby biological interfaces and surfaces. Upon development of the methods, researchers will apply the platform to unravel and harness the enzymatic conversion of biomass into renewable fuels.


Quantum Optical Microscopy of Biomolecules

The QuOMBIS platform marries quantum optical techniques which take advantage of photon-photon correlations in three distinct ways in order to improve signal-to-background and resolution without requiring high excitation doses. The integrated apparatus will be applied to image and track single and few fluorescently labeled cellulases acting on cellulose substrates in an effort to uncover fundamental processes in the production of biofuel. Courtesy Simeon Bogdanov, Paul Kwiat, Virginia Lorenz, Samarthya Bhagia, Mitchel Doktycz, Ali Passian, and Mikael Backlund

Funding Information

This research was supported by the DOE Office of Science, Biological and Environmental Research (BER) Program, grant no. DE-SC0023167.