DEI Lecture in Quantum Engineering

Please join The Office of Diversity, Equity, and Inclusion for the opening event of our 2024-2024 Benjamin Banneker Lecture Series! Dr. Thomas Searles will be presenting on, “Quantum Engineering in the NISQ Era," which will explore the emerging fields of quantum information technologies, as well as the movement to create a nation-wide quantum mechanics program between HBCUs and PWIs. RSVPs not required, though appreciated and encouraged. 

REGISTER HERE

WHEN | WHERE
Friday, November 1, 2024
3:00 - 4:00 PM | Fred Kavli Auditorium in Kohn Hall, KITP

ABOUT THE EVENT

Quantum information technology is emerging and expected to disrupt current critical communications, computing, and sensing infrastructure.  Importantly, each of the near-term applications of quantum information are recognized globally as challenging in both a scientific and technological sense.

In the first part of his talk, Dr. Searles will present recent examples of quantum engineering research that show adaptation of a black-box quantum algorithm (Deutsch-Josza) based on a metamaterial-based graded-index lens for operation in the THz regime [1].   Then, he will present the temperature dependence and characterization of single photon outputs from a fiber-based entangled photon source expected for applications in a quantum-enabled internet [2,3]. He will present a quantum state tomography framework where state reconstruction is directly obtained from a set of measurements (both from optical and near-term intermediate scale superconducting qubit-based quantum hardware) and features enhanced application performance from a suite of artificial intelligence tools [4-7].

In the second part of his talk, Dr. Searles will detail his efforts to create a nation-wide quantum mechanics and quantum computer training programs between HBCU’s and PWI's to create a pipeline for a skilled, diverse workforce to bring ideas central to quantum information to real-world application outside of the laboratory [8].  As such, we will detail present activities at UIC to update an ABET-accredited Engineering Physics curricula and encourage students to gain experience and knowledge in quantum engineering.

ABOUT THE SPEAKER

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Thomas A. Searles is a quantum engineer, and experimental condensed matter physicist, from Albany, GA. He graduated from Morehouse College (BS) and Rice University with a PhD in Applied Physics. In 2021, Searles joined the University of Illinois Chicago as an Associate Professor, under the University System’s Distinguished Faculty Recruitment Program. Prior to UIC, he was a Martin Luther King Visiting Professor at MIT and the Director of the IBM- HBCU Quantum Center. Prof. Searles currently leads a multi- year multimillion dollar consortium for quantum engineering education sponsored by the Department of Energy. In recognition for his research in light-matter interactions and his capability to mentor students in Physics and Engineering, Thomas was awarded the inaugural AIP-NSBP Joseph A. Johnson Award for Excellence and an NSF CAREER Award. Recently, Thomas was appointed as an APS DLS Distinguished Traveling Lecturer and member-at-large of the US National Committee for Theoretical and Applied Mechanics, National Academies.

REFERENCES

1.    A. N. Blackwell, R. Yahiaoui, Y. Chen, P. Y. Chen, T. A. Searles, and Z. A. Chase, " Emulating the Deutsch-Josza algorithm with an inverse-designed terahertz gradient-index lens," Optics Express, accepted (2023).
2.     S. Lohani, J. M. Lukens, A. A. Davis, A. Khannejad*, S. Regmi, D. E. Jones, R. T. Glasser,
T. A. Searles, and B. T. Kirby, “Demonstration of machine-learning-enhanced Bayesian quantum state estimation” New Journal of Physics 25, 083009 (2023). *Undergrad Author
3.     S. Lohani, S. Regmi, J.M. Lukens, R. T. Glasser, T. A. Searles  and B. T. Kirby, “Dimension-adaptive machine-learning-based quantum state reconstruction”, Quantum Machine Intelligence 5, 1 (2023).
4.     S. Lohani, J. M. Lukens, D. A. Jones, R. T. Glasser, T. A. Searles, and B. T. Kirby, “Data-centric machine learning in quantum information science”, Machine Learning Science & Technology 3, 04LT01 (2022).

5.  S. Lohani, J. Lukens, D. Jones, T. A. Searles, B. T. Kirby and R. T. Glasser, “Improving application performance with biased distributions of quantum states”, Phys. Rev. Research 3, 043145 (2021).  
6.  S. Lohani, T. A. Searles, B. T. Kirby and R. T. Glasser, “On the experimental feasibility of quantum state estimation via machine learning”, IEEE Trans. on Quantum Engineering 2, 2103410 (2021).
7. S. Lohani, et al. Machine Learning Science & Technology 3, 04LT01 (2022).

8. A. Asfaw et. al, IEEE Trans. on Education 65, 220 (2022).