More than 1,000 applicants vied for just 20 spots in the Quantum Computing Summer School at Los Alamos National Laboratory this past summer, demonstrating intense demand for specialized training in the field. The school paired students with Los Alamos experts on projects ranging from quantum algorithms to hardware implementation, scientists Torin Stetina and Vasilis Belis presenting on quantum spectral methods for machine learning.
Rolando Somma discussed shadow Hamiltonian simulation as a compressed framework for addressing otherwise intractable simulation problems. The program was hosted in the new Center for Quantum Computing building, designed to support collaboration among students and mentors.
Quantum Algorithms, Simulation & Sensing Drive Student Research
Jehu Martinez, a student at Illinois Institute of Technology, gained practical research experience this summer by taking ownership of a problem alongside Los Alamos National Laboratory researchers. The Quantum Computing Summer School facilitated this hands-on learning, pairing 20 students selected from over 1,000 applicants with experts on projects focused on quantum algorithms, simulation, and sensing. This year’s program uniquely emphasized collaborative research closely integrated with ongoing mentor-led work, extending beyond traditional lectures.
The open-plan workspace within the new Center for Quantum Computing encouraged spontaneous interactions across projects, while dedicated meeting areas enabled extended working sessions between mentors and students. Sumner N. Hearth, a postdoctoral fellow, developed a framework using prior information about a quantum state to improve classical shadow tomography, even when existing methods proved insufficient.
This work addressed performance limitations in state reconstruction, a critical step in verifying quantum computations and characterizing quantum devices. Martinez said, “I was given the freedom to explore the problem and figure out how we could study it.” He added that he was able to get some meaningful results this summer, and they are now working on turning those results into a manuscript. Students explored a diverse range of topics, including quantum phase estimation, quantum sensing and simulations of quantum spin liquids and lattice gauge theories.
The program’s impact extends beyond immediate research outcomes. Sharma, an alumnus of the 2019 summer school, now leads a quantum algorithms team at IBM after returning to Los Alamos as a Graduate Research Assistant and QuICS Hartree Postdoctoral Fellow. “My advice is to use your time there to explore unfamiliar topics and work on challenging research questions with fellow students and Los Alamos researchers,” Sharma stated.
He further explained, “If everything works out, I could have the chance to see an idea go from theory and numerical work all the way into experimental practice,” and believes the experience “is something that could be pivotal for me as I continue my research career.” The program also instilled valuable research practices; one student noted that it taught them the value of debating ideas early and thinking carefully about what makes a good research paper.
The Los Alamos Quantum Computing Summer School was instrumental in shaping my research taste and the way I approach quantum computing.
Kunal Sharma, Senior Research Scientist and Manager for quantum algorithms at IBM
Continuous Quantum Measurement & Noise Correction Techniques Presented
Presentations from University of New Mexico researchers provided key theoretical underpinnings for the summer school’s hands-on projects, with Ivan Deutsch outlining the principles and applications of continuous quantum measurement. Milad Marvian followed, detailing methods for mitigating quantum noise across all layers of the computing stack. Their talks highlighted first-quantized simulations of photoemission spectroscopy and the potential of quantum spectral methods for machine learning. The newly completed Center for Quantum Computing facilitated these exchanges, with its open-plan workspace designed to encourage spontaneous collaboration between students and mentors.
What surprised me most was the deep intuition the researchers have for the quantum systems they work with.
Jehu Martinez, Student at Illinois Institute of Technology
Quantum Computing Summer School Fosters Career Pathways & Collaboration
Sharma’s trajectory, beginning with a 2019 summer school appointment at Los Alamos and continuing through a QuICS Hartree Postdoctoral Fellowship, exemplifies the program’s impact on career development within the field. He presented research demonstrating how experimental quantum data can train models predicting ground-state observables for novel Hamiltonian parameters, a technique broadening the scope of quantum simulations. The summer school’s structure intentionally blends theoretical instruction with practical research, pairing students with Los Alamos experts on diverse projects.
This year’s cohort of 20 benefited from lectures by leaders in quantum information science, including Ivan Deutsch, Distinguished and Regents Professor at the University of New Mexico. Deutsch presented on continuous quantum measurement, outlining both the theory and its applications.
My advice is to use your time there to explore unfamiliar topics and work on challenging research questions with fellow students and Los Alamos researchers.
Kunal Sharma, Senior Research Scientist and Manager for quantum algorithms at IBM
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