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Despite interest and potential in STEM (Science, Technology, Engineering and Mathematics), neurodivergent (ND) individuals face underrepresentation and marginalization. These individuals experience low rates of degree completion and even higher dropout rates from STEM programs. In the workplace, elevated levels of unemployment among individuals with disabilities underscore the need to address obstacles to persistence in STEM programs and pathways to the workforce. The AIE-STEMPLOS (Access to Innovative Education in Science, Technology, Engineering, and Mathematics-Providing Learning Opportunities and Scholarship) program at Landmark College, launched in 2021, aims to empower ND STEM scholars by leveraging effective mentoring strategies to support degree completion and career development in STEM fields. Supported by the National Science Foundation (NSF) through scholarship funding (S-STEM), the program's primary goals are to support domestic low-income, academically talented ND scholars in Computer Science and Life Science, create a robust culture of mentorship within the STEM department, and strengthen scholarly professional development. We generally refer to students as scholars in this program as that is the language preferred by the NSF. The mentoring component is designed to enhance psychosocial and professional development through faculty, group, and peer mentoring. Employing tools like the Birkman Method, mentor maps and Individual Development Plans (IDP), the program fosters self-understanding and community among scholars. Evaluation methods include qualitative and quantitative assessments, with data showing high satisfaction with mentor-mentee relationships, robust engagement in professional development activities, and significant improvements in scholars' professional outlook and STEM identity. This comprehensive approach integrates faculty mentors, career counselors, and weekly cohort meetings for mentoring and professional development activities. This paper will highlight the faculty and group/ peer mentoring components of the program, demonstrating how inclusive educational strategies can promote diversity within STEM fields. 

Abstract As we approach the era of quantum advantage, when quantum computers (QCs) can outperform any classical computer on particular tasks, there remains the difficult challenge of how to validate their performance. While algorithmic success can be easily verified in some instances such as number factoring or oracular algorithms, these approaches only provide pass/fail information of executing specific tasks for a single QC. On the other hand, a comparison between different QCs preparing nominally the same arbitrary circuit provides an insight for generic validation: a quantum computation is only as valid as the agreement between the results produced on different QCs. Such an approach is also at the heart of evaluating metrological standards such as disparate atomic clocks. In this paper, we report a cross-platform QC comparison using randomized and correlated measurements that results in a wealth of information on the QC systems. We execute several quantum circuits on widely different physical QC platforms and analyze the cross-platform state fidelities. 

Finite-temperature phases of many-body quantum systems are fundamental to phenomena ranging from condensed-matter physics to cosmology, yet they are generally difficult to simulate. Using an ion trap quantum computer and protocols motivated by the quantum approximate optimization algorithm (QAOA), we generate nontrivial thermal quantum states of the transverse-field Ising model (TFIM) by preparing thermofield double states at a variety of temperatures. We also prepare the critical state of the TFIM at zero temperature using quantum–classical hybrid optimization. The entanglement structure of thermofield double and critical states plays a key role in the study of black holes, and our work simulates such nontrivial structures on a quantum computer. Moreover, we find that the variational quantum circuits exhibit noise thresholds above which the lowest-depth QAOA circuits provide the best results.