Despite the hype, quantum hardware remains small. Shor’s algorithm famously tops out at factoring the number 21 on general quantum systems, trapped by hardware noise and decoherence in the Noisy Intermediate-Scale Quantum (NISQ) era. While Quantum Machine Learning (QML) offers a noise-resilient lifeline, it has hit an “Optimization Wall.” Static models cannot improve data separability, while parameterizing quantum circuits triggers “Barren Plateaus”—where training gradients vanish completely.

This talk introduces Quantum-Projected Metric Learning (QPMeL) – a paradigm shift that bypasses these limits by reframing QML entirely as a metric learning problem. Instead of drawing rigid classification boundaries, QPMeL optimizes relative geometric distances. It pulls the complex metric geometry of quantum space directly into a classical deep learning loss function, shifting 100% of the training optimization to a standard computer. Crucially, QPMeL maps classical data onto independent unit spheres, mirroring the states of completely unentangled bits. By eliminating noisy entangling layers, this metric approach requires just two gates per qubit – exponentially improving hardware scaling while achieving state-of-the-art multi-class accuracy today.

Speaker: Aviral Shrivastava is a full Professor and the Chair of CS Programs in the School of Computing and AI at the Arizona State University – where he manages one of the nation’s largest CS programs. He currently serves as the Associate Editor-in-Chief of IEEE TCAD and was formerly the Editor-in-Chief of IEEE ESL. His research on “Making Programming Simple” for AI, Cyber-physical and Quantum systems has resulted in 200+ publications, 9 US patents, and 4,600+ citations. An NSF CAREER awardee and recipient of the 2023 IEEE CEDA Outstanding Service Award, Prof. Shrivastava has chaired major academic events including ESWEEK 2022 and LCTES 2024. He holds a Ph.D. from UC Irvine and a bachelor’s from IIT Delhi.

Aviral Shrivastava is a full Professor in the School of Computing and AI (SCAI) at the Arizona State University, where he established and heads the Make Programming Simple Lab (https://labs.engineering.asu.edu/mps-lab/). He completed his Ph.D. and MS in Information and Computer Science and from the University of California, Irvine, and Bachelors in Computer Science and Engineering from IIT Delhi.

Research: Prof. Shrivastava’s research is on making programming simple for embedded and cyber-physical systems. Prof. Shrivastava and his students have proposed novel computer architectures and compiler transformations for hardware error-tolerant computing, multicore computing, accelerated computing. They have also proposed languages, code generation and runtime for expressing and efficiently executing time-sensitive distributed intelligent applications.

Prof. Shrivastava has co-authored 1 book, and has contributed chapters in 4 books. He has more than 120 articles and conference papers in top embedded system journals and conferences, like DAC, ESWEEK, ACM TECS, and ACM TCPS. His papers have received several awards, including nomination for best paper at DAC 2017, best student paper award at VLSI 2016, second highest ranked paper at LCTES 2010, and best paper candidate ASPDAC 2008. He published at least one paper every year at DAC (the top conference in the field) in the last decade (2011 to 2019). Overall, his works have received more than 3000 citations, growing at the rate of over 200 citations every year. His i50-index is 14, i10-index is 84, and h-index is 31 (reference Google Scholar). His inventions have been granted 5 patents, and 5 more applications are pending. Prof. Shrivastava is the recipient of the prestigious 2010 NSF CAREER award. His student’s theses were awarded CIDSE outstanding Ph.D. thesis award in 2021 and 2017 and outstanding Master’s thesis awards in 2011 and 2014. Prof. Shrivastava’s research efforts have been supported by federal agencies (NSF, DOE, NIST), state funding agencies (SFAZ), as well as industry.

Prof. Shrivastava has mentored 2 postdocs, 9 Ph.D. students, and over 20 Masters students. His students are very well placed, including a full Professor at UNIST, South Korea, Assistant Professor at SJSU, ARM research lab, Google, Synopsys, Apple (x2), Qualcomm, Cadence etc.) Prof. Shrivastava is currently supervising 3 Ph.D., and 5 Masters students. Prof. Shrivastava teaches undergraduate and graduate level courses on computer organization, computer architecture, and embedded systems, and has student evaluations averaging over 4/5. He has redesigned the embedded systems course around projects in which students build an autonomously driving car, culminating in an autonomous car race! (https://www.youtube.com/channel/UCDfyzk7HFqeXCb5BK02SemQ)

Prof. Shrivastava is currently the General Chair of Embedded Systems Week (ESWEEK) – the top event in the field of Embedded Systems, comprising of several conferences, symposia and workshops. He also serves in the Steering committee of the Languages Compilers, Theory and tools for Embedded Systems (LCTES). Currently, he is the deputy Editor-in-Chief of IEEE Embedded Systems Letters (IEEE ESL), and associate editor for ACM Transactions of Cyber-Physical Systems (ACM TCPS), ACM Transactions Embedded Computing Systems (ACM TECS), and the IEEE Transactions on Computer Aided Design (IEEE TCAD). Previously he has served as the program chair of CODES+ISSS 2017 and 2018, LCTES 2019, and chair of the Design and Applications track of RTSS 2020.