B. Wisotsky
SAS,
United States
Summary:
Machine learning and artificial intelligence are frequently cited as application areas most likely to benefit from quantum computing, yet the timeline for realizing practical advantage remains an open question. Must meaningful quantum utility in machine learning wait for fault-tolerant hardware, or can it be explored in the current NISQ era? This talk examines the state of quantum machine learning (QML) from an applied research perspective, focusing on how quantum utility is defined, evaluated, and tested today. Drawing on ongoing work at SAS, we discuss representative QML techniques and hybrid quantum-classical workflows being investigated using real-world, customer-motivated problems. The discussion centers on how QML methods are benchmarked against classical approaches, which problem types show early promise, and how hybrid quantum-classical techniques are being applied in practice. The goal of this presentation is to provide both researchers and practitioners with a realistic view of where QML stands today, what constitutes meaningful progress in the absence of fault tolerance, and how enterprises can invest in experimentation and intellectual property that remains relevant as quantum hardware matures.