Triangle Quantum Computing Seminar Series: Quantum Reservoir Computing

Abstract: This talk will focus on quantum reservoir computing (QRC), a quantum extension of classical reservoir computing. Reservoir computing provides an alternative framework for sequential modeling tasks that are typically performed using recurrent neural networks, transformer models, or autoregressive models. In classical reservoir computing, an input signal is propagated through a fixed nonlinear dynamical system called a "reservoir," whose inherent dynamics act as a feature extractor. QRC extends this concept by using quantum systems (or quantum processors) as naturally high-dimensional reservoirs. The talk will examine how QRC compares with classical machine learning approaches. It will conclude with a brief introduction to FirstQFM, a deep-tech company based in Stockholm that develops machine learning foundation models to improve the performance and scalability of quantum computers.

Bio: Isaiah Hull is the Co-founder and CTO of FirstQFM, a Stockholm-based deep tech company building machine learning foundation models to improve the performance and scalability of quantum computers. A former academic and quantum research scientist with a PhD from Boston College, he is also a Quantum Research Fellow at Rethinc.Labs UNC, an NLP consultant for DeepLearning.AI, and the instructor of DataCamp's "Introduction to TensorFlow in Python." He has authored textbooks on machine learning and quantum computing.

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The Duke Quantum Center, the IBM Quantum Innovation Center at NC State, and the UNC Kenan-Flagler's Rethinc. Labs are pleased to present the Fall 2025 Semester Triangle Quantum Computing Seminar series.