Commercial applications of 3D on the Web (panel session - abstract only). - Christian Doegle; Franz Buchenberger; Steve Timmerman; Eric Anschütz; Maurizio Vecchione (Web3D, 2000)

Variational Quantum Factoring. - Eric Anschütz; Jonathan Olson; Alán Aspuru-Guzik; Yudong Cao (QTOP@NetSys, 2017)

Near-Term Quantum-Classical Associative Adversarial Networks. - Eric R. Anschütz; Cristian Zanoci (CoRR, 2019)

Coreset Clustering on Small Quantum Computers. - Teague Tomesh; Pranav Gokhale; Eric R. Anschütz; Frederic T. Chong (CoRR, 2020)

Enhancing Generative Models via Quantum Correlations. - Xun Gao; Eric R. Anschütz; Sheng-Tao Wang; J. Ignacio Cirac; Mikhail D. Lukin (CoRR, 2021)

Critical Points in Quantum Generative Models. - Eric Ricardo Anschütz (ICLR, 2022)

Training Quantum Boltzmann Machines with Coresets. - Joshua Viszlai; Teague Tomesh; Pranav Gokhale; Eric Anschütz; Frederic T. Chong (QCE, 2022)

Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing. - Marco Cerezo; Martin Larocca; Diego García-Martín; Nelson L. Diaz; Paolo Braccia; Enrico Fontana; Manuel S. Rudolph; Pablo Bermejo 002; Aroosa Ijaz; Supanut Thanasilp; Eric R. Anschütz; Zoë Holmes (CoRR, 2023)

Efficient classical algorithms for simulating symmetric quantum systems. - Eric R. Anschütz; Andreas Bauer; Bobak Toussi Kiani; Seth Lloyd (Quantum, 2023)

Quantifying the Limits of Classical Machine Learning Models Using Contextuality. - Eric R. Anschütz; Mariesa Teo; Willers Yang; James Sud; Christopher Kang; Teague Tomesh; Frederic T. Chong (QCE, 2024)

Arbitrary Polynomial Separations in Trainable Quantum Machine Learning. - Eric R. Anschütz; Xun Gao (CoRR, 2024)

Q-CHOP: Quantum constrained Hamiltonian optimization. - Michael A. Perlin; Ruslan Shaydulin; Benjamin P. Hall; Pierre Minssen; Changhao Li; Kabir Dubey; Richard Rines; Eric R. Anschütz; Marco Pistoia; Pranav Gokhale (CoRR, 2024)

Combinatorial NLTS From the Overlap Gap Property. - Eric R. Anschütz; David Gamarnik; Bobak T. Kiani (Quantum, 2024)

A Unified Theory of Quantum Neural Network Loss Landscapes. - Eric Ricardo Anschütz (ICLR, 2025)

Optimizing Sparse SYK. - Matthew Ding 001; Robbie King; Bobak T. Kiani; Eric R. Anschütz (CoRR, 2025)