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Do Quantum Models Scale Like LLMs?
David S. Berman, Ying-Jer Kao, Roger G. Melko +1
In this work, we study the neural scaling laws of RydbergGPT, an autoregressive transformer model trained on qubit projective measurement data gathered from interacting Rydberg ato…
Private and interpretable clinical prediction with quantum-inspired tensor train models
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1
Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover information from patients whose d…
Exploring the Energy Landscape of RBMs: Reciprocal Space Insights into Bosons, Hierarchical Learning and Symmetry Breaking
J. Quetzalcóatl Toledo-Marin, Anindita Maiti, Geoffrey C. Fox +1
Deep generative models have become ubiquitous due to their ability to learn and sample from complex distributions. Despite the proliferation of various frameworks, the relationship…
Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions
Ian Lu, Hao Jia, Sebastian Gonzalez +10
With the approach of the High Luminosity Large Hadron Collider (HL-LHC) era set to begin particle collisions by the end of this decade, it is evident that the computational demands…
Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions
J. Quetzalcoatl Toledo-Marin, Sebastian Gonzalez, Hao Jia +9
Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Mod…
Autoregressive model path dependence near Ising criticality
Yi Hong Teoh, Roger G. Melko
Autoregressive models are a class of generative model that probabilistically predict the next output of a sequence based on previous inputs. The autoregressive sequence is by defin…