Tag: shadow-tomography

All the articles with the tag "shadow-tomography".

• The Credit Assignment Problem: From Rosenblatt's Perceptron to Backpropagation to Quantum Gradients

  10 May, 2026

  A long-form, citation-grounded history of the credit assignment problem — the core question of how to apportion blame for a global error across the internal parameters of a learning machine. From Rosenblatt's 1958 perceptron rule, through Linnainmaa, Werbos, and the 1986 Rumelhart-Hinton-Williams paper, to modern alternatives (feedback alignment, equilibrium propagation, predictive coding, forward-forward, synthetic gradients), reinforcement-learning credit assignment, and the frontier of quantum gradients (parameter-shift rule, quantum natural gradient, HHL, and the Abbas et al. NeurIPS 2023 impossibility result).

• On Quantum Backpropagation, Information Reuse, and Cheating Measurement Collapse

  31 Mar, 2026

  Can quantum models train as efficiently as classical neural networks? Abbas et al. prove backpropagation scaling is impossible without multiple copies of a quantum state, then show how shadow tomography gets you most of the way there. A deep dive into the paper that defines the trainability frontier for quantum machine learning.