Compass in the Mirror: Quantum Backpropagation with the Hopf Ansatz
quant-ph
Submitted: 2026-10-09
Updated: 2026-10-09
Sources
- A Compass on the Quantum State Sphere: The Hopf Ansatz for Arbitrary Pure-State Optimization
- On quantum backpropagation, information reuse, and cheating measurement collapse
- Backpropagation scaling in parameterised quantum circuits
- Quantum Circuit Learning
- Evaluating analytic gradients on quantum hardware
- Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule
- General parameter-shift rules for quantum gradients
- Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms
- Efficient calculation of gradients in classical simulations of variational quantum algorithms
- A Universal Training Algorithm for Quantum Deep Learning
- Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries
- Predicting Many Properties of a Quantum System from Very Few Measurements
- Quantum Shadow Gradient Descent for Variational Quantum Algorithms
- Efficient Quantum Gradient and Higher-order Derivative Estimation via Generalized Hadamard Test
- Computing the gradients with respect to all parameters of a quantum neural network using a single circuit
- Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks
- Adaptive directional gradients for parameterised quantum circuits
- Automatic differentiation in machine learning: a survey
- Variational Quantum Computation of Excited States
- Theory of variational quantum simulation
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