Quantum and Quantum-Inspired Optimizers¶
This page collects optimizers from two adjacent settings. The first is the optimization of variational quantum circuits, where shot noise and the quantum geometry of the parameter space drive the design of measurement-frugal, gradient-free, and natural-gradient methods. The second is quantum-inspired and quantum-hardware optimization of classical neural networks, where quantum fluctuations, adiabatic evolution, or annealer sampling replace or augment the classical training loop.
Optimizers for variational quantum circuits¶
Quantum-inspired and quantum-hardware methods¶
| Optimizer | Venue | Paper | Code |
|---|---|---|---|
| Quantum Adam | Scientific Reports 2018 | Optimization of neural networks via finite-value quantum fluctuations | — |
| RBM training on a D-Wave annealer | Frontiers in Physics 2021 | Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer | — |
| Quantum Hamiltonian Descent (QHD) | arXiv 2023 | Quantum Hamiltonian Descent | official |
| Universal AQC neural-network training | Frontiers in Artificial Intelligence 2024 | Training neural networks with universal adiabatic quantum computing | — |
| QHDOPT | INFORMS Journal on Computing 2025 | QHDOPT: A Software for Nonlinear Optimization with Quantum Hamiltonian Descent | official |
| Stochastic Quantum Hamiltonian Descent (SQHD) | arXiv 2025 | Stochastic Quantum Hamiltonian Descent | — |
| QIASO | AIMS Mathematics 2026 | The quantum-inspired adaptive superposition optimization for neural network training | — |