Concept record · edition 0.1.0
Quantum-inspired optimization
Classical algorithms — tensor-network contraction among them — that borrow structure from quantum many-body methods and run on conventional hardware.
Why it matters
It provides the classical baseline that any claimed quantum advantage on an optimization problem has to beat.
What this does not establish
The label describes an algorithmic lineage, not a demonstrated performance class. It does not imply an advantage over either classical solvers or quantum hardware on a given workload.
Claims using this concept
- tn-011 · Established resultClassical tensor-network contraction has been reported to solve the Sycamore random-circuit sampling problem, narrowing the advantage originally claimed for that specific task.
- tn-012 · Active researchTensor-network methods have been applied to dynamic portfolio optimization on real datasets alongside quantum processors, but the reported work does not establish a general advantage.
- tn-013 · Active researchWhether QAOA delivers a practical advantage over strong classical baselines on real optimization instances remains unresolved.
- tn-014 · Active researchNo source in this atlas establishes that classical tensor-network contraction generally outperforms quantum hardware on industrial optimization workloads.
Sources
primary paper · identifier verified
Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks
Samuel Mugel, Carlos Kuchkovsky, Escolástico Sánchez, Samuel Fernández-Lorenzo, Jorge Luis-Hita, Enrique Lizaso, Román Orús · 2020
arXiv:2007.00017
A reported application of tensor-network optimization to portfolio construction alongside quantum processors — the closest thing in this source set to the commercial framing, and narrower than that framing suggests.
Source record →review article · identifier verified
A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States
Román Orús · 2013
arXiv:1306.2164
Review covering tensor-network structure, entanglement scaling, and contraction strategy; the general reference for this atlas.
Source record →