Claim record · edition 0.1.0
No source in this atlas establishes that classical tensor-network contraction generally outperforms quantum hardware on industrial optimization workloads.
This claim exists to state an absence rather than leave it to be inferred from silence. The strongest results recorded here are a specific sampling task reproduced classically and a specific portfolio-optimization study. Neither is a throughput comparison across portfolio construction, supply-chain logistics, and molecular simulation, and this atlas publishes no performance figures it cannot attribute to a cited source.
Limits of this claim
The absence of a general result is not evidence that classical methods are inferior, nor that they are superior. It records that the comparison the commercial framing asserts has not been established by the sources here. A future benchmark could change this claim; a vendor figure without a resolvable source could not.
Benchmark records behind this claim
Benchmark record · bench-sycamore-sampling
Sampling from the output distribution of the Sycamore random quantum circuits.
- Classical method
- Tensor-network contraction with an optimized contraction order, run on classical hardware.
- Compared against
- The superconducting-processor demonstration that originally framed this task as beyond practical classical reach.
- Reported result
- The cited work reports solving the Sycamore sampling problem classically, substantially narrowing the gap the original demonstration claimed for this task.
Does not establish
It does not show classical tensor networks outperform quantum hardware in general, and it is a benchmark sampling task rather than an application workload. It also does not settle later circuits at different depths or sizes.
Source · arXiv:2111.03011
Benchmark record · bench-dynamic-portfolio
Dynamic portfolio optimization on real market datasets.
- Classical method
- Quantum-inspired tensor-network optimization.
- Compared against
- Quantum processors applied to the same problem instances.
- Reported result
- The cited work reports applying both tensor-network methods and quantum processors to dynamic portfolio optimization with real datasets.
Does not establish
It does not establish a general throughput advantage over quantum annealers or gate-based hardware, does not extend to supply-chain logistics or molecular simulation, and does not license the claim that tensor networks are the better production method for portfolio construction. Instance sizes, cost models, and solution-quality criteria all bound what a result like this transfers to.
Source · arXiv:2007.00017
Supporting sources
primary paper · identifier verified
Solving the sampling problem of the Sycamore quantum circuits
Feng Pan, Keyang Chen, Pan Zhang · 2021
arXiv:2111.03011
Classical tensor-network contraction applied to the Sycamore sampling task. The strongest evidence here that a specific claimed quantum advantage narrowed — for one benchmark task, not for an application.
Source record →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 →primary paper · identifier verified
A Quantum Approximate Optimization Algorithm
Edward Farhi, Jeffrey Goldstone, Sam Gutmann · 2014
arXiv:1411.4028
Primary source for QAOA, the quantum method against which quantum-inspired classical optimizers are usually compared.
Source record →