Claim record · edition 0.1.0
Classical tensor-network contraction has been reported to solve the Sycamore random-circuit sampling problem, narrowing the advantage originally claimed for that specific task.
The result is the clearest published case of tensor-network methods closing a gap that hardware had been said to open, and it is evidence that classical baselines move.
Limits of this claim
It concerns one benchmark sampling task, not an application workload, and it does not generalize to a claim that classical tensor networks outperform quantum hardware broadly. It also does not settle circuits at other depths or sizes.
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
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 →