Skip to content
aiqbench takeQuantum Computing

aiqbench analysis

The paper proposes a way to split a quantum circuit across processors by approximating the weak coupling between parts through a single qubit, which reduces the classical cost of reconstructing the result from exponential to polynomial. The authors test it on a layered circuit modeled on QAOA. For readers, the key question is the accuracy-versus-cost tradeoff: the gains depend on the circuit being weakly coupled in this way, and the error from the approximation is the practical limit to evaluate.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Quantum circuit knitting using a weak-coupling approximation

arXiv1 min read

arXiv:2606.19035v3 Announce Type: replace Abstract: We present a method for performing distributed quantum computing with controlled approximations for circuits that are weakly coupled via a single qubit. Exact distributed quantum computing requires exponential classical information to reconstruct the quantum process. However, we show how the classical cost is reduced to polynomial if the quantum procedure can be partitioned between a qubit that is weakly coupled to the other qubits. We demonstrate our method for a layered circuit based on the circuits used for the quantum approximate optimization algorithm.

Abstract:We present a method for performing distributed quantum computing with controlled approximations for circuits that are weakly coupled via a single qubit. Exact distributed quantum computing requires exponential classical information to reconstruct the quantum process. However, we show how the classical cost is reduced to polynomial if the quantum procedure can be partitioned between a qubit that is weakly coupled to the other qubits. We demonstrate our method for a layered circuit based on the circuits used for the quantum approximate optimization algorithm.

arXiv:2606.19035 [quant-ph]

(or arXiv:2606.19035v3 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2606.19035 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 172 words

Related coverage