Quick Navigation
Topics
Trapped Ion Quantum Computing
Limitations of Noisy Geometrically Local Quantum Circuits
arXiv
Authors: Jon Nelson, Joel Rajakumar, Michael J. Gullans
Year
2025
Paper ID
51681
Status
Preprint
Abstract Read
~2 min
Abstract Words
208
Citations
N/A
Abstract
It has been known for almost 30 years that quantum circuits with interspersed depolarizing noise converge to the uniform distribution at ω\(log n\) depth, where n is the number of qubits, making them classically simulable. We show that under the realistic constraint of geometric locality, this bound is loose: these circuits become classically simulable at even shallower depths. Unlike prior work in this regime, we consider sampling from worst-case circuits and noise of any constant strength. First, we prove that the output distribution of any noisy geometrically local quantum circuit can be approximately sampled from in quasipolynomial time, when its depth exceeds a fixed Θ\(log n\) critical threshold which depends on the noise strength. This scaling in n was previously only obtained for noisy random quantum circuits (Aharonov et. al, STOC 2023). We further conjecture that our bound is still loose and that a Θ(1)-depth threshold suffices for simulability due to a percolation effect. To support this, we provide analytical evidence together with a candidate efficient algorithm. Our results rely on new information-theoretic properties of the output states of noisy shallow quantum circuits, which may be of broad interest. On a fundamental level, we demonstrate that unitary quantum processes in constant dimensions are more fragile to noise than previously understood.
Why This Paper Matters
- This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- It has been known for almost 30 years that quantum circuits with interspersed depolarizing noise converge to the uniform distribution at ω(log n) depth, where n is the number...
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.