!
You're viewing papers too quickly. Please wait a moment.<br>This helps keep the archive available for everyone.
Quick Navigation
Topics
Quantum Algorithms
Sampling hard circuits with verifiably high fidelity
arXiv
Authors: Simon Martiel, Jay-U Chung, Alireza Seif, Soumik Ghosh, Ian Hincks, Abhinav Deshpande, Bill Fefferman, Jay M. Gambetta, Ali Javadi-Abhari
Year
2026
Paper ID
73686
Status
Preprint
Abstract Read
~2 min
Abstract Words
182
Citations
N/A
Abstract
Sampling-based proposals are prominent candidates for demonstrating quantum computations beyond the reach of classical supercomputers. However, it has been difficult to combine their complexity-theoretic hardness with two capabilities needed for scalable quantum computing more generally: suppressing hardware errors, and verifying the quantum computation itself. Here we address both issues by introducing structured circuits, which, in addition to provable hardness guarantees, admit an encoding in a quantum code. This allows us to simultaneously reach high fidelities at high circuit depths, and to certify an experimental fidelity via the circuit structure and measurement of code syndromes. The resulting certificate is device dependent, but requires substantially weaker noise assumptions than existing fidelity proxy benchmarks. We demonstrate our proposal with a 70-qubit, depth-70 Clifford circuit doped with 468 T gates. We use a total of 97 physical qubits to encode this computation in spacetime codes, effectively suppressing gate error rates by 10times after syndrome post-selection, and yielding a state with a fidelity lower bound of 0.284 with 95\% confidence. Our construction is a systematic method for promoting a stabilizer state to a magic state while keeping an error-detected fidelity certificate.
Why This Paper Matters
- It adds a 2026 reference point for readers tracking recent quantum research.
- Sampling-based proposals are prominent candidates for demonstrating quantum computations beyond the reach of classical supercomputers.
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.