Scalable decoding protocols for fast transversal logic in the surface code
- URL: http://arxiv.org/abs/2505.23567v1
- Date: Thu, 29 May 2025 15:41:11 GMT
- Title: Scalable decoding protocols for fast transversal logic in the surface code
- Authors: Mark L. Turner, Earl T. Campbell, Ophelia Crawford, Neil I. Gillespie, Joan Camps,
- Abstract summary: We introduce two new, windowed decoding protocols for coherence logic in the surface code.<n>We show that, with a very small space overhead, our scalable decoders unlock an order of magnitude speed-up for connectivity logic.
- Score: 2.5631808142941415
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Atomic, molecular and optical (AMO) approaches to quantum computing are promising due to their increased connectivity, long coherence times and apparent scalability. However, they have a significantly reduced cadence of syndrome extraction compared to superconducting devices, a potentially crippling slow-down given the substantial logical gate counts required for quantum advantage. Transversal logic, which exploits higher connectivity, has the potential to significantly speed up the logical clock rate by reducing the number of syndrome extraction rounds required, but current decoders for fast transversal logic are not scalable. This is not just because existing decoders are too slow to handle the large decoding volumes resulting from fast logic; transversal logic breaks the key structural properties that make real-time decoding of lattice surgery efficient. We introduce two new, windowed decoding protocols for transversal logic in the surface code that restore modularity and locality to the decoding problem. Using our protocols, we show that, with a very small space overhead, our scalable decoders unlock an order of magnitude speed-up for transversal logic compared to lattice surgery. Taken together, our results provide key evidence for the viability of large-scale algorithms on AMO qubits.
Related papers
- Decoding across transversal Clifford gates in the surface code [0.7100520098029438]
We show how one can decode across an arbitrary sequence of window gates for the unrotated surface code.<n>Our work highlights the complexity and interest in efficient decoding of fast logic for the surface code.
arXiv Detail & Related papers (2025-05-19T18:00:02Z) - Fast correlated decoding of transversal logical algorithms [67.01652927671279]
Quantum error correction (QEC) is required for large-scale computation, but incurs a significant resource overhead.<n>Recent advances have shown that by jointly decoding logical qubits in algorithms composed of logical gates, the number of syndrome extraction rounds can be reduced.<n>Here, we reform the problem of decoding circuits by directly decoding relevant logical operator products as they propagate through the circuit.
arXiv Detail & Related papers (2025-05-19T18:00:00Z) - Learning to decode logical circuits [26.510386591426112]
We introduce a data-centric modular decoder framework, Multi-Core Circuit Decoder (MCCD)<n> MCCD handles both single-qubit and entangling gates within a unified framework.<n>Our approach represents a noise-model solution to the decoding challenge for deep logical quantum circuits.
arXiv Detail & Related papers (2025-04-23T18:00:04Z) - Experimental Demonstration of Logical Magic State Distillation [62.77974948443222]
We present the experimental realization of magic state distillation with logical qubits on a neutral-atom quantum computer.<n>Our approach makes use of a dynamically reconfigurable architecture to encode and perform quantum operations on many logical qubits in parallel.
arXiv Detail & Related papers (2024-12-19T18:38:46Z) - Local Clustering Decoder: a fast and adaptive hardware decoder for the surface code [0.0]
We introduce the Local Clustering Decoder as a solution that simultaneously achieves the accuracy and speed requirements of a real-time decoding system.
Our decoder is implemented on FPGAs and exploits hardware parallelism to keep pace with the fastest qubit types.
It enables one million error-free quantum operations with 4x fewer physical qubits when compared to standard non-adaptive decoding.
arXiv Detail & Related papers (2024-11-15T16:43:59Z) - Demonstrating real-time and low-latency quantum error correction with superconducting qubits [52.08698178354922]
We demonstrate low-latency feedback with a scalable FPGA decoder integrated into a superconducting quantum processor.
We observe logical error suppression as the number of decoding rounds is increased.
The decoder throughput and latency developed in this work, combined with continued device improvements, unlock the next generation of experiments.
arXiv Detail & Related papers (2024-10-07T17:07:18Z) - Spatially parallel decoding for multi-qubit lattice surgery [0.10713888959520208]
Running quantum algorithms protected by quantum error correction requires a real time, classical decoder.<n>Most prior work on real time decoding has focused on an isolated logical qubit encoded in the surface code.<n>For surface code, quantum programs of utility will require multi-qubit interactions performed via lattice surgery.<n>A large merged patch can arise during lattice surgery -- possibly as large as the entire device.
arXiv Detail & Related papers (2024-03-03T00:17:13Z) - Modular decoding: parallelizable real-time decoding for quantum
computers [55.41644538483948]
Real-time quantum computation will require decoding algorithms capable of extracting logical outcomes from a stream of data generated by noisy quantum hardware.
We propose modular decoding, an approach capable of addressing this challenge with minimal additional communication and without sacrificing decoding accuracy.
We introduce the edge-vertex decomposition, a concrete instance of modular decoding for lattice-surgery style fault-tolerant blocks.
arXiv Detail & Related papers (2023-03-08T19:26:10Z) - Deep Quantum Error Correction [73.54643419792453]
Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing.
In this work, we efficiently train novel emphend-to-end deep quantum error decoders.
The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
arXiv Detail & Related papers (2023-01-27T08:16:26Z) - Error Correction Code Transformer [92.10654749898927]
We propose to extend for the first time the Transformer architecture to the soft decoding of linear codes at arbitrary block lengths.
We encode each channel's output dimension to high dimension for better representation of the bits information to be processed separately.
The proposed approach demonstrates the extreme power and flexibility of Transformers and outperforms existing state-of-the-art neural decoders by large margins at a fraction of their time complexity.
arXiv Detail & Related papers (2022-03-27T15:25:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.