Predictive Window Decoding for Fault-Tolerant Quantum Programs
- URL: http://arxiv.org/abs/2412.05115v1
- Date: Fri, 06 Dec 2024 15:17:18 GMT
- Title: Predictive Window Decoding for Fault-Tolerant Quantum Programs
- Authors: Joshua Viszlai, Jason D. Chadwick, Sarang Joshi, Gokul Subramanian Ravi, Yanjing Li, Frederic T. Chong,
- Abstract summary: Real-time decoding is a key ingredient in future fault-tolerant quantum systems.<n> Windowed decoding schemes require that some decoding tasks be delayed until others have completed.<n>We introduce a speculative window decoding scheme, taking inspiration from branch prediction in classical computer architecture.<n>We find that speculation reduces application runtimes by 40% on average compared to prior parallel window decoders.
- Score: 5.258081366634739
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Real-time decoding is a key ingredient in future fault-tolerant quantum systems, yet many decoders are too slow to run in real time. Prior work has shown that parallel window decoding schemes can scalably meet throughput requirements in the presence of increasing decoding times, given enough classical resources. However, windowed decoding schemes require that some decoding tasks be delayed until others have completed, which can be problematic during time-sensitive operations such as T gate teleportation, leading to suboptimal program runtimes. To alleviate this, we introduce a speculative window decoding scheme. Taking inspiration from branch prediction in classical computer architecture our decoder utilizes a light-weight speculation step to predict data dependencies between adjacent decoding windows, allowing multiple layers of decoding tasks to be resolved simultaneously. Through a state-of-the-art compilation pipeline and a detailed simulator, we find that speculation reduces application runtimes by 40% on average compared to prior parallel window decoders.
Related papers
- AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism [17.858104076062897]
Large language models (LLMs) are increasingly used for long-content generation.<n>We propose AdaDecode, which accelerates decoding without requiring auxiliary models or changes to the original model parameters.<n>AdaDecode consistently achieves superior decoding throughput with up to 1.73x speedup.
arXiv Detail & Related papers (2025-06-04T08:32:30Z) - 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) - RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression [68.31184784672227]
In modern applications such as autonomous driving, an overwhelming majority of videos serve as input for AI systems performing tasks.
It is therefore useful to optimize the encoder for a downstream task instead of for image quality.
Here, we address this challenge by controlling the Quantization Parameters (QPs) at the macro-block level to optimize the downstream task.
arXiv Detail & Related papers (2025-01-21T15:36:08Z) - Integrating Window-Based Correlated Decoding with Constant-Time Logical Gates for Large-Scale Quantum Computation [11.657137510701165]
One crucial issue of fault-tolerant quantum computing is reducing the overhead of implementing gates.
Recently proposed correlated decoding and algorithmic fault tolerance" achieve fast universality gates.
This approach is incompatible with window-based decoding, which is a natural requirement for handling large-scale circuits.
arXiv Detail & Related papers (2024-10-22T12:44:41Z) - Managing Classical Processing Requirements for Quantum Error Correction [0.36832029288386137]
We present a framework to reduce the number of hardware decoders and navigate the compute-performace trade-offs.<n>We propose efficient decoder scheduling policies that can reduce the number of hardware decoders required to run a program by up to 10X.
arXiv Detail & Related papers (2024-06-26T00:50:10Z) - Efficient Encoder-Decoder Transformer Decoding for Decomposable Tasks [53.550782959908524]
We introduce a new configuration for encoder-decoder models that improves efficiency on structured output and decomposable tasks.
Our method, prompt-in-decoder (PiD), encodes the input once and decodes the output in parallel, boosting both training and inference efficiency.
arXiv Detail & Related papers (2024-03-19T19:27:23Z) - Spatially parallel decoding for multi-qubit lattice surgery [0.10713888959520208]
Running quantum algorithms protected by quantum error correction requires a real time, classical decoder.
Most prior work on real time decoding has focused on an isolated logical qubit encoded in the surface code.
For surface code, quantum programs of utility will require multi-qubit interactions performed via lattice surgery.
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) - Speculative Contrastive Decoding [55.378200871224074]
Large language models(LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-optimal due to the exposure bias.
Inspired by speculative decoding and contrastive decoding, we introduce Speculative Contrastive Decoding(SCD), a straightforward yet powerful decoding approach.
arXiv Detail & Related papers (2023-11-15T14:15:30Z) - Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster [61.83949316226113]
FastCoT is a model-agnostic framework based on parallel decoding.
We show that FastCoT saves inference time by nearly 20% with only a negligible performance drop compared to the regular approach.
arXiv Detail & Related papers (2023-11-14T15:56:18Z) - 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) - Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability [90.17852645780945]
This paper proposes a novel rateless autoencoder (AE)-based code design suitable for decoding the transmitted message before the noisy codeword is fully received.
The proposed rateless AEs significantly outperform the conventional AE designs for scenarios where it is desirable to trade off reliability for lower decoding delay.
arXiv Detail & Related papers (2023-01-28T15:47:14Z) - Scalable surface code decoders with parallelization in time [8.28402656078529]
We introduce a sliding-window decoding scheme that provides fast classical processing for the surface code through parallelism.
Our scheme divides the syndromes in spacetime into overlapping windows along the time direction, which can be decoded in parallel with any inner decoder.
arXiv Detail & Related papers (2022-09-19T17:42:53Z) - Adversarial Neural Networks for Error Correcting Codes [76.70040964453638]
We introduce a general framework to boost the performance and applicability of machine learning (ML) models.
We propose to combine ML decoders with a competing discriminator network that tries to distinguish between codewords and noisy words.
Our framework is game-theoretic, motivated by generative adversarial networks (GANs)
arXiv Detail & Related papers (2021-12-21T19:14:44Z)
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.