Scalable Quantum Error Correction for Surface Codes using FPGA
- URL: http://arxiv.org/abs/2301.08419v2
- Date: Mon, 15 May 2023 05:56:12 GMT
- Title: Scalable Quantum Error Correction for Surface Codes using FPGA
- Authors: Namitha Liyanage, Yue Wu, Alexander Deters and Lin Zhong
- Abstract summary: A fault-tolerant quantum computer must decode and correct errors faster than they appear.
We report a distributed version of the Union-Find decoder that exploits parallel computing resources for further speedup.
The implementation employs a scalable architecture called Helios that organizes parallel computing resources into a hybrid tree-grid structure.
- Score: 67.74017895815125
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A fault-tolerant quantum computer must decode and correct errors faster than
they appear. The faster errors can be corrected, the more time the computer can
do useful work. The Union-Find (UF) decoder is promising with an average time
complexity slightly higher than $O(d^3)$. We report a distributed version of
the UF decoder that exploits parallel computing resources for further speedup.
Using an FPGA-based implementation, we empirically show that this distributed
UF decoder has a sublinear average time complexity with regard to $d$, given
$O(d^3)$ parallel computing resources. The decoding time per measurement round
decreases as $d$ increases, a first time for a quantum error decoder. The
implementation employs a scalable architecture called Helios that organizes
parallel computing resources into a hybrid tree-grid structure. We are able to
implement $d$ up to 21 with a Xilinx VCU129 FPGA, for which an average decoding
time is 11.5 ns per measurement round under phenomenological noise of 0.1\%,
significantly faster than any existing decoder implementation. Since the
decoding time per measurement round of Helios decreases with $d$, Helios can
decode a surface code of arbitrarily large $d$ without a growing backlog.
Related papers
- Let the Code LLM Edit Itself When You Edit the Code [50.46536185784169]
underlinetextbfPositional textbfIntegrity textbfEncoding (PIE)
PIE reduces computational overhead by over 85% compared to the standard full recomputation approach.
Results demonstrate that PIE reduces computational overhead by over 85% compared to the standard full recomputation approach.
arXiv Detail & Related papers (2024-07-03T14:34:03Z) - Ambiguity Clustering: an accurate and efficient decoder for qLDPC codes [0.0]
We introduce Ambiguity Clustering (AC), an algorithm which seeks to divide measurement data into clusters which are decoded independently.
AC is between one and three orders of magnitude faster than BP-OSD with no reduction in logical fidelity.
Our CPU implementation of AC is already fast enough to decode the 144-qubit Gross code in real time for neutral atom and trapped ion systems.
arXiv Detail & Related papers (2024-06-20T17:39:31Z) - FPGA-based Distributed Union-Find Decoder for Surface Codes [3.780617572622938]
A fault-tolerant quantum computer must decode and correct errors faster than they appear to prevent exponential slowdown due to error correction.
We report a distributed version of the Union-Find decoder that exploits parallel computing resources for further speedup.
arXiv Detail & Related papers (2024-03-20T13:36:59Z) - The closed-branch decoder for quantum LDPC codes [0.0]
Real-time decoding is a necessity for implementing arbitrary quantum computations on the logical level.
We present a new decoder for Quantum Low Density Parity Check (QLDPC) codes, named the closed-branch decoder.
arXiv Detail & Related papers (2024-02-02T16:22:32Z) - Bit-flipping Decoder Failure Rate Estimation for (v,w)-regular Codes [84.0257274213152]
We propose a new technique to provide accurate estimates of the DFR of a two-iterations (parallel) bit flipping decoder.
We validate our results, providing comparisons of the modeled and simulated weight of the syndrome, incorrectly-guessed error bit distribution at the end of the first iteration, and two-itcrypteration Decoding Failure Rates (DFR)
arXiv Detail & Related papers (2024-01-30T11:40:24Z) - TCNCA: Temporal Convolution Network with Chunked Attention for Scalable
Sequence Processing [52.64837396100988]
MEGA is a recent transformer-based architecture, which utilizes a linear recurrent operator whose parallel computation, based on the FFT, scales as $O(LlogL)$, with $L$ being the sequence length.
We build upon their approach by replacing the linear recurrence with a special temporal convolutional network which permits larger receptive field size with shallower networks, and reduces the computational complexity to $O(L)$.
We evaluate TCNCA on EnWik8 language modeling, long-range-arena (LRA) sequence classification, as well as a synthetic reasoning benchmark associative recall.
arXiv Detail & Related papers (2023-12-09T16:12:25Z) - Parallel window decoding enables scalable fault tolerant quantum
computation [2.624902795082451]
We present a methodology that parallelizes the decoding problem and achieves almost arbitrary syndrome processing speed.
Our parallelization requires some classical feedback decisions to be delayed, leading to a slow-down of the logical clock speed.
Using known auto-teleportation gadgets the slow-down can be eliminated altogether in exchange for increased qubit overhead.
arXiv Detail & Related papers (2022-09-18T12:37:57Z) - Rapid Person Re-Identification via Sub-space Consistency Regularization [51.76876061721556]
Person Re-Identification (ReID) matches pedestrians across disjoint cameras.
Existing ReID methods adopting real-value feature descriptors have achieved high accuracy, but they are low in efficiency due to the slow Euclidean distance computation.
We propose a novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by 0.25$ times.
arXiv Detail & Related papers (2022-07-13T02:44:05Z) - Instantaneous Grammatical Error Correction with Shallow Aggressive
Decoding [57.08875260900373]
We propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC)
SAD aggressively decodes as many tokens as possible in parallel instead of always decoding only one token in each step to improve computational parallelism.
Experiments in both English and Chinese GEC benchmarks show that aggressive decoding could yield the same predictions but with a significant speedup for online inference.
arXiv Detail & Related papers (2021-06-09T10:30:59Z) - Optimal local unitary encoding circuits for the surface code [0.2770822269241973]
The surface code is a leading candidate quantum error correcting code, owing to its high threshold.
We present an optimal local unitary encoding circuit for the planar surface code.
We also show how our encoding circuit for the planar code can be used to prepare fermionic states in the compact mapping.
arXiv Detail & Related papers (2020-02-02T11:09:46Z)
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.