Hierarchical decoding to reduce hardware requirements for quantum
computing
- URL: http://arxiv.org/abs/2001.11427v1
- Date: Thu, 30 Jan 2020 16:09:51 GMT
- Title: Hierarchical decoding to reduce hardware requirements for quantum
computing
- Authors: Nicolas Delfosse
- Abstract summary: We propose a fault-tolerant quantum computing architecture based on surface codes with a cheap hard-decision decoder.
We obtain a 1,500x reduction in bandwidth and decoding hardware thanks to the lazy decoder.
Our simulations show a 10x speed-up of the Union-Find decoder and a 50x speed-up of the Minimum Weight Perfect Matching decoder.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Extensive quantum error correction is necessary in order to scale quantum
hardware to the regime of practical applications. As a result, a significant
amount of decoding hardware is necessary to process the colossal amount of data
required to constantly detect and correct errors occurring over the millions of
physical qubits driving the computation. The implementation of a recent highly
optimized version of Shor's algorithm to factor a 2,048-bits integer would
require more 7 TBit/s of bandwidth for the sole purpose of quantum error
correction and up to 20,000 decoding units. To reduce the decoding hardware
requirements, we propose a fault-tolerant quantum computing architecture based
on surface codes with a cheap hard-decision decoder, the lazy decoder, combined
with a sophisticated decoding unit that takes care of complex error
configurations. Our design drops the decoding hardware requirements by several
orders of magnitude assuming that good enough qubits are provided. Given qubits
and quantum gates with a physical error rate $p=10^{-4}$, the lazy decoder
drops both the bandwidth requirements and the number of decoding units by a
factor 50x. Provided very good qubits with error rate $p=10^{-5}$, we obtain a
1,500x reduction in bandwidth and decoding hardware thanks to the lazy decoder.
Finally, the lazy decoder can be used as a decoder accelerator. Our simulations
show a 10x speed-up of the Union-Find decoder and a 50x speed-up of the Minimum
Weight Perfect Matching decoder.
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