Stochastic matrix multiplier using superconductor single-flux-quantum circuit

Abstract A superconducting single-flux-quantum (SFQ) circuit is suitable for stochastic computation that represents information in terms of the probability of ‘1’s in a finite-bit-length number train, due to its high-speed operation. In this paper, we demonstrate a matrix multiplier based on the SFQ logic circuits and superconductive random number generators (SRNGs). The stochastic multiplier required for the matrix product calculation is implemented using an AND gate. The stochastic adder is implemented by a multiplexer where the random number train generated by the SRNG is utilized as a control signal. We implemented <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>×</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> matrix multiplier targeting a frequency of 50 GHz, using the 10 kA cm −2 Nb 4-layer superconductor circuit fabrication process. Numerical calculation results show that the designed stochastic matrix multiplier can multiply with a computational error of 1.12% when the input stochastic numbers are 1000-bit number trains. In the measurements, the input stochastic numbers were also generated by SRNGs, and the operation of the test circuit was verified. We experimentally demonstrated the correct operation of the matrix multiplier, and the obtained calculation errors agreed with theoretical values. To the best of our knowledge, this is the first demonstration of stochastic arithmetic operation using SFQ circuits.

Authors

Institutions

Publication Details

Journal
Superconductor Science and Technology
Published
2024-10-07
DOI
https://doi.org/10.1088/1361-6668/ad83fe
Citations
5
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
1.74
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Stochastic matrix multiplier using superconductor single-flux-quantum circuit

Yuki Yamanashi, Nobuyuki Yoshikawa, Hijiri Okumura
5 citations
Superconductor Science and Technology
Quantum Computing Algorithms and Architecture
1.74
article

Stochastic matrix multiplier using superconductor single-flux-quantum circuit

Yuki Yamanashi, Nobuyuki Yoshikawa, Hijiri Okumura
article en
5 citations

Abstract

Abstract A superconducting single-flux-quantum (SFQ) circuit is suitable for stochastic computation that represents information in terms of the probability of ‘1’s in a finite-bit-length number train, due to its high-speed operation. In this paper, we demonstrate a matrix multiplier based on the SFQ logic circuits and superconductive random number generators (SRNGs). The stochastic multiplier required for the matrix product calculation is implemented using an AND gate. The stochastic adder is implemented by a multiplexer where the random number train generated by the SRNG is utilized as a control signal. We implemented <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>×</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> matrix multiplier targeting a frequency of 50 GHz, using the 10 kA cm −2 Nb 4-layer superconductor circuit fabrication process. Numerical calculation results show that the designed stochastic matrix multiplier can multiply with a computational error of 1.12% when the input stochastic numbers are 1000-bit number trains. In the measurements, the input stochastic numbers were also generated by SRNGs, and the operation of the test circuit was verified. We experimentally demonstrated the correct operation of the matrix multiplier, and the obtained calculation errors agreed with theoretical values. To the best of our knowledge, this is the first demonstration of stochastic arithmetic operation using SFQ circuits.

Superconductor Science and TechnologyVol. 37(11)
Yokohama National University (JP)
Affordable and clean energy
Openalex Percentile: Top 13%
Quantum Computing Algorithms and Architecture
1.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.