Transformation apparatus, decision apparatus, quantum computation apparatus, and quantum machine learning system

Patent metadata

Field	Value
Publication number	US-2019197426-A1
Application number	US-201816168014-A
Country	US
Kind code	A1
Filing date	Oct 23, 2018
Priority date	Oct 24, 2017
Publication date	Jun 27, 2019
Grant date	—

Abstract

First, a dual basis B − of B of modulo N is obtained by classical computation. Next, quantum computation is performed using the periodicity of a point sequence included in a sum set of sets obtained by parallel translation of a lattice L(B) by integral multiples of t for a plurality of integers, and an n-dimensional r j =(r j1 , . . . , r jn ) and r j0 are obtained for j=1 , . . . , m. Subsequently, by classical computation, the closest vector r j (c) =(r j1 (c) , . . . , r jn (c) )∈L(B − ) of the n-dimensional vector r j , and the difference vector r j (d) =r j −r j (c) =(r j1 (d) , . . . , r jn (d) ) corresponding to r j (c) are obtained.

First claim

1 . A transformation apparatus receiving inputs of a basis B and a target vector t, and, outputting {r j1 (c) , r j1 (d) , r j0 } and m, M and N that satisfy

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Assignees

• Nippon Telegraph & Telephone

Inventors

• Kawano Yasuhito
• Sekigawa Hiroshi

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N3/048

  Activation functions · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title