US2021365828A1PendingUtilityA1

Multi-pass system for emulating sampling of a plurality of qubits and methods for use therewith

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Assignee: BEIT INCPriority: Jun 21, 2019Filed: Aug 4, 2021Published: Nov 25, 2021
Est. expiryJun 21, 2039(~12.9 yrs left)· nominal 20-yr term from priority
Inventors:Marcin BrianskiWitold JarnickiLukasz Czerwinski
G06F 17/11G06N 10/60G06N 10/80G06N 5/01G06F 9/455G06F 15/82G06F 15/80G06F 9/45508G06N 10/00G06N 10/40B82Y 10/00
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Claims

Abstract

Sampling of a plurality of qubits arranged in a grid topology with N columns is emulated by: producing final weights and variable assignments for the N columns based on N iterative passes through the grid topology, wherein the final weights and variable assignments for a selected column of the N columns is based on preliminary weights and variable assignments generated for a column adjacent to the selected column of the N columns; and emulating a sample the plurality of qubits based on the final weights and variable assignments for each of the N columns.

Claims

exact text as granted — not AI-modified
What is claimed is: 
     
         1 . A system for emulating sampling of a plurality of qubits arranged in a grid topology with N columns, the system comprising:
 a memory that stores operational instructions; and   at least one classical processor that is configured by the operational instructions to perform operations, the operations including:   producing final weights and variable assignments for the N columns based on N iterative passes through the grid topology, wherein the final weights and variable assignments for a selected column of the N columns is based on preliminary weights and variable assignments generated for a column adjacent to the selected column of the N columns;   wherein the sampling of the plurality of qubits is emulated by a sample based on the final weights and variable assignments for each of the N columns.   
     
     
         2 . The system of  claim 1 , wherein a first iterative pass of the N iterative passes generates the final weights and variable assignments for an Nth column of the N columns, based on the preliminary weights and variable assignments generated for a (N−1)st column of the N columns. 
     
     
         3 . The system of  claim 2 , wherein the first iterative pass of the N iterative passes generates preliminary weights and variable assignments for all columns of the N columns in a range between a second column of the N columns and the (N−1)st column of the N columns. 
     
     
         4 . The system of  claim 2 , wherein the first iterative pass of the N iterative passes generates preliminary weights and variable assignments for a first column of the N columns, based on based on null weights corresponding to a null column adjacent to the first column of the N columns. 
     
     
         5 . The system of  claim 2 , wherein the last iterative pass of the N iterative passes generates the final weights and variable assignments for a first column of the N columns, based on the final weights and variable assignments for a second column of the N columns. 
     
     
         6 . The system of  claim 1 , wherein each of the N iterative passes generates the final weights and variable assignments for a corresponding one of the N columns. 
     
     
         7 . The system of  claim 1 , wherein a pth iterative pass of the N iterative passes generates the final weights and variable assignments for an (N−p+1)st column of the N columns, based on the preliminary weights and variable assignments generated for a (N−p)th column of the N columns. 
     
     
         8 . The system of  claim 7 , wherein the pth iterative pass of the N iterative passes generates preliminary weights and variable assignments for all columns of the N columns in a range between a second column of the N columns and the (N−p)th column of the N columns. 
     
     
         9 . The system of  claim 1 , wherein the grid topology corresponds to a quadratic unconstrained binary optimization (QUBO) instance. 
     
     
         10 . The system of  claim 1 , wherein the sample corresponds to a Boltzmann distribution. 
     
     
         11 . A method for emulating sampling of a plurality of qubits arranged in a grid topology with N columns, the method comprising:
 producing final weights and variable assignments for the N columns based on N iterative passes through the grid topology, wherein the final weights and variable assignments for a selected column of the N columns is based on preliminary weights and variable assignments generated for a column adjacent to the selected column of the N columns; and   emulating a sample the plurality of qubits based on the final weights and variable assignments for each of the N columns.   
     
     
         12 . The method of  claim 11 , wherein a first iterative pass of the N iterative passes generates the final weights and variable assignments for an Nth column of the N columns, based on the preliminary weights and variable assignments generated for a (N−1)st column of the N columns. 
     
     
         13 . The method of  claim 12 , wherein the first iterative pass of the N iterative passes generates preliminary weights and variable assignments for all columns of the N columns in a range between a second column of the N columns and the (N−1)st column of the N columns. 
     
     
         14 . The method of  claim 12 , wherein the first iterative pass of the N iterative passes generates preliminary weights and variable assignments for a first column of the N columns, based on based on null weights corresponding to a null column adjacent to the first column of the N columns. 
     
     
         15 . The method of  claim 12 , wherein the last iterative pass of the N iterative passes generates the final weights and variable assignments for a first column of the N columns, based on the final weights and variable assignments for a second column of the N columns. 
     
     
         16 . The method of  claim 11 , wherein each of the N iterative passes generates the final weights and variable assignments for a corresponding one of the N columns. 
     
     
         17 . The method of  claim 11 , wherein a pth iterative pass of the N iterative passes generates the final weights and variable assignments for an (N−p+1)st column of the N columns, based on the preliminary weights and variable assignments generated for a (N−p)th column of the N columns. 
     
     
         18 . The method of  claim 17 , wherein the pth iterative pass of the N iterative passes generates preliminary weights and variable assignments for all columns of the N columns in a range between a second column of the N columns and the (N−p)th column of the N columns. 
     
     
         19 . The method of  claim 11 , wherein the grid topology corresponds to a quadratic unconstrained binary optimization (QUBO) instance. 
     
     
         20 . The method of  claim 11 , wherein the sample corresponds to a Boltzmann distribution.

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