MULTI-PASS SYSTEM FOR EMULATING SAMPLING OF A PLURALITY OF QUBITS AND METHODS FOR USE THEREWITH
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.
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.