# US 2020/0401926 A1 · Multi-pass system for emulating a quantum computer and methods for use therewith

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- Number: US 2020/0401926 A1
- Document ID: US20200401926A1
- Status: Published application
- Kind: A1
- Application number: 16/835,914
- Filed: 2020-03-31
- Published: 2020-12-24
- Earliest claimed: 2019-06-21
- Assignees: BEIT Inc.
- Applicants: Beit Inc.
- Inventors: Marcin Brianski; Witold Jarnicki; Lukasz Czerwinski
- Web page: https://patentgrep.com/patent/US20200401926A1

## CPC

- G06F 9/455: Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F 17/11: for solving equations {, e.g. nonlinear equations, general mathematical optimization problems (optimization specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 10/60: Quantum algorithms, e.g. based on quantum optimisation, quantum Fourier or Hadamard transforms
- G06N 10/80: Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computers; Platforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A system is presented for emulating sampling of a quantum computer having a plurality of qubits arranged in a grid topology with N columns. The system includes a classical processor that is configured by operational instructions to perform operations that include producing final weights and variable assignments for the N columns based on N iterative passes through the grid topology, wherein each of the N iterative passes generates preliminary weights and variable assignments for a corresponding subset of the N columns, wherein the preliminary weights and variable assignments for a selected column of the corresponding subset based on the preliminary weights and variable assignments generated for a column adjacent to the selected column of the corresponding subset, and 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.

## Claims

### Claim 1

A system for emulating sampling of a quantum computer having a plurality of qubits arranged in a grid topology with N columns, the system comprising:
a memory that stores operational instructions;
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 each of the N iterative passes generates preliminary weights and variable assignments for a corresponding subset of the N columns, and wherein the preliminary weights and variable assignments for a selected column of the corresponding subset of the N columns is based on the preliminary weights and variable assignments generated for a column adjacent to the selected column of the corresponding subset of the N columns;
wherein the sampling of the quantum computer having the plurality of qubits is emulated by a sample based on the final weights and variable assignments for each of the N columns.

### Claim 2 (depends on 1)

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.

### Claim 3 (depends on 2)

The system of claim 2, wherein the corresponding subset of the N columns associated with the first iterative pass of the N iterative passes includes 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.

### Claim 4 (depends on 2)

The system of claim 2, wherein the first iterative pass of the N iterative passes generates the preliminary weights and variable assignments for a first column of the N columns, based on null weights corresponding to a null column.

### Claim 5 (depends on 2)

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.

### Claim 6 (depends on 1)

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.

### Claim 7 (depends on 1)

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.

### Claim 8 (depends on 7)

The system of claim 7, wherein the corresponding subset of the N columns associated with the pth iterative pass of the N iterative passes includes 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.

### Claim 9 (depends on 1)

The system of claim 1, wherein the grid topology corresponds to a quadratic unconstrained binary optimization (QUBO) model.

### Claim 10 (depends on 1)

The system of claim 1, wherein the sample corresponds to a Boltzmann distribution.

### Claim 11

A system for emulating sampling of a quantum computer having a plurality of qubits arranged in a grid topology with N columns, the system comprising:
a memory that stores operational instructions;
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 each of the N iterative passes generates preliminary weights and variable assignments for a number of columns of the N columns based on preliminary weights and variable assignments generated for an adjacent column for each of the number of columns, wherein final weights and variable assignments are generated for a final column of the N columns for each of the N iterative passes based on the preliminary weights and variable assignments generated for a column of the N columns adjacent to the final column of the of the N columns, wherein the final weights and variable assignments for the final column of the N columns are used in a next successive pass of the N iterative passes to reduce the number of columns of the N columns where the preliminary weights and variable assignments are regenerated until the N iterative passes are complete and final weights and variable assignments for each of the N columns have been generated;
wherein the sampling of the quantum computer having the plurality of qubits is emulated by a sample based on the final weights and variable assignments for each of the N columns.

### Claim 12 (depends on 11)

The system of claim 11, wherein producing the preliminary weights and variable assignments for the N columns based on the N iterative passes includes:
performing a first pass of the N iterative passes through the grid topology wherein a Nth column of the N columns corresponds to the final column, and wherein the number of columns of the N columns where preliminary weights and variable assignments are generated is equal to N−1.

### Claim 13 (depends on 12)

The system of claim 12, wherein producing the preliminary weights and variable assignments for the N columns based on the N iterative passes includes:
performing N−1 other passes of the N iterative passes through the grid topology by:
(a) setting p=2;
(b) performing an pth pass through the grid topology, wherein the (N−p+1)th column of the N columns corresponds to the final column, and wherein the number of columns of the N columns where preliminary weights and variable assignments are regenerated is equal to (N−p);
(c) incrementing p; and
(d) repeating steps (b) and (c) until p=N.

### Claim 14 (depends on 12)

The system of claim 12, wherein the first pass of the N iterative passes through the grid topology includes generating the preliminary weights and variable assignments for a first column of the N columns based on null weights corresponding to a null column.

### Claim 15 (depends on 11)

The system of claim 11, wherein the grid topology corresponds to a quadratic unconstrained binary optimization (QUBO) model.

### Claim 16 (depends on 11)

The system of claim 11, wherein the sample corresponds to a Boltzmann distribution.

### Claim 17

A method for emulating sampling of a quantum computer having a plurality of qubits arranged in a grid topology with N columns, the method comprising:
performing a first iterative pass through the grid topology to produce final weights and variable assignments for a Nth column of the N columns, by generating preliminary weights and variable assignments for N−1 columns of the N columns based on preliminary weights and variable assignments generated for an adjacent column for each of the N−1 columns, wherein the final weights and variable assignments are generated for the Nth column based on the preliminary weights and variable assignments generated for an (N−1)st column of the N columns adjacent to the Nth column;
performing N−1 other iterative passes of the N iterative passes through the grid topology by:
(a) setting p=2;
(b) performing an pth pass through the grid topology, to produce final weights and variable assignments for the (N−p+1)st column of the N columns, by regenerating preliminary weights and variable assignments for N−p columns of the N columns based on preliminary weights and variable assignments regenerated for an adjacent column for each of the N−p columns, wherein the final weights and variable assignments are generated for the (N−p+1)st column based on the preliminary weights and variable assignments regenerated for an (N−p)th column of the N columns adjacent to the (N−p+1)st column and the final weights and variable assignments generated for the (N−p+2)nd column;
(c) incrementing p;
(d) repeating steps (b) and (c) until p=N; and
(e) performing a final pass through the grid topology, to produce final weights and variable assignments for a first column of the N columns, based on the final weights and variable assignments generated for a second column of the N columns;
wherein the sampling of the quantum computer having the plurality of qubits is emulated by a sample based on the final weights and variable assignments for each of the N columns.

### Claim 18 (depends on 17)

The method of claim 17, wherein the first iterative pass of the N iterative passes through the grid topology includes generating the preliminary weights and variable assignments for the first column of the N columns based on null weights corresponding to a null column adjacent to the first column of the N columns.

### Claim 19 (depends on 17)

The method of claim 17, wherein the grid topology corresponds to a quadratic unconstrained binary optimization (QUBO) model.

### Claim 20 (depends on 17)

The method of claim 17, wherein the sample corresponds to a Boltzmann distribution.