Analog Quantum Processor Qubit Coupling

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Solution Overview

Problem

Current approaches to quantum computation, such as the circuit model, require long qubit coherence times and are not as robust as adiabatic or quantum annealing methods, which do not rely on quantum gates and circuits, but these methods face challenges in scalability and efficiency in solving complex computational problems.

Innovation Solution

A system comprising a computer with an analog processor, specifically a quantum processor with interconnected qubits and coupling devices, that uses adiabatic or annealing evolution to solve computational problems by physically evolving the quantum state, allowing for the mapping of complex problems onto a topological representation solvable by the analog processor.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the circuit model of quantum computation is used, then quantum gates and circuits can be implemented, but long qubit coherence times are required and robustness is reduced

Engineering Contradiction:
ImproverobustnessVSAvoidqubit coherence time
Core Design Contradiction:
ReliabilityVSDuration of action of moving object

Solution Approach 1:

The patent replaces the circuit model approach (which requires long coherence times) with an analog processing approach using adiabatic or quantum annealing evolution. This substitution eliminates the need for long qubit coherence times by using the natural physical evolution of the quantum system to solve computational problems through Hamiltonian transformation rather than sequential gate operations.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If adiabatic or quantum annealing methods are used, then robustness is improved and long coherence times are not required, but scalability and efficiency in solving complex computational problems are reduced

Engineering Contradiction:
ImproverobustnessVSAvoidcomputational efficiency
Core Design Contradiction:
ReliabilityVSProductivity

Solution Approach 1:

The patent creates a universal analog processor that can handle multiple types of computational problems by mapping them onto a common topological representation. The system uses a graph embedding approach where different computational problems can be represented as graphs on various surfaces (sphere, torus, higher-genus surfaces), providing a universal framework that maintains both robustness and computational efficiency across diverse problem types.

Inventive Principle:
Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent transforms computational problems by changing their representation parameters - mapping problems onto different topological surfaces and using graph embeddings with varying properties. This parameter transformation allows the system to efficiently handle complex computational problems by selecting appropriate topological representations that optimize both robustness and computational efficiency.

Inventive Principle:
Principle #35Parameter changes

3Adaptability or versatility

If computational problems are mapped onto topological representations, then complex problems can be solved by analog processors, but system complexity increases

Engineering Contradiction:
Improveproblem-solving capabilityVSAvoidsystem complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the problem-solving process into distinct stages: problem formulation, graph embedding on topological surfaces, Hamiltonian construction, and quantum evolution. This segmentation allows each component to be optimized independently while maintaining overall system versatility, reducing the perceived complexity through structured modularization of the computational approach.

Inventive Principle:
Principle #1Segmentation

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enables the efficient solution of complex computational problems without relying on long qubit coherence times, enhancing the robustness and scalability of quantum computation by physically evolving the quantum state to find solutions.

Implementation Method 1

uses adiabatic or annealing evolution to solve computational problems by physically evolving the quantum state

Methodology Applied
Scientific EffectAdiabatic evolution: Adiabatic Heating

Implementation Method 2

uses adiabatic or annealing evolution to solve computational problems by physically evolving the quantum state

Methodology Applied
Scientific EffectQuantum annealing: Annealing

Implementation Method 3

Superconducting qubits are a type of superconducting quantum device that can be included in a superconducting integrated circuit

Methodology Applied
Scientific EffectSuperconductivity: Superconductivity

Data Source

PatentEP2263166B1Systems, devices, and methods for analog processing
Publication Date: 2020.02.19 D WAVE SYSTEMS INC
  • EP2263166B1 patent drawingFigure 1A
  • EP2263166B1 patent drawingFigure 1B
  • EP2263166B1 patent drawingFigure 2A

AI summary

A system may include first and second qubits that cross one another and a first coupler having a perimeter that encompasses at least a part of the portions of the first and second qubits, the first coupler being operable to ferromagnetically or anti-ferromagnetically couple the first and the second qubits together. A multi-layered computer chip may include a first plurality N of qubits laid out in a first metal layer, a second plurality M of qubits laid out at least partially in a second metal layer that cross each of the qubits of the first plurality of qubits, and a first plurality N times M of coupling devices that at least partially encompasses an area where a respective pair of the qubits from the first and the second plurality of qubits cross each other.