Quantum Annealing QUBO Optimization via Sparse Coded Dictionary
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying maximally repeating patterns in data using quantum processors are inefficient due to the complexity of optimizing objective functions across interconnected qubits, particularly in adiabatic quantum computation and quantum annealing, where the connectivity structure of the quantum processor is not fully utilized.
Innovation Solution
A method involving Hierarchical Deep Learning (HDL) is employed, where a quantum processor is used to minimize a quadratic unconstrained binary optimization (QUBO) problem by casting weights as Boolean variables and iteratively optimizing both the weights and dictionary values, respecting the connectivity structure of the quantum processor, using adiabatic quantum computation or quantum annealing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If quantum processors are used to optimize objective functions for pattern recognition, then pattern identification capability is improved, but computational complexity and optimization difficulty increase due to interconnected qubit relationships
Solution Approach 1:
The patent segments the optimization problem by introducing a sparsely connected subset of qubits that interact only with a small fraction of total qubits. This segmentation allows the complex objective function to be divided into manageable parts, where the sparsely connected qubits handle the most critical optimization tasks while remaining qubits provide computational support, thereby reducing overall optimization complexity while maintaining pattern recognition capability.
Solution Approach 2:
The patent introduces an intermediary layer of sparsely connected qubits that mediate between the input data representation and the final optimization outcome. These intermediary qubits selectively interact with specific subsets of qubits, acting as a buffer that simplifies the optimization process by filtering out redundant interactions and focusing computational resources on the most significant pattern recognition tasks.
2Productivity
If adiabatic quantum computation is used for optimization, then quantum annealing capability is improved, but the connectivity structure of the quantum processor is not fully utilized leading to inefficient computation
Solution Approach 1:
The patent applies local quality by creating sparsely connected qubits with specific interaction patterns that match the local connectivity structure of the quantum processor. Instead of requiring dense connectivity, the method optimizes the local interaction properties of qubits, allowing each qubit to engage in targeted interactions that maximize annealing efficiency while respecting the hardware's inherent connectivity constraints.
Solution Approach 2:
The patent introduces dynamic adaptability in the quantum annealing process by allowing the sparsely connected qubits to dynamically adjust their interaction strengths and connectivity patterns during the annealing schedule. This dynamic behavior enables the system to adapt to the specific connectivity structure of the quantum processor, maximizing the utilization of available quantum pathways while maintaining annealing capability.
3Measurement precision
If the objective function is optimized across all qubits, then comprehensive pattern analysis is improved, but computation time increases due to the need to consider all qubit interactions
Solution Approach 1:
The patent extracts the most critical optimization tasks from the full set of qubit interactions by identifying and isolating the sparsely connected qubits that provide the most valuable pattern recognition insights. These extracted qubits are then optimized independently or in small groups, allowing comprehensive pattern analysis to be achieved through a subset of qubits rather than requiring all qubits to participate in every optimization step.
Solution Approach 2:
The patent applies partial action by optimizing only the essential qubit interactions necessary for pattern recognition rather than exhaustively optimizing all possible qubit combinations. The sparsely connected qubits perform partial optimizations that capture the most significant patterns, while the remaining qubits provide computational support without requiring full participation in every optimization cycle, thereby reducing computation time while maintaining analysis comprehensiveness.
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 effectively identifies maximally repeating patterns in data by leveraging the quantum processor's connectivity structure, enhancing the efficiency of pattern recognition and feature generation in machine learning applications.
Implementation Method 1
A quantum processor may take the form of a superconducting quantum processor. A superconducting quantum processor may include a number of qubits and associated local bias devices... Adiabatic quantum computation typically involves evolving a system from a known initial Hamiltonian to a final Hamiltonian by gradually changing the Hamiltonian.
Implementation Method 2
Quantum annealing is a computation method that may be used to find a low-energy state, typically preferably the ground state, of a system. Similar in concept to classical annealing, the method relies on the underlying principle that natural systems tend towards lower energy states because lower energy states are more stable.
Implementation Method 3
Some embodiments implement multiple Josephson junctions connected either in series or in parallel (i.e., a compound Josephson junction) and some embodiments implement multiple superconducting loops.
Data Source
AI summary
Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.