Machine-Learned Integer Ranges for Lower-Qubit One-Hot Encoding
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Solution Overview
Problem
One-hot encoding in quantum computing systems consumes a large number of qubits, particularly for integer variables, leading to inefficiency and resource overprovisioning, especially when matching Hamiltonian to a non-fully connected quantum computing topology.
Innovation Solution
A machine learning model is trained on historical data to predict distributions and ranges for integer variables, allowing for focused one-hot encoding that reduces the number of qubits required by identifying likely ranges within integer variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If one-hot encoding is used to represent integer variables in QUBO models, then the encoding is simple and straightforward, but the number of qubits required increases proportionally to the integer value range
Solution Approach 1:
The patent applies segmentation by dividing the integer variable range into multiple segments or groups. Instead of creating one binary variable for each integer value (0 to N-1), the range is partitioned into segments, and one-hot encoding is applied within each segment. This reduces the total number of binary variables needed while maintaining the ability to represent all integer values through segment identification and offset calculation.
Solution Approach 2:
The patent introduces an additional dimensional structure to the encoding by organizing binary variables into hierarchical levels or dimensions. Rather than a flat one-hot encoding where each integer gets its own variable, the encoding is structured across multiple dimensions (e.g., most significant segment, least significant segment, offset within segment), reducing the total variable count while preserving information.
2Reliability
If one-hot encoding creates binary variables for each integer value, then the encoding is complete and accurate, but the problem size scales poorly with larger integer ranges
Solution Approach 1:
The patent segments the integer variable domain into multiple ranges or buckets. Each segment is represented by a subset of binary variables, and the complete representation is constructed by combining segment selection with offset within segment. This segmentation maintains encoding accuracy while preventing problem size from scaling linearly with the full integer range.
Solution Approach 2:
The patent applies partial one-hot encoding by not creating binary variables for all possible integer values, but only for a subset or segments of the range. By using additional structural information (segment identifiers, offsets, or hierarchical relationships), the encoding achieves completeness without requiring variables for every single integer value, thus reducing problem size while maintaining reliability.
Data Source
AI summary
A machine learning model is trained, using historical data, to generate distributions for integer variables of a problem. When a new problem or problem instance is presented, the model is used to predict a distribution for each of the integer variables. A range is determined from each of the distributions. One-hot encoded binary variables are generated from the ranges. This reduces the number of qubits needed to one-hot encode the problem instance.