Quantum Feature Encoding With QIP Loss for Information Preservation
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Solution Overview
Problem
Existing quantum encoding strategies, such as phase and amplitude encoding, fail to preserve the fundamental properties or characteristics of classical data in quantum form, leading to a quantum information gap (QIG) that complicates the learning process of quantum machine learning models.
Innovation Solution
A quantum information preserving (QIP) loss function is introduced to minimize the quantum information gap by transforming classical features into quantum features, projecting them into logits, and calculating a loss function that minimizes the average Kullback-Leibler divergence between projected feature distributions, ensuring information preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If phase and amplitude encoding are used to transform classical data into quantum states, then quantum computing operations can be performed, but the fundamental properties or characteristics of the classical data are not preserved in the quantum form
Solution Approach 1:
The patent introduces a quantum information preserving loss function that provides feedback during the training process. This loss function measures the divergence between classical and quantum feature distributions and adjusts the encoding parameters accordingly, enabling the system to learn and preserve classical data characteristics while maintaining quantum computational capabilities.
Solution Approach 2:
The patent employs parameterized quantum circuits with adjustable encoding parameters that are optimized through training. By changing these parameters dynamically during the learning process, the system adapts the quantum encoding to preserve classical data characteristics, transforming the static encoding problem into a dynamic optimization problem.
2Ease of manufacture
If existing quantum encoding strategies are used, then quantum machine learning algorithms can be implemented, but the learning process is complicated due to the quantum information gap
Solution Approach 1:
The quantum information preserving loss function provides a feedback mechanism that guides the training process. By continuously measuring the information preservation quality and adjusting encoding parameters accordingly, the system simplifies the learning process despite the inherent quantum information gap, making quantum machine learning more manageable.
3Speed
If classical data is transformed into quantum states using standard encoding, then quantum processing can proceed, but information preservation of visual features is not ensured
Solution Approach 1:
The loss function provides feedback that specifically targets information preservation in visual features. During training, the system adjusts encoding parameters based on the measured divergence between classical and quantum representations, ensuring that visual feature information is preserved while maintaining quantum processing speed.
Solution Approach 2:
The patent uses parameterized encoding circuits where parameters are optimized to preserve visual feature information. By dynamically adjusting these parameters during training, the system achieves both fast quantum processing and high information preservation precision for visual data.
Data Source
AI summary
A method, system, and computer program product for minimizing a quantum information gap. Classical features are projected into logits. Furthermore, quantum features are projected into logits using quantum center vectors. A loss function measuring how well a model's prediction aligns with true labels is calculated using the logits of the classical features and a set of labels. Furthermore, an expression is calculated that minimizes an average Kullback-Leibler divergence between projected feature distributions from two different modalities or sources using the logits of the classical features and the logits of the quantum features. Additionally, the quantum information preserving loss function used to train a model to minimize the quantum information gap is calculated using the loss function, the expression, and a loss factor. After training the model, the trained model produces a feature vector, which preserves the important information and patterns present in the original classical feature vector.