Temporal Quantum Feature Maps for Variable-Length Sequence Prediction
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Solution Overview
Problem
Existing quantum kernel and feature map circuits are not well-suited for embedding sequential data, particularly for sequences of varying lengths, which poses challenges in kernel-based sequential data prediction.
Innovation Solution
The implementation of Temporal Quantum Feature Maps (TQFMs) for kernel-based sequential data prediction, which enables quantum embedding of sequences of variable lengths using quantum channels, allowing for the computation of kernel elements between sequences on a quantum computer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing quantum kernel and feature map circuits are used, then quantum computation can be performed, but they are not well-suited for embedding sequential data of varying lengths
Solution Approach 1:
The patent introduces temporal quantum feature maps that dynamically adapt to sequences of varying lengths by processing sequences as time-series data through quantum channels. The system can handle different sequence lengths (k=1, k>1) by applying temporal evolution operators that accommodate the variable temporal structure, making the quantum kernel method versatile for sequential data embedding tasks.
Solution Approach 2:
The patent maps sequential data into a high-dimensional quantum feature space by applying quantum feature maps that transform classical sequences into quantum states. This dimensional transformation enables the system to capture temporal dependencies and sequence structures that are not readily available in the original data representation, improving the embedding capability for variable-length sequences.
2Adaptability or versatility
If quantum feature maps are used for sequential data, then sequence embedding is enabled, but the complexity of processing variable length sequences increases
Solution Approach 1:
The patent segments the sequence processing into discrete temporal steps by applying quantum channels at different time points. The sequence is divided into individual time-step representations that are processed independently through quantum evolution operators, then combined to form the final quantum state. This segmentation approach simplifies the processing of variable-length sequences by breaking down the complex task into manageable temporal segments.
3Measurement precision
If classical kernel methods are used for sequential data prediction, then computation is straightforward, but precision and predictive accuracy are limited
Solution Approach 1:
The patent changes the fundamental parameters of the computation by transitioning from classical to quantum domain. Quantum feature maps utilize quantum mechanical properties such as superposition and entanglement to create high-dimensional feature representations that capture complex temporal patterns. The quantum kernel function computes similarities in this enhanced feature space, providing superior predictive accuracy for sequential data while maintaining the computational framework of quantum mechanics.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to TQFMs for kernel-based sequential data prediction. A system can comprise a memory that can store computer-executable components. The system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a computation component that can use a TQFM to compute a kernel element between two sequences of symbols, on a quantum computer, by respectively processing two input sequences as vectors.