QELSTM Circuit Layout for Constant-Depth Qubit Entanglement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing quantum LSTM models face challenges with increasing circuit depth as the number of qubits increases, leading to hardware noise and trainability issues, while feature dimension reduction methods result in information loss and reduced accuracy.
Innovation Solution
A Quantum Enhanced Long short-term memory (QELSTM) model with feature dimensionality reduction via linear combinations of correlated features, using an optimal CNOT gate design that maintains constant circuit depth by alternating qubit indices, ensuring qubits with highest correlations are adjacent, thus reducing unnecessary connectivity and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of qubits is increased to process more features, then the model's ability to represent quantum states improves, but the circuit depth increases leading to hardware noise and trainability issues
Solution Approach 1:
The patent applies segmentation by dividing the feature processing into two stages: classical dimensionality reduction (PCA) to reduce the number of features before quantum processing, and then quantum processing on the reduced feature set. This segmentation allows the system to handle more information overall while keeping the quantum circuit depth manageable, thus resolving the contradiction between representing more quantum states and maintaining circuit reliability.
2Device complexity
If feature dimension reduction is applied to reduce the number of qubits, then circuit depth and complexity are reduced, but information loss occurs and accuracy decreases
Solution Approach 1:
The patent introduces PCA as an intermediary classical processing step that transforms the original feature space into a reduced feature space while preserving maximum variance. This intermediary transformation allows information to be preserved in a compressed form before quantum processing, thus reducing circuit complexity without significant information loss.
3Productivity
If classical LSTM is used for time series forecasting, then the model is computationally manageable, but convergence is slow and accuracy is limited compared to quantum approaches
Solution Approach 1:
The patent merges classical and quantum computing approaches by using classical PCA for dimensionality reduction and feature extraction, then feeding the reduced features into a quantum LSTM model for processing. This hybrid merging allows the system to leverage the computational manageability of classical methods while achieving the faster convergence and higher accuracy of quantum methods.
Data Source
Figure 1A
Figure 2A
Figure 2B
AI summary
Managing circuit depth with increasing n qubits is a technical problem to be solved. A method and system for an object count prediction by analyzing time series using Quantum Enhanced LSTM with feature dimensionality reduction via linear combinations of correlated features. Optimal CNOT gate design that follows qubit entanglement structure by alternating between odd and even qubit indices, with each qubit connected to two other adjacent or non-adjacent qubits is disclosed, which. creates constant circuit depth that can limit to predefined number of quantum operations independent of number of the set of qubits in the QELSTM model. Further, the linear combination generates a set of transformed features that are mapped to the qubits of the QELSTM via a mapping technique that enables entanglement of the qubits based on correlation of the transformed features and in accordance with the optimal CNOT gate design so as to maintain the constant circuit depth.