Quantum Learning Device With Hilbert-Space Encoding for Forecasting
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Solution Overview
Problem
Classical machine learning techniques face challenges with combinatorial explosion due to high dimensionality and complex interdependencies in real-world data, leading to computational complexity and resource-intensive requirements, which existing quantum systems fail to adequately address.
Innovation Solution
A quantum cognition model utilizing a processor, storage element, and encoded instructions to generate operators in Hilbert Space, enabling efficient forecasting by encoding data within the ground state of a quantum subsystem through an artificial neural network, employing techniques like Hilbert Space Expansion, Pruning, and Hierarchical Learning to manage high-dimensional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If classical machine learning techniques are used to handle high-dimensional data with complex interdependencies, then the model can capture all features and variables, but the computational complexity and resource requirements explode exponentially
Solution Approach 1:
The patent replaces classical computational systems with a quantum cognitive system that uses quantum mechanical principles (Hilbert space, operators, ground states) to process high-dimensional data. This substitution enables the system to handle complex interdependencies exponentially more efficiently than classical machines, resolving the contradiction between comprehensive feature capture and computational complexity
Solution Approach 2:
The patent transitions from classical data representation to quantum state representation in Hilbert space, adding a dimensional transformation that allows efficient handling of high-dimensional relationships. By encoding data as quantum states and using operators to represent variables, the system manages complexity that would be intractable in classical dimensions
2Adaptability or versatility
If the number of features and variables in a machine learning model increases to capture real-world complexity, then the model's representational capability improves, but the solution space grows exponentially making it intractable
Solution Approach 1:
The patent substitutes quantum mechanical formalism for classical enumeration of solution spaces. By representing features as operators in Hilbert space and using quantum ground states to encode data, the system achieves exponential compression of the representational space while maintaining the ability to capture complex real-world relationships
3Adaptability or versatility
If classical computers are used to explore the combinatorially large space of possible responses in language generation, then all possible valid responses can be considered, but finding the optimal response becomes computationally challenging
Solution Approach 1:
The patent replaces classical brute-force exploration of response spaces with quantum cognitive processing. By representing language elements as operators and using quantum state manipulation to explore relationships, the system can efficiently identify optimal responses among combinatorially large possibilities without exhaustive search
Data Source
AI summary
A system and method for forecasting with a quantum subsystem using an artificial neural network (ANN) comprising: obtaining at least one data point and one or more stored parameters; mapping the at least one data point to one or more physical controls using the artificial neural network based on the one or more stored parameters; configuring the quantum subsystem at low temperatures based on the one or more physical controls; measuring generalized forces exerted on the quantum subsystem by the one or more physical controls; adjusting the one or more physical controls through the artificial neural network to lower energy of the quantum subsystem with respect to a training data set; and forecasting a missing data value in the data set to lower the energy of the quantum subsystem.