Quantum Learning System Using Optimal Transport Loss
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Solution Overview
Problem
The learning of a quantum dataset distribution is challenging due to the lack of appropriate generators and loss functions for quantum generative models, making it difficult to construct effective quantum generative models that can generate quantum data.
Innovation Solution
A learning system comprising a quantum computation unit, a classical computation unit, and a management unit that determines the structure of a generator from latent variables and quantum circuit parameters, calculates the ground cost and its gradient, and updates the quantum circuit parameters using the optimal transport loss, thereby learning the distribution of a quantum dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum machine learning is used to handle quantum data, then learning effectiveness is expected to be significantly superior to classical machine learning, but appropriate generators and loss functions have not been studied making learning difficult
Solution Approach 1:
The patent transforms the quantum learning problem into a classical optimization problem by changing the parameter space. It uses classical computation to determine generator structure from latent variables and quantum circuit parameters, then uses quantum computation to calculate costs and gradients, creating a hybrid parameter optimization approach that resolves the contradiction between quantum learning effectiveness and model construction complexity
Solution Approach 2:
The patent introduces an intermediary classical computation layer that bridges quantum data and classical optimization. The classical computation unit determines generator structure and updates parameters, while the quantum computation unit calculates ground cost and gradients. This intermediary classical layer makes quantum generative modeling feasible by providing a tractable optimization pathway
2Reliability
If optimal transport loss is used as loss function, then gradients vanishing problem is prevented and learning converges, but calculation of ground cost and optimal transport loss requires quantum computation
Solution Approach 1:
The patent segments the computation into distinct quantum and classical portions. The quantum computation unit specifically calculates the ground cost and its gradient, while the classical computation unit handles optimal transport loss calculation and parameter updates. This segmentation allows the use of optimal transport loss for reliable convergence while distributing computational requirements across appropriate hardware
Solution Approach 2:
The patent uses the quantum computation unit as an intermediary to provide accurate ground cost calculations to the classical optimization process. This quantum intermediary enables the classical optimizer to use optimal transport loss effectively, ensuring convergence while maintaining a clear division of computational labor between quantum and classical systems
Data Source
AI summary
A quantum computation unit, a classical computation unit, and a management unit are provided to learn distribution of a quantum dataset. A structure of a generator is determined from multiple samples of latent variables and values of quantum circuit parameters. A ground cost and a gradient of the ground cost are calculated from the generator and the quantum dataset. An optimal transport loss and a gradient of the optimal transport loss are calculated using the ground cost and the gradient of the ground cost. An updating process is executed that updates the quantum circuit parameter using the gradient of the optimum transport loss, thereby reducing the optimum transport loss. The updating process is repeated until the optimum transport loss converges.