Quantum Gradient Estimation for Optimization Efficiency
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Solution Overview
Problem
Existing optimization techniques, such as gradient descent, face challenges in efficiently optimizing problems with many parameters and require gradient calculation, which can be non-trivial and resource-intensive.
Innovation Solution
A hybrid computing system that combines a digital processor with a quantum processor to optimize parameters of an objective function using a method that estimates gradients based on samples from a probability distribution generated by the quantum processor, allowing for faster and more accurate optimization without explicit gradient calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gradient descent optimization is used, then optimization accuracy can be improved, but computational resource consumption and time increase significantly
Solution Approach 1:
The patent replaces the classical mechanical gradient calculation system with a quantum mechanical system. Instead of computing gradients through traditional numerical differentiation or backpropagation, the invention uses quantum circuits to estimate gradients by measuring expectation values of quantum operators. This substitution of classical computational mechanisms with quantum mechanical processes enables more efficient optimization by leveraging quantum parallelism and superposition to evaluate multiple gradient components simultaneously.
Solution Approach 2:
The patent changes the fundamental parameters of the optimization process by introducing quantum states and operators as replacements for classical gradient computations. The optimization method transforms the problem from one requiring explicit gradient calculations to one involving quantum expectation value measurements, fundamentally altering how gradient information is obtained and processed in the optimization loop.
2Measurement precision
If explicit gradient calculation is performed, then optimization can be more accurate, but the process becomes non-trivial and resource-intensive
Solution Approach 1:
The patent substitutes complex classical gradient calculation mechanisms with simpler quantum measurement processes. Instead of implementing elaborate numerical differentiation schemes, automatic differentiation graphs, or backpropagation through multiple layers, the invention uses quantum circuits that directly prepare states and measure expectation values to obtain gradient information, significantly simplifying the computational process.
Solution Approach 2:
The patent introduces quantum states and quantum operators as intermediary elements between the objective function and the gradient information. These quantum intermediaries enable the extraction of gradient data through expectation value measurements, avoiding the need for direct complex gradient computations and serving as a bridge that simplifies the overall optimization process.
3Productivity
If classical random number generators are used, then optimization can proceed, but bias in samples reduces convergence speed
Solution Approach 1:
The patent replaces classical pseudo-random number generation systems with quantum random number generation. Instead of using algorithms that produce pseudo-random numbers with inherent biases and patterns, the invention employs quantum circuits that generate truly random samples through quantum measurement processes. This substitution leverages the fundamental indeterminacy of quantum mechanics to produce high-quality random numbers that improve optimization convergence.
Solution Approach 2:
The patent changes the source of random numbers from classical computational systems to quantum physical systems. By using quantum states and measurements to generate random samples, the invention fundamentally alters the nature of randomness from pseudo-random to truly random, eliminating biases inherent in classical random number generators and improving the quality and reliability of optimization samples.
Data Source
AI summary
Quantum-classical gradient estimation is described for use in determining a value of at least one optimizable parameter of an objective function. An optimization method can be performed by a digital processor coupled to an analog processor and can include, until the objective function converges: obtaining a set of samples generated by the analog processor; estimating a gradient of the objective function based on a current value of the at least one optimizable parameter and the set of samples; determining first and second order moments based on the estimated gradient, and, updating the optimizable parameters based on the moments. A method to train a machine learning data can include: receiving a training data set; generating a training model having an objective function; performing acts of the optimization method to optimize parameters of the training model; and, returning the optimized training model to the machine learning model.