Robust Inference Optimization With Boltzmann Sampling

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Solution Overview

Problem

Robust inference problems are often too complex for direct numerical solving, and existing methods struggle to efficiently optimize complex models using stochastic optimization techniques.

Innovation Solution

The use of stochastic-gradient methods with sampling devices, such as quantum processors, to smooth objective functions and connect gradients to a Boltzmann distribution, enabling efficient computation through processes like quantum annealing, thermal relaxation, or classical computers, for optimizing model parameters.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If direct numerical solving is used for robust inference problems, then mathematical modeling can be performed, but the problem becomes too complex to solve efficiently

Engineering Contradiction:

Improveaccuracy of inferenceVSAvoidcomputational complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional numerical optimization methods with a physical sampling system that uses thermal or quantum processes to solve the robust inference problem. The sampling device physically embodies the objective function and automatically finds optimal configurations through natural physical processes, eliminating the need for complex numerical algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces a sampling device as an intermediary between the problem definition and solution. This device uses physical processes (thermal relaxation or quantum annealing) to sample from the Boltzmann distribution, which corresponds to the optimal solution of the robust inference problem, thereby mediating the complex computational task.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If stochastic optimization is used to simplify computation, then complex models can be optimized, but the objective function becomes difficult to evaluate directly

Engineering Contradiction:

Improveoptimization speedVSAvoiddifficulty of evaluating objective function

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent replaces direct evaluation of the objective function with a physical sampling process. The sampling device uses thermal or quantum processes to automatically evaluate and sample from the objective function landscape, converting a difficult computational evaluation task into a natural physical process.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the discrete optimization problem into a continuous probability distribution (Boltzmann distribution) that can be sampled physically. By changing the parameterization from discrete configurations to continuous probability amplitudes, the system enables efficient stochastic optimization through physical processes.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If exact solving of the inference problem is performed, then accurate gradients can be obtained, but the computational cost becomes prohibitive

Engineering Contradiction:

Improvegradient accuracyVSAvoidcomputation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent uses stochastic sampling to obtain approximate gradients rather than exact gradients. By sampling a finite number of configurations from the Boltzmann distribution, the system obtains sufficiently accurate gradient estimates for optimization without performing exhaustive exact calculations, achieving the right balance between precision and efficiency.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent replaces computational gradient calculation with physical sampling. The sampling device uses thermal or quantum processes to generate samples that naturally provide gradient information through statistical mechanics, eliminating the need for time-consuming numerical differentiation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach allows for the efficient solution of complex inference problems in machine learning tasks, improving accuracy and robustness in applications like clustering, recommender systems, and natural language processing.

Implementation Method 1

connect the gradient of the smoothed function approximation to a Boltzmann distribution, which can be sampled by a sampling device using a simulated process and/or quantum process, in particular quantum-annealing process, thermal or adiabatic relaxation

Methodology Applied

Scientific EffectBoltzmann distribution:

Implementation Method 2

quantum-annealing process

Methodology Applied

Scientific EffectQuantum annealing:

Implementation Method 3

thermal or adiabatic relaxation of a classical computer, semi-classical computer, or a quantum processor/device

Methodology Applied

Scientific EffectThermal relaxation: Stress Relaxation

Data Source

PatentUS12423374B2Systems and methods for stochastic optimization of a robust inference problem

Publication Date: 2025.09.23 1QB INFORMATION TECHNOLOGIES INC

AI summary

The present disclosure provides methods and systems for stochastic optimization of a robust inference problem using a sampling device.