Probabilistic Random Variable Data Type for Unknown Value Computing
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Solution Overview
Problem
Conventional computer programming languages are limited by their ability to only handle known values, making it inefficient to solve problems involving unknown values, such as those in quantum simulations and statistical analyses, as they require numerical algorithms that become inefficient with high dimensions and cannot represent multiple states at once.
Innovation Solution
A quantum-inspired computing method is introduced, where atomic random variables with definite probabilities are defined, allowing for the use of random variables as a basic data type in programming languages, enabling the representation of indefinite values and supporting various mathematical operations and statistical properties to navigate solution spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional computer programming languages use basic data types to represent known values, then computer operations can be executed efficiently, but the system cannot handle unknown values or represent multiple states simultaneously
Solution Approach 1:
The patent changes the fundamental parameter of data representation from deterministic known values to probabilistic random variables. By defining random variables with probability distributions (e.g., P(X=0) and P(X=1)), the system can represent unknown values and multiple states simultaneously, directly resolving the contradiction between handling unknown values and maintaining programming language structure
Solution Approach 2:
The patent adds a probabilistic dimension to traditional computing by introducing probability distributions as a new layer of data representation. Instead of single-state values, the system uses multi-state random variables that exist in a probability space, enabling representation of multiple states simultaneously without fundamentally restructuring the underlying computing architecture
2Adaptability or versatility
If numerical algorithms are used to solve problems with unknown values in high dimensions, then solutions can be obtained, but computational efficiency deteriorates significantly
Solution Approach 1:
The patent substitutes traditional numerical algorithms with a probabilistic computing model where random variables and their operations directly represent and manipulate unknown values. This replacement eliminates the need for inefficient numerical approximation methods in high-dimensional spaces, as the probabilistic framework naturally handles such problems through algebraic operations on random variables rather than iterative numerical computations
3Adaptability or versatility
If random variables are introduced as a basic data type to represent indefinite values, then multiple states can be represented simultaneously, but the complexity of mathematical operations increases
Solution Approach 1:
The patent segments the complexity of random variable operations by defining atomic random variables (ARVs) as fundamental building blocks with simple probability distributions. Complex random variables are constructed by combining ARVs through algebraic operations, allowing the system to manage mathematical complexity through modular composition rather than monolithic complexity
Solution Approach 2:
The patent creates a universal framework where random variables serve multiple functions: they represent unknown values, encode multiple states through probability distributions, and enable algebraic manipulation of probabilistic information. This multi-functionality reduces the need for separate mechanisms to handle different aspects of probabilistic computing, simplifying the overall operational complexity despite the enhanced representational capability
Data Source
AI summary
A method of computing includes defining a first atomic random variable (ARV) and first random variable (RV) in a programming language system. The first ARV having a non-deterministic value of either zero according to a second probability or one according to a first probability. A sum of the first probability and the second probability is one. A covariance of the first ARV and a second ARV is zero. The first RV has a first indefinite value at a first definite probability and includes a polynomial of one or more atomic random variables (ARVS) that includes the first ARV. The method includes executing a computer instruction that includes a mathematical operation involving the first RV as a basic data type and produces a second RV having a second indefinite value at a second definite probability, represents a result distribution, and tracks a response to the one or more ARVS.