Quantum Annealing Random Number Generator with Ising Model Mapping

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Solution Overview

Problem

Current random number generators, including those using quantum annealing machines, cannot produce high-quality random numbers that conform to a user-designated arbitrary distribution, and existing methods using pseudo-random number generators are not secure due to periodic characteristics.

Innovation Solution

A random number generator system that receives a designated probability distribution, generates an Ising model using binary variables, performs quantum annealing to acquire binary variable values, and outputs random numbers from specific subintervals based on these values, ensuring the generated numbers follow the desired distribution.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If quantum annealing machines are used to generate random numbers, then unpredictability is improved, but the ability to conform to user-designated arbitrary distributions deteriorates

Engineering Contradiction:

ImproveunpredictabilityVSAvoiddistribution conformity

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent introduces an intermediary system consisting of a classical computer and a quantum annealing machine that works together. The classical computer generates the Ising model based on the desired distribution and controls the quantum annealing process, while the quantum annealing machine provides the random sampling. This intermediary classical control layer enables the system to conform to user-designated distributions while maintaining quantum-generated unpredictability.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent changes the parameter representation by mapping the desired probability distribution parameters to Ising model parameters (Hamiltonian coefficients). By transforming the distribution specification into an Ising model formulation, the system can leverage quantum annealing to sample from the desired distribution, thus achieving both unpredictability and distribution conformity.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If pseudo-random number generators are used, then distribution conformity is improved, but security deteriorates due to periodic characteristics

Engineering Contradiction:

Improvedistribution conformityVSAvoidsecurity

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent replaces the mechanical/computational pseudo-random number generation system with a quantum physical system. Instead of using deterministic algorithms that produce periodic sequences, the system uses quantum annealing which inherently produces non-periodic, unpredictable sequences while still being able to conform to specified distributions through the Ising model formulation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Device complexity

If conventional random number generation methods are used, then device complexity is reduced, but quality of random numbers deteriorates

Engineering Contradiction:

Improvesystem simplicityVSAvoidrandom number quality

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent segments the random number generation task into two parts: the classical computer handles model generation and control, while the quantum annealing machine handles the actual random sampling. This segmentation allows each component to perform its specialized function, achieving high-quality random numbers without requiring the entire system to be overly complex.

Inventive Principle:

Principle #1Segmentation

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

The system effectively produces high-quality random numbers that conform to a desired distribution, making them secure and unpredictable, thus addressing the limitations of existing technologies.

Implementation Method 1

because of a characteristic called quantum fluctuations, the same combination of qubits is not necessarily always obtained, and instead various combinations of qubits in a near-stable state are obtained

Methodology Applied

Scientific EffectQuantum fluctuations:

Data Source

PatentUS20240303042A1Random number generator, random number generation method, and non-transitory computer readable medium storing program

Publication Date: 2024.09.12 NEC CORP

AI summary

A random number generator includes an input receiving unit configured to receive an input for designating a probability distribution of random numbers, a model generation unit configured to generate, based on the probability distribution, an Ising model using n binary variables (n is an integer equal to or greater than 2) each of which is assigned to a respective one of n subintervals obtained by dividing a numerical range of random numbers, an annealing result acquisition unit configured to acquire values of the n binary variables, the values being an execution result of quantum annealing for the Ising model, and a random number output unit configured to output, as a random number, a value included in the subinterval assigned to the binary variable whose value, which has been obtained as the execution result, is equal to a predetermined value.