Continuous-Variable Quantum Neural Network Encryption
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Solution Overview
Problem
Current cryptosystems are not secure enough to prevent data from being attacked by powerful quantum computers, particularly due to the vulnerability of classical algorithms like RSA to quantum computing threats.
Innovation Solution
An encryption and decryption method using a continuous-variable quantum neural network (CVQNN) that updates weights with training samples, performs legal measurement bases synchronization, converts information into quadratic plaintext, encrypts, and decrypts data, leveraging the ease of preparation and handling of continuous-variable quantum sources for enhanced security and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical cryptographic algorithms (e.g., RSA) are used to enhance security through mathematical complexity, then security is improved, but quantum computing can break these algorithms in polynomial time, making them vulnerable
Solution Approach 1:
The patent replaces classical mechanical/computational cryptographic systems with a quantum-based cryptographic system. Specifically, it substitutes the classical key distribution mechanism with a quantum key distribution mechanism that uses quantum states (continuous variables) to encode and transmit cryptographic keys, making the system inherently secure against quantum computing attacks through the laws of quantum mechanics
Solution Approach 2:
The patent changes the fundamental parameters of cryptographic security by transitioning from discrete classical bits to continuous quantum variables. The system uses continuous quadrature amplitudes of quantum states (X and P operators) as the basis for key distribution, fundamentally altering the parameter space from discrete mathematical complexity to continuous quantum physical properties
2Reliability
If quantum key distribution with classical one-time pad method is used to solve key distribution, then security is improved, but lots of keys are generated and consumed, increasing system complexity
Solution Approach 1:
The patent makes the quantum key distribution system universal by enabling it to work with continuous-variable quantum states that can represent multiple key values simultaneously through superposition. The continuous quadrature amplitudes allow the system to encode vast amounts of cryptographic information in a single quantum state, making the key distribution mechanism applicable to various cryptographic protocols without requiring separate key management systems
Solution Approach 2:
The patent uses quantum cloning in a controlled sense through the continuous-variable nature of the quantum states. The quantum states can be copied and distributed to multiple parties while maintaining their quantum properties, allowing the same quantum key material to be efficiently distributed to multiple recipients without generating separate physical key materials for each party
3Productivity
If discrete-variable quantum neural network is used for encryption, then quantum processing capability is improved, but discrete quantum sources are difficult to prepare and gradient algorithm is hard to implement, reducing practical effectiveness
Solution Approach 1:
The patent substitutes the discrete-variable quantum system with a continuous-variable quantum system. Instead of using discrete qubits that require complex preparation and measurement, the system uses continuous quantum variables (quadrature amplitudes) that can be prepared and measured using standard optical components, dramatically simplifying the implementation while maintaining quantum processing capability
Solution Approach 2:
The patent changes the variable type from discrete to continuous, allowing the quantum neural network to operate with continuous quadrature amplitudes. This parameter change enables the use of standard optical continuous-variable quantum sources and measurements, making the quantum neural network practically implementable with existing technology while retaining the quantum advantages for encryption
Data Source
AI summary
A method and a system for encryption and decryption based on continuous-variable quantum neural network CVQNN. The method includes: updating a weight of the CVQNN with a training sample; triggering, by a sender, a legal measurement bases synchronization between the sender and the CVQNN; converting, by the sender, the information to be sent into a quadratic plaintext according to the synchronized measurement bases, and sending the quadratic plaintext to the CVQNN; encrypting, by the CVQNN, a received quadratic plaintext, and sending an encrypted quadratic plaintext to a receiver; after receiving the encrypted quadratic plaintext, sending by the receiver the encrypted quadratic plaintext to the CVQNN for decryption to obtain decrypted information. The embodiments implement data encryption and decryption by introducing CVQNN model and synchronization measurement technology. The embodiments provide advantages of high reliability, high security and easy realization.