Quantum Randomness Verification with Classical Challenge Response
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Solution Overview
Problem
Efficient certification of true randomness in quantum computing devices is challenging due to computational power limitations and restrictions imposed by quantum mechanics, making it difficult to verify the quantum behavior of these devices.
Innovation Solution
A classical computing device verifies quantum randomness using a physically unclonable function (PUF) to generate training data for regression models, which are used in conjunction with a quantum computing device to perform a single round of communication-based verification, incorporating a public source of randomness and hardware-based cryptographic functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical tests (NIST, Diehard) are used to verify randomness, then randomness quality can be assessed, but true randomness cannot be certified with finite computational power
Solution Approach 1:
The patent introduces an intermediary verification mechanism using a classical verifier and a challenge-response protocol based on Learning Parity with Noise (LPN) problems. The classical verifier acts as an intermediary that can efficiently verify quantum randomness without requiring full computational power to generate it, resolving the contradiction between verification accuracy and certification reliability.
2Measurement precision
If quantum computing device computational power is utilized, then true randomness can be generated, but verification becomes difficult due to quantum mechanics restrictions on accessing internal state
Solution Approach 1:
The patent extracts only the necessary verification information from the quantum device's operations without requiring access to its internal quantum state. By using challenge-response protocols where the quantum device responds to classical challenges, the verification process obtains sufficient randomness certification information while respecting quantum mechanics restrictions on internal state access.
Solution Approach 2:
A classical verifier serves as an intermediary that bridges the quantum device and the verification process. The classical verifier formulates challenges and interprets responses, enabling efficient verification of quantum randomness without direct access to the quantum device's internal state, thus resolving the verification difficulty.
3Reliability
If multiple rounds of communication are used for verification, then verification robustness can be improved, but communication efficiency decreases
Solution Approach 1:
The patent performs preliminary setup actions during an offline phase, including generating training data for the quantum device and establishing verification parameters. This preliminary preparation enables the online verification phase to proceed with minimal communication rounds while maintaining robustness, as the heavy lifting of setup and model training is completed in advance.
Data Source
AI summary
Methods, systems, and apparatus for verifying quantum randomness. In one aspect a computing device receives data from a quantum computer. The data includes a timestamp, a binary-valued vector, and a predicted response to a challenge string. The predicted response to the challenge string is generated using a regression model that has been trained to fit LPN instances as a linear function, where the LPN instances are constructed using a physically unclonable function. The computing device determines a parity of a random number output by a public source of randomness at a time specified by the timestamp and performs either a generation round or a test round based on the parity to verify the randomness of a bit generated by the quantum computer. The generation round uses the data to verify a preimage of the binary-valued vector and the test round uses the data to verify an equation.