Granted Patent B2

US 12,321,715 · App. 17/171,502 · Granted Jun 3, 2025

Inventors: Alexander Buts (Vernon Hills, IL); Marco Pistoia (Amawalk, NY); Dylan Herman (Woodcliff Lake, NJ)

Assignee: JPMORGAN CHASE BANK, N.A.

G06F7/58

Abstract

Claims (40)

1. A method for verifying a randomness of an intended random number, the method being implemented by at least one processor, the method comprising:

accessing, by the at least one processor, the intended random number by using a random number generator (RNG) that interfaces with a quantum computer to generate an output;

converting, by the at least one processor, the intended random number into a bitmap image;

analyzing, by the at least one processor via a validator function using a machine learning technique, the bitmap image with reference to a predetermined model that is trained using a plurality of white noise images, wherein the machine learning technique compares the bitmap image to the predetermined model to determine the randomness of the intended random number;

displaying, by the at least one processor and based on a result of the analyzing, the determined randomness on a display of a user interface;

responsive to a determination that the bitmap image is not a true random image, outputting, by the at least one processor via the validator function, a pseudo random bitmap; and

responsive to a determination that the bitmap image is a true random image, outputting, by the at least one processor via the validator function, a true random bitmap, transmitting, by the at least one processor, the true random bitmap to a transformation function, and generating, by the at least one processor via the transformation function, a true random number that corresponds to the true random bitmap.

2. The method of claim 1 , wherein the analyzing to determine the randomness comprises determining that the intended random number is a true random number.

3. The method of claim 2 , further comprising:

generating a second bitmap image; and

transforming the second bitmap image into a bit sequence that corresponds to the true random number.

4. The method of claim 1 , wherein the quantum computer uses qubits in order to generate the output.

5. A computing apparatus for verifying a randomness of an intended random number, the computing apparatus comprising:

a processor;

a memory; and

a communication interface coupled to each of the processor and the memory,

wherein the processor is configured to:

access the intended random number by using a random number generator (RNG) that interfaces with a quantum computer to generate an output;

convert the intended random number into a bitmap image;

analyze, via a validator function using a machine learning technique, the bitmap image with reference to a predetermined model that is trained using a plurality of white noise images, wherein the machine learning technique compares the bitmap image to the predetermined model to determine the randomness of the intended random number; and

display, based on a result of the analyzing, the determined randomness on a display of a user interface;

responsive to a determination that the bitmap image is not a true random image, output, via the validator function, a pseudo random bitmap; and

responsive to a determination that the bitmap image is a true random image, output, via the validator function, a true random bitmap, transmit the true random bitmap to a transformation function, and generate, via the transformation function, a true random number that corresponds to the true random bitmap.

6. The computing apparatus of claim 5 , wherein the processor is further configured to determine that the intended random number is a true random number.

7. The computing apparatus of claim 6 , wherein the processor is further configured to:

generate a second bitmap image; and

transform the second bitmap image into a bit sequence that corresponds to the true random number.

8. The computing apparatus of claim 5 , wherein the quantum computer uses qubits in order to generate the output.

9. A non-transitory computer readable storage medium storing instructions for verifying a randomness of an intended random number, the storage medium comprising executable code which, when executed by a processor, causes the processor to:

access the intended random number by using a random number generator (RNG) that interfaces with a quantum computer to generate an output;

convert the intended random number into a bitmap image;

analyze, via a validator function using a machine learning technique, the bitmap image with reference to a predetermined model that is trained using a plurality of white noise images, wherein the machine learning technique compares the bitmap image to the predetermined model to determine the randomness of the intended random number; and

display, based on a result of the analyzing, the determined randomness on a display of a user interface;

responsive to a determination that the bitmap image is not a true random image, output, via the validator function, a pseudo random bitmap;

responsive to a determination that the bitmap image is a true random image, output, via the validator function, a true random bitmap, transmit the true random bitmap to a transformation function, and generate, via the transformation function, a true random number that corresponds to the true random bitmap.

10. The storage medium of claim 9 , wherein the executable code further causes the processor to determine that the intended random number is a true random number.

11. The storage medium of claim 10 , wherein the executable code further causes the processor to:

generate a second bitmap image; and

transform the second bitmap image into a bit sequence that corresponds to the true random number.

12. The storage medium of claim 9 , wherein the quantum computer uses qubits in order to generate the output.

Assignments (1)

Continuity (2)

References Cited (10)