Jpmorgan Chase Bank, N.a.

Alexander Buts

Marco Pistoia

Dylan Herman

Shaohan Hu

Pierre Minssen

Romina Yalovetzky

Steven L Weiner

Robert S Newnam

Rocky J Maufort

Ta-Wei Chen

Robert Matles

Tyrone M Saunders

Benjamin H Sansom

Matthew Liste

Aniella Arantes

Andrew Tayag Rodriguez

Anitha Srinivasan

David A Carter

Apoorv Saxena

Eric Bikorimana

Jonathan Leach

Implementations generally relate to an image-based login and authentication system. In some implementations, a method includes displaying a plurality of random images to a user and receiving from the user a selection of at least one target image from the plurality of random images. The method further includes generating a hash number for the at least one target image, where the hash number identifies the at least one target image, and concealing the hash number in the at least one target image, where the concealing of the hash number provides security in an authentication of the at least one target image. The method further includes generating an encrypted identification token, where the encrypted identification token includes the hash number. The method further includes associating the encrypted identification token with the user, and storing the encrypted identification token in a database for authentication of at least one target image and the user.

Image-based login and authentication

A system and a method for verifying a randomness of an intended random number is provided. The method includes: accessing the intended random number; converting the intended random number into a bitmap image; analyzing the bitmap image with reference to a predetermined model; and using a result of the analyzing to determine whether the intended random number is a true random number or a pseudorandom number. The analysis of the bitmap image may be performed by using a machine learning image classification technique with respect to a model that is trained by using white noise images.

Method and system for verification of random numbers

A method and system for performing dynamic provisioning of augmented reality information is provided. The method includes acquiring, from a user device of a user, user device-based information, retrieving rebate information corresponding to the user device-based information, and retrieving user information of the user of the user device. Based on the acquired and retrieved information, the method further tracks movement or activity of the user device, and determines whether a predetermined condition is met. When the predetermined condition is determined to have been met, the method displays one or more rebates in augmented reality over image information displayed on a display of the user device. However, when the predetermined condition is determined not to have been met, the method continues the tracking of movement or activity of the user device until the predetermined condition is determined to be met.

System and method for dynamic provisioning of augmented reality information

A method may include: a computer program populating a Hermitian matrix A with input data; calculating an upper bound a for a maximum eigenvalue for the Hermitian matrix A; initializing a time evolution value t=1/a; generating a first quantum computer program using the time evolution value t; communicating the first quantum computer program to a quantum computer; receiving a result including a binary value for each n-bit string and a probability for each binary value; converting each binary value into an integer; identifying a maximum absolute value of the integers; determining a value x for the maximum absolute value of all of the integers; updating the time evolution value t based on the value of x; generating a second quantum computer program using the updated time evolution value t; and communicating, by the classical computer program, the second quantum computer program to the quantum computer.

Systems and methods for optimization of time evolution for quantum computer-based eigenvalue estimation

Embodiments use quantum conditional logic in the Quantum Phase Estimation Algorithm (QPEA) to compute eigenvalues prior to inversion. Embodiments estimate the eigenvalues of a unitary, U=e, generated by a N×N Hermitian matrix Â. The binary representations of the n-bit estimations of eigenvalues of Â may be encoded in these states: |λ=|bb. . . b; λis an estimation of the i-th eigenvalue, excluding degeneracy, and .bb. . . bis its binary representation. To perform the eigenvalue inversion, an n-qubit controlled Ry rotation with angle λ/2conditioned on seeing |bb. . . bis applied for each possible n-bit binary string bb. . . b(2values). The overall unitary is called a 'uniformly controlled Ry rotation' in literature.

Systems and methods for enhanced eigenvalue inversion using quantum conditional logic

Systems and methods for zero-footprint and safe execution of quantum computing programs are disclosed. According to one embodiment, in an electronic device comprising at least one computer processor, a method for cloud-based execution of quantum-computing programs may include: (1) receiving, from a user interface on a client device, a serialized file comprising a domain, an application, and an algorithm; (2) receiving, from the user interface, problem data and an identification of a quantum computing backend for executing the problem data; (3) instantiating a quantum program for execution and communicating the quantum program and the problem data to the quantum computing backend for execution; (4) receiving, from the quantum computing backend, an output of the execution; and (5) communicating the output to the user interface on the client device.

Systems and methods for zero-footprint and safe execution of quantum computing programs

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New York, NY, United States of America

Alexander Buts