Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for hybrid classical-quantum optimization using random matrix theory-based subproblem identification on correlation matrices are disclosed. A method may include a classical computer program: receiving a problem to optimize and time series data comprising a plurality of parameters; computing an average and a correlation matrix for the time series data; determining an aspect ratio for the correlation matrix; filtering the correlation matrix based on the aspect ratio and using a denoising solution; redefining the problem into a plurality of subproblems; determining that one of the plurality of subproblems exceeds a limit of a quantum computer; repeatedly dividing the subproblem until the limit of the quantum computer is met; embedding the subproblems on the quantum computer, wherein the quantum computer is configured to execute a quantum optimization routine on each of the subproblems and output a plurality of solution vectors; and recombining the plurality of solution vectors.

Abstract: 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=eiÂt, generated by a N×N Hermitian matrix Â. The binary representations of the n-bit estimations of eigenvalues of  may be encoded in these states: |?i=|b1b2 . . . bn; ?i is an estimation of the i-th eigenvalue, excluding degeneracy, and .b1b2 . . . bn is its binary representation. To perform the eigenvalue inversion, an n-qubit controlled Ry rotation with angle ?i/2(n?1) conditioned on seeing |b1b2 . . . bn is applied for each possible n-bit binary string b1b2 . . . bn (2n values). The overall unitary is called a “uniformly controlled Ry rotation” in literature.

Abstract: Systems and methods for quantum computing-based summarization are disclosed. A method for quantum computing-based summarization may include a classical computer program: receiving a document having a plurality of sentences; receiving a summary parameter that represents a subset of the plurality of sentences to include in a summary of the document; generating a vector for each sentence; calculating a centrality value for each vector; calculating a similarity value to other vectors for each vector; creating a cost function using the similarity values, the centrality values, a number of the plurality of sentences in the document, and the summary parameter; instructing a quantum computer to optimize the cost function using a quantum algorithm; receiving a dictionary comprising a plurality of distributions of the plurality of sentences and a probability for each distribution; and generating a summary comprising a subset of the plurality sentences based on a distribution having a highest probability.

Abstract: 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=eiÂt, generated by a N×N Hermitian matrix Â. The binary representations of the n-bit estimations of eigenvalues of  may be encoded in these states: |?i=|b1b2 . . . bn; ?i is an estimation of the i-th eigenvalue, excluding degeneracy, and .b1b2 . . . bn is its binary representation. To perform the eigenvalue inversion, an n-qubit controlled Ry rotation with angle ?i/2(n?1) conditioned on seeing |b1b2 . . . bn is applied for each possible n-bit binary string b1b2 . . . bn (2n values). The overall unitary is called a “uniformly controlled Ry rotation” in literature.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.