JPWO2022200475A5

JPWO2022200475A5 -

Info

Publication number: JPWO2022200475A5
Application number: JP2023556533A
Authority: JP
Publication date: 2024-04-08

Claims

1. A system comprising:
a receiver component, the receiver component having a processor executing computer-executable components stored in a computer-readable memory, the computer-executable components accessing a classical data set;
and a feature component that generates one or more machine learning input features based on a quantum transformation of the classical dataset.

The computer executable components include:
10. The system of claim 1, further comprising an execution component that executes a classical machine learning model on the one or more machine learning input features.

The computer executable components include:
a transformation component that transforms the classical data set into a set of quantum probability amplitudes;
and a quantum component that runs a quantum circuit on the set of quantum probability amplitudes, thereby providing the quantum transformation of the classical data set.

The system of claim 3, wherein the quantum circuit is a quantum Fourier transform.

The computer executable components include:
5. The system of claim 1, further comprising a visualization component that visually renders both the classical dataset and the one or more machine learning input features.

The system of any one of claims 1 to 5, wherein the classical data set includes time series data.

A system with the features of claim 3, as described in any one of claims 1 to 6, wherein the quantum circuit is implemented on a quantum computing device that includes physical qubits.

The system of claim 7, wherein the quantum component is electronically integrated with a quantum computing device.

A system with the features of claim 3, as described in any one of claims 1 to 8, wherein the set of quantum probability amplitudes collectively represents a quantum state vector.

The system of claim 7, as recited in claim 9, wherein the quantum component is configured to execute any suitable initialization circuit on the quantum computing device to cause the states of the qubits of the quantum computing device to conform to the quantum state vector.

1. A computer-implemented method comprising:
accessing, by a device operatively coupled to the processor, the classical data set;
generating, by the device, one or more machine learning input features based on the quantum transformation of the classical dataset.

12. The computer-implemented method of claim 11, further comprising running, by the device, a classical machine learning model on the one or more machine learning input features.

converting, by said device, said classical data set into a set of quantum probability amplitudes;
13. The computer-implemented method of claim 11 or 12, further comprising: running, by the device, a quantum circuit on the set of quantum probability amplitudes, thereby providing the quantum transformation of the classical data set.

The computer-implemented method of claim 13, wherein the quantum circuit is a quantum Fourier transform.

15. The computer-implemented method of claim 11, further comprising visually rendering, by the device, both the classical dataset and the one or more machine learning input features.

The computer-implemented method of any one of claims 11 to 15, wherein the classical data set includes time series data.

A computer-implemented method with the features of claim 13, as recited in any one of claims 11 to 16, wherein the quantum circuit is executed on a quantum computing device that includes physical qubits.

1. A computer program for facilitating quantum enhanced features for classical machine learning, the computer program comprising :
Accessing classical data sets;
generating one or more machine learning input features based on the quantum transformation of the classical dataset.

20. The computer program product of claim 18, further comprising: causing the processor to run a classical machine learning model on the one or more machine learning input features.

The processor,
converting said classical data set into a set of quantum probability amplitudes;
20. A computer program product as claimed in claim 18 or 19, adapted to cause a quantum circuit to be run on the set of quantum probability amplitudes, thereby providing the quantum transformation of the classical data set.

The computer program of any one of claims 18 to 20, wherein the quantum circuit is a quantum Fourier transform.

22. A computer program product as claimed in any one of claims 18 to 21, configured to cause the processor to visually render both the classical dataset and the one or more machine learning input features.

1. A system comprising:
a processor executing computer-executable components stored in a computer-readable memory, the computer-executable components comprising: a receiver component for receiving a classical time series dataset from an operator device;
and a feature component that generates one or more quantum enhanced machine learning input features based on a quantum transformation of the classical time series dataset.

The computer executable components include:
24. The system of claim 23, further comprising an execution component that transmits the one or more quantum enhanced machine learning input features to the operator device.

The computer executable components include:
a transformation component that generates quantum probability amplitudes based on the classical time series data set;
and a quantum component that runs a quantum algorithm selected by the operator device on the quantum probability amplitudes, thereby providing the quantum transformation of the classical time series data set.

The system of claim 25, wherein the quantum algorithm is a quantum Fourier transform.

The computer executable components include:
27. The system of claim 23, further comprising a visualization component that graphs the classical time series dataset or the one or more quantum enhanced machine learning input features.

1. A computer-implemented method comprising:
1. A computer-implemented method comprising: receiving, by a device operatively coupled to a processor, a classical time series dataset from an operator device; and generating, by the device, one or more quantum enhanced machine learning input features based on a quantum transformation of the classical time series dataset.

30. The computer-implemented method of claim 28, further comprising transmitting, by the device, the one or more quantum enhanced machine learning input features to the operator device.

generating, by the device, a quantum probability amplitude based on the classical time series data set;
30. The computer-implemented method of claim 28 or 29, further comprising: executing, by the device, a quantum algorithm selected by the operator device on the quantum probability amplitudes, thereby providing the quantum transformation of the classical time series data set.

The computer-implemented method of claim 30, wherein the quantum algorithm is a quantum Fourier transform.

32. The computer-implemented method of claim 28, further comprising graphing, by the device, the classical time series dataset or the one or more quantum enhanced machine learning input features.