Using a IBM Quantum Computer and artificial intelligence to Predict the 6/49 Lottery
BEST-V2-Q-Predictor-6/49.ipynb
In this example, i combine a classical machine learning model (logistic regression) with quantum computing. The classical model is used to train on historical lottery data, and the quantum circuit is used to predict numbers given quantum operations.
While the quantum operations here may appear random due to the use of Hadamard gates, their application can be adjusted depending on the results of the model training. This way, the quantum circuit will take into account the patterns identified, rather than generating completely random numbers.
This is one of the most promising areas of research in quantum machine learning and requires significant effort to implement and test in real-world conditions.
This is the result of running my program on an IBM quantum computer with a database of lottery combinations for the past 2 years.
BEST-V1-AI-Q-Predictor-6/49.ipynb
New software incorporating multiple neural networks for in-depth analysis of historical data from past lottery combinations.
The program uses quantum computing to make predictions after trainin.g the neural network, and during the execution of the quantum circuit, it enters the data into a superposition state. However, the training of the neural network and its operation do not depend on quantum computing; quantum computing is used as an additional step to make predictions based on already trained models.
The program uses a quantum computer to make predictions using superposition at the execution stage of the quantum circuit. The data obtained after training the neural networks is fed to the quantum computer, where superposition is used for quantum calculations and predictions.
General process:
Data processing and PCA.
Train an autoencoder on the processed data.
Extract features by the autoencoder.
Train and evaluate CNN and RNN models on these features.
Use the features to create a quantum circuit.
Run the quantum circuit on the quantum backend and obtain predictions.
The program does involve the use of quantum computing, but superposition and quantum prediction are not used directly to train the neural networks. Let’s look at this in more detail.
Superposition in the quantum part
The program involves creating a quantum circuit for prediction:
Creating a quantum circuit: The create_quantum_circuit function creates a quantum circuit where each qubit is first put into a superposition state using Hadamard gates, and then RX rotation is used based on the data values.
Running on a quantum backend: This quantum circuit is then transpiled and run on the selected IBM quantum backend, where measurements are made.
Passing data to the quantum computer
After training the neural networks (autoencoder, CNN, and RNN), the program uses the encoded features from the autoencoder to create a quantum circuit. Specifically, the data extracted from the autoencoder is fed to the input of the quantum circuit.
Training and Prediction
Training Neural Networks: First, an autoencoder is trained on normalized data. After training, the autoencoder is used to extract features, which are then fed to the input of CNN and RNN for training and evaluation.
Using a Quantum Computer: The encoded features (output of the autoencoder) are used to create a quantum circuit. This data is passed through Hadamard gates to put it into a superposition state, and then RX gates are used to encode the data values.
The program uses three different neural networks for deep machine learning:
The program uses three neural networks:
Autoencoder for feature extraction and data dimensionality reduction.
CNN for classification based on the extracted features.
RNN for classification of sequential data based on the same features.
These three networks allow deep machine learning to be performed on the data, providing both processing and feature extraction, as well as subsequent classification and prediction.
Quantum computing and superposition can improve the output of trained AI models by adding new ways to analyze and process data that traditional computers cannot. In the context of this program, this could include:
Increasing computational complexity:
Quantum computers can process complex optimization and combinatorics problems much more efficiently than classical computers. This can help improve model parameters, find optimal hyperparameters, or make more accurate predictions based on complex dependencies in the data.
Improving prediction quality through quantum superposition:
Superposition allows quantum computers to process many states simultaneously. This can lead to more accurate and multidimensional predictions, since the quantum computer can evaluate many possible combinations of data in parallel.
How can this be applied in software?
1. Quantum Optimization
Quantum algorithms such as the Quantum Adjugated Base Optimization Algorithm (QAOA) or machine learning algorithms such as the Quantum Perceptron can be used to optimize neural network weights or model parameters. This can improve the accuracy of CNN and RNN models.
2. Quantum Analogies for Autoencoders
Autoencoders can be implemented in a quantum context for feature extraction. Quantum autoencoders can better capture complex correlations in data due to their ability to handle high-dimensional states and superposition.
3. Quantum Generative Models
Quantum generative models such as the Variational Quantum Autoencoder (QVAE) or the Quantum Generative Adversarial Network (QGAN) can generate new data based on trained models, which can improve predictions and increase the diversity of the training data.
Example of quantum computer enhancement
In your program, after extracting features using an autoencoder and training CNN and RNN models, you can use quantum computing in the following ways:
Quantum algorithm for feature enhancement:
Use a quantum circuit to analyze and transform the features extracted by the autoencoder. This can include quantum operations that can improve the quality of features by taking into account their multivariate interactions.
Quantum prediction:
After training the CNN and RNN, use a quantum computer to make predictions based on the encoded features. For example, a quantum algorithm can combine the output of a CNN and RNN using superposition to make a more accurate prediction.
Summary
Using quantum computing and superposition can significantly improve the output of trained AI models, providing new opportunities for data processing and optimization. In this program, this can be done through feature enhancement, quantum optimization of models, and using quantum predictions to improve the accuracy and quality of predictions.
