Stock Price Prediction with Quantum Machine Learning in Python

An overview of the challenges and opportunities

Photo by Anton Maksimov 5642.su on Unsplash

Today, we’re diving into the intersection of quantum computing and machine learning, exploring quantum machine learning. Our main goal is to compare the performance of a quantum neural network for stock price time series forecasting with a simple single-layer MLP.

To facilitate this project, we’ll be utilizing the Historical API endpoint offered by Financial Modeling Prep (FMP) for reliable and accurate data which is very critical. With that being said, let’s dive into the article.

Importing the Data

Let’s start by importing the necessary libraries for our analysis. These libraries will provide the basic tools required to explore and implement our project.

import numpy as np
import pandas as pd
import requests
import json
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.metrics import mean_squared_error
from qiskit import QuantumCircuit
from qiskit.circuit.library import PauliFeatureMap
from qiskit.algorithms.optimizers import ADAM
from qiskit.circuit import Parameter
from qiskit.primitives import Sampler