The convergence of artificial intelligence, financial modeling, and quantum computing is no longer theoretical—it is becoming an engineering reality. In this project, we implemented a quantum-inspired algorithm for cryptocurrency price forecasting using Amazon Braket, AWS’s fully managed quantum computing service. The goal was to explore how hybrid quantum-classical models can capture the highly nonlinear and chaotic dynamics of crypto markets more effectively than conventional machine learning models.
Architectural Overview
The architecture is built entirely on Amazon Braket’s environment, combining classical pre-processing and quantum circuit simulation within the same Jupyter notebook.
The workflow consists of four main stages:
Data Acquisition and Feature Engineering
Market data is obtained through public APIs (e.g., Yahoo Finance) and pre-processed to compute technical indicators such as lagged returns, moving averages, relative strength index (RSI), and short-term volatility. These features are standardized and reduced in dimension through PCA (Principal Component Analysis) to align with the limited qubit space of the quantum circuit.
Baseline Models
Before introducing quantum components, the system trains classical baselines (Linear Regression and Random Forest) as benchmarks for interpretability and reproducibility. These models establish the expected predictive accuracy of standard methods under identical data conditions.
Quantum Variational Regressor (VQC)
The quantum core of the system is a Variational Quantum Circuit (VQC) implemented via PennyLane’s Braket plugin. Each data point is embedded into a quantum state through rotation gates (RX, RZ) that encode normalized financial features.
The circuit’s parameters—rotation angles and entanglement layers—are optimized via gradient-based learning (Adam optimizer) to minimize a cost function defined over the next-day return (the percentage price change expected for the next trading session).
The model runs on braket.local.qubit with backend='default', an analytic local simulator included with Amazon Braket. This enables rapid prototyping and debugging without incurring any QPU cost or requiring S3 integration.
Evaluation and Comparison
Once trained, the hybrid model outputs a predicted return distribution for the following day. Its results are evaluated against classical baselines using mean absolute error (MAE) and R² metrics. The notebook also produces a visual comparison between predicted and actual returns, providing an intuitive view of how the quantum circuit approximates market dynamics.
Why Amazon Braket
Amazon Braket provides a unified environment to design, simulate, and execute quantum algorithms using either local simulators or managed devices (SV1, DM1, or real QPUs from IonQ and Rigetti).
In this workflow, the local simulator allows data scientists to iterate quickly, while managed devices can be used later to assess the circuit’s robustness under real quantum noise. The seamless integration with AWS services (S3, CloudWatch, IAM) ensures security, scalability, and enterprise-grade governance.
Outlook
Although current quantum hardware still operates under noise and qubit limitations, experiments like this demonstrate how financial forecasting can evolve toward quantum-ready architectures.
By combining classical feature extraction and quantum state encoding inside Amazon Braket, this approach establishes a reproducible framework for testing hybrid models that—when larger fault-tolerant QPUs become available—could outperform purely classical algorithms in capturing market complexity.
Contact
If you are interested in exploring this algorithm further, discussing its implementation, or accessing the code, feel free to reach out:
📧 Jordi Garcia Castillón – AI & Cybersecurity Consultant | Researcher in Quantum and AI Security
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