Quantum Random Forest Model Prediction Circuit Gate Complexity

Key Points

• The research aims to develop a quantum random forest model focusing on its gate complexity for classification tasks.
• Developed quantum random forest prediction circuits for both binary and multiclass classification.
• Defined gate complexity as the count of elementary one- and two-qubit gates.
• Compared gate usage with the general upper bound to assess efficiency.
• Achieved fewer elementary gates than the general upper bound for prediction circuits.
• Demonstrated feasibility for both binary and multiclass classification problems.