Task1: ML task: file_name: ml_task1.pdf or ml_task.py Applied ML to solve a High Energy Data analysis issue, more specifically, separating the signal events from the background events.
Imrovement techniques used:
- Reduced overfitting with dropouts
- Reduced overfitting by ignoring the last feature column
- Normalize data
Further improvements:
- Better hyperparameter(learning rate) search techniques can be employed.
- Use ensemble of models
- More data, more features may improve results
Accuracy on training data: 0.8199999928474426 Error on training data: 0.18000000715255737 Accuracy on test data: 0.7400000095367432 Error on test data: 0.25999999046325684
Quantum Computing part: Task2: file_name=quantum_1.py or quantum_1.pdf implemented a simple quantum operation with Cirq 1.With 5 qubits 2.Applied Hadamard operation on every qubit 3.Applied CNOT operation on (0, 1), (1,2), (2,3), (3,4) 4.SWAPED (0, 4) 5. Rotated X with pi/2 6. Plotted the circuit
Task 3: file_name=quantum_2.py or quantum_2.pdf Created a circuit that is a series of small cirq.Rx rotations and plot the probability of measuring the state in the |0⟩ state.