This repo contains my team (Entangled_Nets) submission for the QML Challenges in QHack 2021, a quantum machine learning hackathon. We achieved perfect score (2500).
The challenges are:
- A20: Measurement
Calculate the probability of a rotated qubit is in the ground state.
- A30: Expectation Values
Evaluate an expectation value for a measurement of a rotated qubit.
- A50: Entanglement
Calculate a tensor-product observable for an entangled state.
- B100: Exploring Quantum Gradients
Compute the gradient of the provided QNode (a quantum circuit on a particular device) using the parameter-shift rule.
- B200: Higher-Order Derivatives
Given a variational quantum circuit, compute the gradient and the Hessian of the circuit using the parameter-shift rule by hand (do not use PennyLane’s built-in gradient methods).
- B500: Finding the Natural Gradient
Calculate the Fubini-Study metric and using it to find the quantum natural gradient (QNG).
- C100: Optimizing a Quantum Circuit
Provided with a variational quantum circuit, find the minimum expectation value this circuit can produce by optimizing its parameters.
- C200: QAOA
Set up a QAOA circuit in PennyLane and use pre-optimized parameters to identify the maximum independent set of a graph with six nodes.
- C500: Variational Quantum Classifier
Design a variational quantum classifier that can classify unknown test data from the same distribution of the given data with an accuracy of more than 95%. Helpful paper: Data re-uploading for a universal quantum classifier.
- D100: Optimization Orchestrator
Implement the classical control flow and optimization portion of the VQE to find the ground state energy of a given Hamiltonian.
- D200: Ansatz Artistry
Design an ansatz for a class of Hamiltonians whose n-qubit eigenstates must have the form:
.
- D500: Moving On Up
Implement a variational method that will find the ground state, as well as the first two excited states of the provided Hamiltonian. Helpful paper: Variational Quantum Computation of Excited States.