One of the most challenging and important tasks in chemistry is finding the stable geometry of molecules. Classically the problem is computationally intensive. Hence, there is huge interest in quantum computers to solve this problem. The problem can be formulated as an optimization problem, wherein we minimize the energy by placing the molecules properly. This can be achieved by treating this as a variational problem and solving it using VQE. VQE is the algorithm of choice as it has proven itself the best algorithm to run on current NISQ machines. However, is there a way we can make VQE even more efficient and resilient to noise?
The answer is yes! What if we could reduce the number of qubits required for the problem? This would translate to massive gains in noise resiliency. In our project, we aim to do so using the Qubit Efficient Encoding. We contrasted our method against the standard VQE method calculating the H3+ molecules and fewer qubits are required (reducing it from 6 to 4!) for encoding Hamiltonian. This method has huge potential in saving the computational resource and hence provides more noise resistance. In the future, we will also explore the extension of QEE method to simulate the dynamical systems.
Team Name:
BladeRunner
Project Description:
One of the most challenging and important tasks in chemistry is finding the stable geometry of molecules. Classically the problem is computationally intensive. Hence, there is huge interest in quantum computers to solve this problem. The problem can be formulated as an optimization problem, wherein we minimize the energy by placing the molecules properly. This can be achieved by treating this as a variational problem and solving it using VQE. VQE is the algorithm of choice as it has proven itself the best algorithm to run on current NISQ machines. However, is there a way we can make VQE even more efficient and resilient to noise?
The answer is yes! What if we could reduce the number of qubits required for the problem? This would translate to massive gains in noise resiliency. In our project, we aim to do so using the Qubit Efficient Encoding. We contrasted our method against the standard VQE method calculating the H3+ molecules and fewer qubits are required (reducing it from 6 to 4!) for encoding Hamiltonian. This method has huge potential in saving the computational resource and hence provides more noise resistance. In the future, we will also explore the extension of QEE method to simulate the dynamical systems.
Presentation:
https://docs.google.com/presentation/d/1lKY0P5rQSDCLQkPKvkeHMzE5Hakn4-2rP0qmInWVTkc/edit?usp=sharing
Source code:
https://github.com/yulunwang/QHack-OptimizeStructure
Which challenges/prizes would you like to submit your project for?
IBM Qiskit Challenge, Hybrid Algorithms Challenge, Quantum Chemistry Challenge, Science Challenge, Simulation Challenge