Student Presenter(s): Joanna Pedretti, Ali Gedawi, Shwetha Jayaraj, Bryan Garay, and Sabrina Wang
Faculty Mentor: Yusui Chen
School/College: Engineering and Computing Sciences, Old Westbury

A Quantum Rubik Cube, meant to show the complexity of a task to generalize, create, and solve a Rubik's cube in various dimensional spaces. On a traditional 3 x 3 Rubik's cube, we rotate it in a 3-dimensional space to solve it on each of its respective faces. Classical methods using machine learning algorithms require much more time, money and effort to run. In this project we propose instead of classical algorithms and computers to solve a 3 x 3-dimensional problem in various quantum states, we propose to utilize a quantum computer, and develop new algorithms to solve, and subsequently lower the time, money, and effort. We will map each individual color as a quantum bit, their values represent the direction rather than the various colors. Thus, each rotation of the scrambled cube will be represented as a quantum gate acting on a collection of qubits. From this, a solved state represents each of these qubits pointing in the correct direction. We then can explore quantities to measure the distance between the solved and scrambled state, such as fidelity of quantum states. Once developing these algorithms, we can easily use this method to minimize the cost function to quickly solve and optimize systems to apply for machine learning algorithms on the quantum realm.