Implementing a Quantum Approximate Optimization Algorithm on a 53-Qubit NISQ Device

(Phys.org) A large team of researchers working with Google Inc. and affiliated with a host of institutions in the U.S., one in Germany and one in the Netherlands has implemented a quantum approximate optimization algorithm (QAOA) on a 53-qubit noisy intermediate-scale quantum (NISQ) device.
In this new effort, the researchers created a QAOA and ran it on Google’s state of the art NISQ computing platform. As Harvard’s Boaz Barak notes in Nature Physics, their QAOA worked as a combination of smaller algorithms that have been created to run simulations on a quantum computer, such as simulated annealing. Such algorithms begin by presenting a random answer and then seek to improve upon it using quantum operators. Using the algorithm, researchers learned more about ways to reduce noise or mitigate its effects. They also learned more about the use of hyperparameters and possible ways to map key problems onto a quantum architecture.