Combining Machine Learning With Quantum Computing for a New Generation of Quantum Algorithms

Quantum computing is at a fascinating time. Vendors are delivering quantum computers with larger capacities every year. The open challenge is finding algorithms for the current era of quantum computing. In this era, noisy devices limit the number of gates executed before the quantum computer loses coherence. Therefore, the design space for quantum algorithms that could be competitive with quantum computers is quite strict: One requires applications that can potentially be more efficient than computations carried out on classical devices, yet employ a short number of quantum gates.

I'll present a new algorithm that my group is developing, in collaboration with NVIDIA, that employs machine learning and quantum computing to simulate chemical systems. This algorithm is intended to be part of a new generation of algorithms that harness the best of both worlds, and is a candidate for quantum computing advantage.