Quantum Computing

Quantum Computing

Our group is one of the pioneering groups of quantum computing algorithm research. Alán began research at the interface of quantum computing and chemistry and materials since 2004.

Quantum computers have potential to be the next revolution in computing and their hardware capabilities grow tremendously every year. Indeed, in 2019, the first computation was performed by a Google quantum computer that can run a certain algorithm significantly faster than the world’s best supercomputer at that time. With these developments in mind, there are three pillars of research areas our quantum subgroup focuses on.

The first pillar of our research is to develop Noisy Intermediate-Scale Quantum (NISQ) algorithms that will disrupt the fields of computational and materials chemistry. By leveraging state-of-the-art techniques in quantum chemistry and machine learning, we are building the tools necessary to propel theoretical chemistry into the new computing paradigm. A core example is our Variational Quantum Eigensolver (VQE) algorithm, which is a key component of NISQ hybrid quantum-classical algorithms. We actively research different methods for reducing hardware resource requirements and noise-resilient quantum algorithms which will push forward the quantum computing research frontiers.

The second pillar of our work is quantum machine learning algorithms development where we are interested to employ both quantum data and quantum learning techniques. One such example is quantum Generative Adversarial Learning. We are also interested in applying classical machine learning techniques to quantum computing hardware discovery, such as design of multi-qubit couplers for superconducting circuit based quantum computers. These designs could significantly improve second-generation quantum computers.

The third pillar is quantum resource optimization and systematic data structuring. Our lab has traditions of spearheading open-source packages and software to integrate with NISQ hardwares. The efforts include Tequila, an open source python library for the development of novel quantum algorithms), qHiPSTER (quantum high performance simulator), and OpenFermion (open source library for compiling and analyzing quantum algorithms to simulate fermionic systems including quantum chemistry).

We strive to continue to develop novel algorithms for different applications of quantum computers and run them on near-term quantum computers.