Quantum Algorithms Team Overview
Quantum Algorithms for Chemical Sciences
Computing driven by quantum mechanics affords a powerful computation paradigm in which information storage and processing can occur in a truly parallel fashion over a computational space that scales exponentially with the number of available bits. As such, quantum computing has the potential to be a world-changing advancement in computing to solve well-defined scientific objectives. Achieving success will require an integrated team of scientists developing quantum algorithms, closely working together with computer scientists, applied mathematicians and the quantum hardware developers.
Fostering close interactions between algorithm developers, computer scientists, applied mathematicians and quantum hardware platform developers to optimally design, create and run novel algorithms that will advance scientific discovery in chemical sciences.
Quantum chemistry simulations have been an early exemplar of quantum computing, demonstrating the potential of various types of quantum devices to aid in scientific discovery in the chemical sciences. The team will focus on the development of new classes of algorithms that, for the first time, will be able to capture time dynamics of physical systems on near-term devices. Our team will advance the rapidly-growing field of quantum machine learning by developing a quantum autoencoder that can compresses quantum data into a subspace and then decompresses it and applying it to scientific problems relevant in chemical sciences.
Optimal mapping of algorithms to quantum circuits is key to enable them to run on near term quantum devices constrained by the number of operations that can be performed. Our team is developing efficient compiling and optimization techniques and software tools, within open-source frameworks, that provide an effective implementation and execution of the algorithms.
Mathematical advances will go hand-in-hand with the development of new and quantum computer resource efficient algorithms. Our team is developing stochastic optimization algorithms that lead to accelerated convergence of the hybrid quantum-classical algorithms we will exploit to minimize the number of consecutive operations performed on a quantum device. We will also explore quantum linear algebra solvers targeting our quantum algorithms on time dynamics and machine learning.