Question-3: How can machine learning improve the performance of quantum algorithms for quantum chemistry?
Alan Aspuru-Guzik (University of Toronto)
Machine Learning for Quantum Chemistry
(Abstract will come soon….)
Sophia Economou (VirginiaTech – USA)
Variational Quantum Eigensolvers
Variational quantum eigensolvers (VQEs) constitute a class of hybrid quantum-classical algorithms that are envisioned to be appropriate for noisy intermediate scale quantum processors. The majority of VQEs focus on quantum simulation, and particularly finding properties of many-body quantum systems, such as the ground state energies of complicated molecules. In VQEs, the quantum processor is where the quantum state is variationally prepared and measurements are made, while the classical computer performs optimization. As such, VQEs can be thought of as training of quantum circuits. In this tutorial, I will present the concept of VQEs and focus on challenges in the state preparation, measurement, and optimization aspects, and discuss connections to machine learning.
Jarrod McClean (Google Quantum Artificial Intelligence Lab)
Probing the role of learning in computational chemistry with a quantum computation perspective
Quantum algorithms for the simulation of chemical systems promise to open up the ability to simulate reactions and novel materials to previously inaccessible accuracy and size. However, the perspective of quantum computation also teaches us about the limits of computation and its role in design or formulation of physical theories. Here we address some of these limitations by taking a learning perspective and explore the role of a quantum computer in such a setting. We highlight this with recent results on how the availability of data can cause some quantum problems thought to be challenging to simulate classically to become easy in a classical learning setting, but use this to hone our perspective on the ideal role for a fault tolerant quantum computer in chemistry.
Min-Hsiu Hsieh (University of Technology Sydney – Australia)
The power of quantum neural networks
Quantum neural networks (QNNs) have been broadly used in various works with different levels of claimed benefits. One of my research interests in quantum machine learning is to understand the power of QNNs. In this talk, I will first compare the expressive power of QNNs with Boltzmann machines. Next, I will provide our results on the learnability of QNNs in terms of its trainability and generalization. Finally, I will provide a few applications of QNNs on machine learning tasks and ground state approximations.
Roger Melko (Department of Physics & Astronomy, University of Waterloo – Canada)
Reconstructing quantum states with generative models
Generative models are a powerful tool in unsupervised learning, where the goal is to learn the unknown probability distribution that underlies a data set. Recently, it has been demonstrated that modern generative models adopted from industry are capable of reconstructing quantum states, given projective measurement data on individual qubits. These virtual reconstructions can then be studied with probes unavailable to the original experiment. In this talk I will outline the strategy for quantum state reconstruction using generative models, and show examples on experimental data from a Rydberg atom quantum simulator. I will discuss the continuing theoretical development of the field, including the exploration of powerful autoregressive models for the reconstruction of sign-problematic and mixed quantum states.