Day 1 - Wednesday, July 21

Isaac Chuang, MIT

Modern quantum algorithms originate historically from three disparate origins: simulation, search, and factoring. Today, we can now understand and appreciate all of these as being instances of a single framework, and remarkably, the essence is how the rotations of a single quantum bit can be transformed non-linearly by a simple sequence of operations. On the face of it, this is physically non-intuitive, because quantum mechanics is linear. The key is to think not about eigenvalues and closed systems, but instead, about singular values and subsystem dynamics.

Day 2 - Thursday, July 22

Dorit Aharanov, Hebrew University

Following the second quantum revolution, which had completely undermined how we think of algorithms, the last decade gave birth to a third quantum revolution - which has changed the way we think of physical experiments. I will demonstrate this with some examples of how quantum computational ideas such as quantum error correction and quantum algorithms can be used to enhance conventional quantum experiments, to achieve increased efficiency and precision in sensing, metrology, and more. I will then describe my recent attempt together with Cotler and Qi to generalize these developments and provide a universal mathematical model for quantum experiments, which we call Quantum algorithmic measurements (QUALMs). In this framework, we show that certain experimental tasks (such as determining the time reversal symmetry of a many body quantum system), can be performed exponentially more efficiently if enhanced with even simple quantum computational abilities. Improvements on our initial protocols were recently implemented experimentally on Sycamore (see Robert Huang's talk later today). These and other results which I will mention, suggest that quantum experiments constitute a new playground in which quantum-computational advantages can be exhibited.

https://arxiv.org/abs/2101.04634

Joonho Lee, Columbia University

Many-electron problems pose some of the greatest challenges in computational science, with important applications across many fields of modern science. Fermionic quantum Monte Carlo (QMC) methods are among the most powerful approaches to these problems. However, they can be severely biased when controlling the fermionic sign problem using constraints is necessary for scalability. Here we propose an approach that combines constrained QMC with quantum computing tools to reduce such biases. We experimentally implement our scheme using up to 16 qubits in order to unbias constrained QMC calculations performed on chemical systems with as many as 120 orbitals. These experiments represent the largest chemistry simulations performed on quantum computers (more than doubling the size of prior electron correlation calculations), while obtaining accuracy competitive with state-of-the-art classical methods. Our results demonstrate a new paradigm of hybrid quantum-classical algorithm, surpassing the popular variational quantum eigensolver in terms of potential towards the first practical quantum advantage in ground state many-electron calculations.

https://arxiv.org/abs/2106.16235