Day 2 - Thursday, July 22
Dorit Aharanov, Hebrew University
The 3rd quantum revolution: Quantum Algorithmic Experiments
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.
Joonho Lee, Columbia University
Unbiasing Fermionic Quantum Monte Carlo with a Quantum Computer
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.
Austin Minnich, Columbia University
Towards physical quantum relevancy on superconducting quantum computers: electronic structure of bioinorganic clusters and symmetry-protected topological chains
Physical quantum simulation is a promising near-term application of emerging quantum hardware. The capabilities of superconducting quantum devices have increased dramatically over the past 5 years from few qubit simulations to those involving tens of qubits and hundreds of entangling gates. Present devices are thus expected to enable quantum simulations to move beyond toy models to those of physical relevance. In this talk, I will describe our team’s simulations of two physically relevant systems in chemistry and condensed matter physics. The simulations, of the electronic dynamics of iron sulfur clusters and the preparation of a symmetry-protected topological phase, employ close to 10 qubits and around a hundred entangling gates and are thus among the most complex simulations performed on superconducting hardware to date.
Sun et al, PRX Quantum
Robert Huang, IQIM, Caltech
Experimental advantage in learning with noisy quantum memory
Quantum technology has the potential to enhance our ability to learn about the physical world through both simulation and experiment. With the rapid development of quantum computers aiming towards fault-tolerant operation and long-lived quantum memories, it is natural to try to understand the roles this technology can play, not just in simulation but in processing and learning raw data from our physical world. In this work, we prove that machines with the ability to keep states in quantum memory can learn from exponentially fewer experiments than those with only classical memory in many tasks. Examples include predicting many highly incompatible observables, learning unknown quantum dynamics, and performing quantum principal component analysis. Furthermore, we underscore the power of quantum memory and its accessibility to near-term implementation by experimentally demonstrating an advantage by training a machine learning model that can utilize a noisy quantum memory of at least 40 qubits. These results pave a way towards using quantum sensors, quantum memories, and machine learning to advance physical sciences.
Google scholar page
Ryan Babbush, Google
A Google perspective on the most viable applications of early fault-tolerant quantum computers
This talk will survey the landscape of applications for small fault-tolerant quantum computers. First, we will discuss barriers to achieving quantum advantage with algorithms delivering only a quadratic speedup (arXiv:2011.04149), and the implications of this for popular applications such as Monte Carlo based pricing of financial derivatives and combinatorial optimization (arXiv:2007.07391). Then, we will discuss areas where larger speedups seem possible, including the solving of linear systems and differential equations, and quantum simulation. We will discuss several of our team's recent papers on quantum simulating chemistry including arXiv:2011.03494 and arXiv:2105.12767.