Predicting Many Properties of a Quantum System from Very Few Measurements

Predicting the properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a ‘classical shadow’, can be used to predict many different properties; order log(M) measurements suffice to accurately predict M different functions of the state with high success probability. The number of measurements is independent of the system size and saturates information-theoretic lower bounds. Moreover, target properties to predict can be selected after the measurements are completed. We support our theoretical findings with extensive numerical experiments. We apply classical shadows to predict quantum fidelities, entanglement entropies, two-point correlation functions, expectation values of local observables and the energy variance of many-body local Hamiltonians. The numerical results highlight the advantages of classical shadows relative to previously known methods

Understanding Novel Superconductors with Ab Initio Calculations

This chapter gives an overview of the progress in the field of computational superconductivity. Following the MgB2 discovery (2001), there has been an impressive acceleration in the development of methods based on Density Functional Theory to compute the critical temperature and other physical properties of actual superconductors from first-principles. State-of-the-art ab-initio methods have reached predictive accuracy for conventional (phonon-mediated) superconductors, and substantial progress is being made also for unconventional superconductors. The aim of this chapter is to give an overview of the existing computational methods for superconductivity, and present selected examples of material discoveries that exemplify the main advancements.Comment: 38 pages, 10 figures, Contribution to Springer Handbook of Materials Modellin

Variational Spin-Squeezing Algorithms on Programmable Quantum Sensors

Arrays of atoms trapped in optical tweezers combine features of programmable analog quantum simulators with atomic quantum sensors. Here we propose variational quantum algorithms, tailored for tweezer arrays as programmable quantum sensors, capable of generating entangled states on demand for precision metrology. The scheme is designed to generate metrological enhancement by optimizing it in a feedback loop on the quantum device itself, thus preparing the best entangled states given the available quantum resources. We apply our ideas to the generation of spin-squeezed states on Sr atom tweezer arrays, where finite-range interactions are generated through Rydberg dressing. The complexity of experimental variational optimization of our quantum circuits is expected to scale favorably with system size. We numerically show our approach to be robust to noise, and surpassing known protocols

Mixed-State Entanglement from Local Randomized Measurements

We propose a method for detecting bipartite entanglement in a many-body mixed state based on estimating moments of the partially transposed density matrix. The estimates are obtained by performing local random measurements on the state, followed by postprocessing using the classical shadows framework. Our method can be applied to any quantum system with single-qubit control. We provide a detailed analysis of the required number of experimental runs, and demonstrate the protocol using existing experimental data [Brydges et al., Science 364, 260 (2019)SCIEAS0036-807510.1126/science.aau4963]