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Google Quantum A.I. Lab Team
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News and updates from the Quantum A.I. Lab's corner of the multiverse
News and updates from the Quantum A.I. Lab's corner of the multiverse

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A comprehensive and well researched cover story in "The Economist" about quantum technologies.

http://www.economist.com/technology-quarterly/2017-03-09/quantum-devices

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We published a comment in Nature outlining the Quantum AI team's thoughts on commercializing near-term quantum computing technologies.

http://www.nature.com/news/commercialize-early-quantum-technologies-1.21583

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Boris Altshuler visited the Google Quantum AI Lab on November 10, 2016.

Abstract
Quantum Computers (QC) consist of a large number of interacting quantum bits. Solutions of computational problems are encoded in bit-strings which result from problem-specific manipulations. In contrast with Classical Computers, the state of a QC is characterized by a quantum superposition of the bit-strings (a wave function) rather than by a particular bit-string representing a computational basis. Instead of usual focus on quantum algorithms, here we will discuss QC using concepts from many-body physics as quantum dynamical systems. Recent progress in understanding the dynamics of quantum systems with large number of degrees of freedom is based on the concept of Many-Body Localization: the eigenstates can be localized in the Hilbert space in a way similar to the conventional real space Anderson Localization of a single quantum particle by a quenched disorder. Depending on the temperature (total energy) or other tunable parameters the system can find itself either in the localized or in the many-body extended phase. In the former case, the system of interacting quantum particles/spins cannot be described in terms of conventional Statistical Mechanics: the notion of the thermal equilibrium loses its meaning. Moreover the violation of the conventional thermodynamics does not disappear with the Anderson transition to an extended state. In a finite range of the tunable parameters we expect the non-ergodic extended phase: the many-body wave-functions being extended are multifractal in the Hilbert space making thermal equilibrium unreachable in any reasonable time scale. It means the system by itself keeps some memory of its original quantum state. This property can be extremely useful for quantum computation, which cannot be implemented without connection between the remote parts of the Hilbert space, i.e. states localized in the computational basis are useless. The ergodic states should also be avoided: in the Hilbert space of high dimension they easily lose the quantum information. We will discuss evidences for the existence of delocalized non-ergodic systems and speculate about their properties by comparing them with non-integrable classical dynamical systems such as Solar Systems.

Speaker Bio
Boris Altshuler works in the field of Condensed Matter theory. He made substantial contributions to the understanding of the effects of disorder, quantum interference and interactions between electrons on the properties of bulk, low-dimensional, and mesoscopic conductors. Boris was educated in Russia. He graduated from the Leningrad (now St. Petersburg) State University and joined Leningrad Institute for Nuclear Physics first as a graduate student and later as a member of the research stuff. His PhD thesis advisor was Arkadii Aronov. After moving to USA Boris was on faculty of the Massachusetts Institute of Technology and later of the Princeton University. He was also a Fellow of NEC laboratories America (Princeton, NJ). Now he is a professor of Physics at Columbia University. Boris Altshuler is a recipient of a number of scientific awards - the most significant are 1993 Hewlett-Packard Europhysics Prize (Agilent Prize) and 2003 Oliver Buckley Prize of American Physical Society. He is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. He is also a foreign member of The Norwegian Academy of Science and Letters and of the Academy of Romanian Scientists.

https://www.youtube.com/watch?v=NVuFWl1UcQQ

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Garnet Chan visited the Google LA Quantum AI Lab on October 6, 2016.

Abstract
Quantum mechanics is the fundamental theory underlying all of chemistry, materials science, and the biological world, yet solving the equations appears to be an exponentially hard problem. Is there hope to simulate the quantum world using classical computers? I will discuss why simulating quantum mechanics is not usually as hard as it first appears, and give some examples of how modern day quantum mechanical calculations are changing our understanding of practical chemistry and materials science.

Speaker Bio
Garnet Chan recently joined the Cal Tech faculty as the Bren Professor in Chemistry. Before that he was the A. Barton Hepburn Professor of Chemistry at Princeton University, where he was also a member of the physics faculty. Professor Chan received his PhD from the University of Cambridge in 2000. He was born in London and grew up in Hong Kong. Professor Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, and is concerned with quantum many-particle phenomena and the numerical methods to simulate them. Over the last decade, his group has contributed to and invented a variety of methods addressing different aspects of quantum simulations, ranging from the challenges of strong electron correlation, to treating many-particle problems in the condensed phase, to dynamical simulations of spectra and coupling between electron and nuclear degrees of freedom. Some of these methods include density matrix renormalization and tensor network algorithms for real materials, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings including dynamical mean-field theory and density matrix embedding theory, and new quantum Monte Carlo algorithms. The primary focus is on methodologies for problems which appear naively exponentially hard, but where an understanding of inherent physics, for example in terms of the entanglement structure, allows for calculations of polynomial cost.

https://www.youtube.com/watch?v=86x0_-JGlGQ

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“...nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical...”
Richard Feynman
Simulating Physics with Computers

One of the most promising applications of quantum computing is the ability to efficiently model quantum systems in nature that are considered intractable for classical computers. In collaboration with Harvard, Lawrence Berkeley National Labs, UC Santa Barbara, Tufts University and University College London, we have performed the first completely scalable quantum simulation of a molecule. Learn more in the Google Research blog, linked below.

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Artificial Intelligence and Machine Consciousness

Two talks on Quantum Computing and Deep Neural Networks by Googlers Hartmut Neven and Christian Szegedy presented at the Science of Consciousness Conference 2016 in Tucson, Arizona.

https://www.youtube.com/watch?v=iuhmBFbY4Xw&list=PLl_UXfN1hubVda8RyXj1FLgwK4tW_AqEA&index=6
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