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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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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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Computational multiqubit tunnelling in programmable quantum annealers

The Google Quantum A.I. Lab Team, together with external collaborators, has recently published a paper (http://www.nature.com/ncomms/2016/160107/ncomms10327/abs/ncomms10327.html) in Nature Communications which studies computational multiqubit tunnelling in programmable quantum annealers. Quantum annealing (QA) is an optimization technique inspired by classical simulated annealing (SA). SA is a global optimization algorithm that mimics classical thermal activation at a high enough initial algorithmic “temperature” to escape false local minima of an optimization function. As the temperature is lowered to distinguish between local minima with small energy differences, SA can freeze. The idea behind QA is to use quantum tunneling to escape local minima even at low temperature. Quantum tunnelling is a phenomenon in which a quantum state traverses energy barriers higher than the energy of the state itself

Despite substantial academic and industrial interest in QA, computational multiqubit tunnelling had not yet been observed, and a theory of co-tunnelling under realistic noise models (including low-frequency noise) was lacking. In this paper we introduce a 16-qubit probe for tunnelling, a computational primitive where classical paths are trapped in a false minimum. To distinguish between tunnelling and thermal activation, we study the thermal dependence of the probability of success for the computational primitive. Thermal activation shows an increasing probability of success with increasing temperature, as expected. Multiqubit tunnelling, on the other hand, shows a decreasing probability of success with increasing temperature, both in theory and experiment.

We performed our experiments to observe computational multiqubit tunneling in a D-Wave Two quantum annealer. On the one hand, we obtain a good agreement with a standard quantum open system master equation (Redfield) and the new theory introduced in this paper (multiqubit NIBA). On the other hand, we observe the opposite dependence of temperature if we use a related numerical model, Spin Vector Monte Carlo (SVMC) (http://arxiv.org/abs/1401.7087), which aims to mimic quantum annealing but does not include entanglement.

The theory of multiqubit tunneling introduced in our paper (multiqubit NIBA) explains the effect of low frequency noise at the multiqubit freezing point. As the annealing progresses, the physical environment of the qubits, and the low frequency noise in particular, induces transitions in the physical system. These transitions tend to thermalize the system at the low physical temperature. At some point, the system freezes. If it did not, a quantum annealer would solve most optimization problems, simply because the energy of sub-optimal solutions is much higher than the very low physical temperature. Our theory shows that in the multiqubit setting this freezing is related to an energy shift introduced by the low frequency noise, which is linear in the number of qubits.

We applied the insights gained in this work to construct proof-of-principle optimization problems and programmed these into the D-Wave 2X quantum annealer (http://www.dwavesys.com/press-releases/d-wave-systems-announces-general-availability-1000-qubit-d-wave-2x-quantum-computer) that Google operates jointly with NASA. The problems were designed to demonstrate that quantum annealing can offer runtime advantages for hard optimization problems characterized by rugged energy landscapes. We found that for problem instances involving nearly 1000 binary variables, QA significantly outperforms SA: it is more than 10^8 times faster than SA running on a single core. You can see this benchmark in a more recent paper (http://arxiv.org/abs/1512.02206) and a related blogpost (http://googleresearch.blogspot.com/2015/12/when-can-quantum-annealing-win.html).

Sergio Boixo

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Panel Discussion at IQIM's Quantum Summit, held at Caltech on January 27

How soon will we have quantum computers? In what ways will they transform our lives? Listen in as some of the top experts from tech companies working on quantum computing weigh in.

Panel moderator: Jennifer Ouellette (Senior Science Editor, Gizmodo.com)
Panelists (left to right): Ray Beausoleil (HP Labs), Charles Bennett (IBM), Parsa Bonderson (Microsoft Station Q), Jim Clarke (Intel), Raymond Laflamme (Institute for Quantum Computing), Hartmut Neven (Google)

https://www.youtube.com/watch?v=ZG0EGXZJlBA
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