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Google Quantum A.I. Lab Team
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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 ( 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) (, 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 ( 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 ( and a related blogpost (

Sergio Boixo
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At Milan I propose changing name POST EXPO to
QUANTUM MI LABS.I invite all companies to start Quantum MI LABS,also in Fashion,Design and Quantum Energy.But my Govern love fossil energy,
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William Oliver visited the Google LA Quantum AI Lab on August 13, 2015.


Superconducting qubits are coherent artificial atoms assembled from electrical circuit elements. Their lithographic scalability, compatibility with microwave control, and operability at nanosecond time scales all converge to make the superconducting qubit a highly attractive candidate for the constituent logical elements of a quantum information processor. Over the past decade, spectacular improvement in the manufacturing and control of these devices has moved superconducting qubits from the realm of scientific curiosity to the threshold of technological reality. In this talk, we review this progress and present aspects of our work related to the quantum systems engineering of high-coherence devices and high-fidelity control. For more information: [1] J. Bylander, et al., Nature Physics 7, 565 (2011) [2] W.D. Oliver & P.B. Welander, MRS Bulletin 38, 816 (2013)


William D. Oliver is a Senior Staff Member at MIT Lincoln Laboratory in the Quantum Information and Integrated Nanosystems Group and a Professor of the Practice in the MIT Physics Department. He provides programmatic and technical leadership for programs related to the development of quantum and classical high-performance computing technologies. His interests include the materials growth, fabrication, design, and measurement of superconducting qubits, as well as the development of cryogenic packaging and control electronics involving cryogenic CMOS and single-flux quantum digital logic. 

Prior to joining MIT & Lincoln Laboratory in 2003, Will was a graduate research associate with Prof. Yoshihisa Yamamoto at Stanford University investigating quantum optical phenomena and entanglement of electrons in two-dimensional electron gas systems. He previously spent two years at the MIT Media Laboratory developing an interactive computer music installation called the Singing Tree as part of Prof. Tod Machover’s Brain Opera. 

Will has published 52 journal articles and 7 book chapters, is an active seminar lecturer, and is inventor or co-inventor on two patents. He serves on the US Committee for Superconducting Electronics; is an Applied Superconductivity Conference (ASC) Board Member; and is a member of the American Physical Society, IEEE, Sigma Xi, Phi Beta Kappa, and Tau Beta Pi. In 2013, he was a JSPS visiting researcher at the University of Tokyo. 

Will received his PhD in Electrical Engineering from the Stanford University in 2003, the SM in Electrical Engineering and Computer Science from MIT in 1997, and a BS in Electrical Engineering and BA in Japanese from the University of Rochester (NY) in 1995.
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Good luck on your proj!
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Guilhem Semerjian visited the Quantum AI Lab at Google LA to give a talk on "Random Constraint Satisfaction Problems, Classical and Quantum Results". This talk already took place on May 29, 2014 but we only publish it now because it contains material that was still unpublished at the time of the presentation.


In the 90's numerical simulations have unveiled interesting properties of random ensembles of constraint satisfaction problems (satisfiability and graph coloring in particular). When a parameter of the ensemble (the density of constraints per variable) increases the probability of a satisfying instance drops abruptly from 1 to 0 in the large size limit. This threshold phenomenon has motivated a lot of research activity in theoretical computer science and in mathematics. In addition non-rigorous methods borrowed from theoretical physics (more precisely from mean-field spin glasses) have been applied to such problems, yielding a series of new results, including quantitative conjectures on the location of the satisfiability threshold and a much more detailed description of the structure of the satisfiable phase. I will survey some of these results, and their extensions to the quantum case where a transverse field is added to these classical Hamiltonians.


Guilhem Semerjian has applied techniques developed in statistical mechanics to interdisciplinary problems arising at the frontier of theoretical computer science and discrete mathematics, in particular satisfaction of random constraint satisfaction problems. He has also been interested in the effect of quantum fluctuations on these problems, within the perspective of adiabatic quantum computing.
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Christopher Monroe visited the Google LA Quantum AI Lab on May 4, 2015 to talk about "Modular Ion Trap Quantum Networks: Going Big":


Laser-cooled and trapped atomic ions are standards for quantum information science, acting as qubits with unsurpassed levels of quantum coherence while also allowing near-perfect measurement. Trapped ions can be entangled locally with external laser beams that map the internal atomic qubits through their Coulomb-coupled motion; they can be entangled remotely through optical photons traveling through fibers. This quantum hardware platform scales favorably when compared to any other platform, because (a) each trapped ion is an atomic clock that is identical to the others, (b) the qubit wiring (interaction graph) is provided by external fields and is hence reconfigurable, and (c) the modular architecture of local and remote quantum gates provides a realistic path to building out huge systems with thousands of qubits. I will summarize the state-of-the art in ion trap quantum networks, which will soon involve systems of 50+ fully-interacting qubits, eclipsing the performance of classical computers for certain tasks. The remaining challenges in the ion trap architecture are primarily engineering, setting the stage for focused efforts to build a large scale quantum computer.


Christopher Monroe is an quantum physicist who specializes in the isolation of individual atoms for applications in quantum information science. After graduating from MIT, Monroe studied with Carl Wieman and Eric Cornell at the University of Colorado, earning his PhD in Physics in 1992. His work paved the way toward the achievement of Bose-Einstein condensation in 1995 and led to the 2001 Nobel Prize for Wieman and Cornell. From 1992-2000 he was a postdoc then staff physicist at the National Institute of Standards and Technology, in the group of David Wineland. With Wineland, Monroe led the team that demonstrated the first quantum logic gate in 1995, and exploited the use of trapped atoms for the first controllable qubit demonstrations, eventually leading to Wineland's Nobel Prize in 2012. In 2000, Monroe became Professor of Physics and Electrical Engineering at the University of Michigan, where he pioneered the use of single photons to couple quantum information between atoms and also demonstrated the first electromagnetic atom trap integrated on a semiconductor chip. From 2006-2007 was the Director of the National Science Foundation Ultrafast Optics Center at the University of Michigan. In 2007 he became the Bice Zorn Professor of Physics at the University of Maryland and a Fellow of the Joint Quantum Institute. In 2008, Monroe's group succeeded in producing quantum entanglement between two widely separated atoms and for the first time teleported quantum information between matter separated by a large distance. Since 2009 his group has investigated the use of ultrafast laser pulses for speedy quantum entanglement operations, pioneered the use of trapped ions for quantum simulations of many-body models related to quantum magnetism, and with Jungsang Kim (Duke University) has proposed and made the first steps toward a scalable, reconfigurable, and modular quantum computer.
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Daniel Lidar visited the Quantum AI Lab at Google LA to give the talk: "Quantum Information Processing: Are We There Yet?" This talk took place on January 22, 2015.


Quantum information processing holds great promise, yet large-scale, general purpose quantum computers capable of solving hard problems are not yet available despite 20+ years of immense effort. In this talk I will describe some of this promise and effort, as well as the obstacles and ideas for overcoming them using error correction techniques. I will focus on a special purpose quantum information processor called a quantum annealer, designed to speed up the solution to tough optimization problems. In October 2011 USC and Lockheed-Martin jointly founded a quantum computing center housing a commercial quantum annealer built by the Canadian company D-Wave Systems. A similar device is operated by NASA and Google. These processors use superconducting flux qubits to minimize the energy of classical spin-glass models with as many spins as qubits, an NP-hard problem with numerous applications. There has been much controversy surrounding the D-Wave processors, questioning whether they offer any advantage over classical computing. I will survey the recent work we have done to benchmark the processors against highly optimized classical algorithms, to test for quantum effects, and to perform error correction.


Daniel Lidar has worked in quantum computing for nearly 20 years. He is a professor of electrical engineering, chemistry, and physics at USC, and hold a Ph.D. in physics from the Hebrew University of Jerusalem. His work revolves around various aspects of quantum information science, including quantum algorithms, quantum control, the theory of open quantum systems, and theoretical as well as experimental adiabatic quantum computation. He is a Fellow of the AAAS, APS, and IEEE. Lidar is the Director of the USC Center for Quantum Information Science and Technology, and is the Scientific Director of the USC-Lockheed Martin Center for Quantum Computing. Two of his former graduate students are now research scientists at Google’squantum artificial intelligence lab.
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+John Olson As a joke referencing qubits being 1 and 0? 
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The Quantum Artificial Intelligence Laboratory is launching a collaborative program by allocating 20% of the compute time of the D-Wave Two quantum annealer at NASA Ames to researchers interested in artificial intelligence algorithms and advanced programming techniques (mapping, decomposition, embedding) for quantum annealing, with the objective to advance the state-of-the-art in quantum computing and its application to artificial intelligence. The program is administered by the Universities Space Research Association (USRA). For details please follow the link:
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Interesting to see Google hosting Nobelist Frank Wilczek.

In his lecture, he talks about color vision v. action, symmetry, gauge theory and the higher dimensions of M-theory.

I've been going on about these matters for 40 years, but what do I know?
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Have them in circles
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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,
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)
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These particles that are contained in these. Are smaller then atoms 500,000 equaling the size of an atomic and they defy the laws of physics right? Also this AI should be able to evolve it's self and think for its self.. right?
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Adiabatic Quantum Computing (AQC) and Quantum Annealing are computational methods that have been proposed to solve combinatorial optimization and sampling problems. Several efforts are now underway to manufacture processors that implement these strategies. The Fifth International Conference on ...
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Alán Aspuru-Guzik visited the Quantum AI Lab at Google LA on May 12, 2015 and gave this talk: "Billions and Billions of Molecules: Molecular Materials Discovery in the Age of Machine Learning"


Many of the challenges of the twenty-first century are related to molecular processes such as the generation, transmission, and storage of clean energy, water purification and desalination. These transformations require a next generation of more efficient and ecologically-friendly materials. In the life sciences, we face similar challenges, for example drug-resistant bacterial strains require novel antibiotics. One of the paradigm shifts that the theoretical and experimental chemists needs to embrace is that of accelerated molecular discovery: The design cycles need to be sped up by the constant interaction of theoreticians and experimentalists, the use of high-throughput computational techniques, tools from machine learning and big data, and the development of public materials databases. I will describe three projects from my research group that aim to operate in this accelerated design cycle. First, I will describe our efforts on the Harvard Clean Energy Project (, a search for materials for organic solar cells. I will continue by talking about our work on developing organic molecules for energy storage in flow batteries. Finally, I will describe our work towards the discovery of novel molecules for organic light-emitting diodes. If time permits, I will talk about molecular networks related to the origins of life.


Professor Alán Aspuru-Guzik is currently Professor of Chemistry and Chemical Biology at Harvard University. He began at Harvard in​ 2006 and ​was promoted to Full Professor in 2013. Alán received his B.Sc.​ Chemistry from the National Autonomous University of Mexico (UNAM) in 1999. He received the Gabino Barreda Medal from UNAM. ​He obtained a PhD in Physical Chemistry from the University of California, Berkeley in 2004, under Professor William A. Lester, Jr., he was a postdoctoral scholar in the group of Martin Head-Gordon at UC Berkeley from 2005-2006. In 2009, Professor Aspuru-Guzik received the DARPA Young Faculty Award, the Camille and Henry Dreyfus Teacher-Scholar award and the Sloan Research Fellowship. In 2010, he received the Everett-Mendelsson Graduate Mentoring Award and received the HP Outstanding Junior Faculty award by the Computers in Chemistry division of the American Chemical Society. In the same year, he was selected as a Top Innovator Under 35 by the Massachusetts Institute of Technology Review magazine. In 2012, he was elected as a fellow of the American Physical Society, and in 2013, he received the ACS Early Career Award in Theoretical Chemistry. He is associate editor of the journal Chemical Science.

Professor Aspuru-Guzik carries out research at the interface of quantum information and chemistry. In particular, he is interested in the use of quantum computers and dedicated quantum simulators for chemical systems. He has proposed quantum algorithms for the simulation of molecular electronic structure, dynamics and the calculation of molecular properties. He recently has proposed two new approaches for quantum simulation: the variational quantum eigensolver and the adiabatic quantum chemistry approach. He also proposed the demon-like algorithmic cooling algorithm. He has studied the role of quantum coherence in excitonic energy transfer in photosynthetic complexes. Alán has been involved as a theoretician in several experimental demonstrations of quantum simulators using quantum optics, nuclear magnetic resonance, nitrogen vacancy centers and recently superconducting qubits.

​Alán develops methodology for the high-throughput search of organic materials, especially organic materials. This has led to his discovery of candidate molecules for high mobility organic semiconductors, organic flow battery molecules and high-performance molecules for organic light-emitting diodes. Alan is very interested in the interface of machine learning and material discovery and has carried out the largest set of quantum chemistry calculations to date.
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The shortest distance between two planar graphs - a surjection center of the sphere!
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+Edgar Bull Feel free to email me at for additional info :)
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Matthias Troyer visited the Google Quantum AI Lab to speak about "High Performance Quantum Computing". This talk took place on December 2, 2014.


As the outlines of a roadmap to building powerful quantum devices becomes more concrete an important emerging question is that of important real-world applications of quantum computers. While there exist many quantum algorithms which asymptotically outperform classical algorithms, asymptotic superiority can be misleading. In order for a quantum computer to be competitive, it needs to not only be asymptotically competitive but be able to solve problems within a limited time (for example one year) that no post-exa-scale classical supercomputer can solve within the same time. This search for a quantum killer-app turns out to be a formidable challenge. Using quantum chemistry simulations as a typical example, it turns out that significant advances in quantum algorithms are needed to achieve this goal. I will review how substantial improvements and optimized massively parallel implementation strategies of quantum algorithms have brought the problem of quantum chemistry from the realm of science fiction closer to being realistic. Similar algorithmic improvements will be needed in other areas in order to identify more “killer apps” for quantum computing. I will end with a short detour to quantum annealers and present a summary of our recent results on simulated classical and quantum annealing.


Matthias Troyer is professor of computational physics at ETH Zurich where he teaches advanced C++ programming, high performance computing, and simulations methods for quantum systems. He is a pioneer of cluster computing in Europe, having been responsible for the installation of the first Beowulf cluster in Europe with more than 500 CPUs in 1999, and the most energy efficient general purpose computer on the top-500 list in 2008. He is a Fellow of the American Physical Society and his activities range from quantum simulations and quantum computing to the development of novel simulation algorithms, high performance computing, and computational provenance. He is, the author of the Boost MPI C++ library for message passing on parallel computers, and the leader of the open-source ALPS library for the simulation of quantum many body systems.
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How do you know that superposition has been achieved and being implemented in the computer. Thanks !!
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Nice cartoon by Theoretical Physicists John Preskill and Spiros Michalakis describing how things are different in the Quantum World and how that can lead to powerful Quantum Computers.

Quantum Computers Animated
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5:40 isn't that minecraft text?
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News and updates from the Quantum A.I. Lab's corner of the multiverse
The Quantum Artificial Intelligence Lab is a collaboration between Google, NASA Ames Research Center and USRA. We're studying the application of quantum optimization to difficult problems in Artificial Intelligence.

Follow this page for news and discussion about quantum computing, and updates from the team at the Quantum A.I. Lab. 

You can learn more about the lab and its mission here.