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Google Quantum A.I. Lab Team
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Experiment suggests that humans are capable of perceiving even the feeblest flash of light.
Michael B (TouchéComm)'s profile photoEdgar Bull's profile photoGuangyue Li's profile photo
If I want to use it ,what I do ?

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Using a small quantum system consisting of three superconducting qubits, researchers at UC Santa Barbara and Google have uncovered a link between aspects of classical and quantum physics thought to be unrelated: classical chaos and quantum entanglement. Their findings suggest that it would be possible to use controllable quantum systems to investigate certain fundamental aspects of nature.
Fred Sullenberger's profile photoClinton Siegle's profile photo
A speculative answer is this.
There is a multiverse the dimensional number I am guessing is somewhere between 10 to power 192 or more.
The number comes from Drake equation adding multiverses and asking the wrong question receiving an answer which did not make sense until I realized what had happened.
The person using the simulator either at CERN, D Wave or Google does not know the difference between exact and 100 percent replacement values in a Monte Carlo simulation question. Example you run a simulation with everything. Note the word everything or everyone not just your universe or a grouping of universes similar to your universe. Some how MineCraft and D wave computing or CERN interacts with another universe how or why wrong question at present. The issue is the person realizes their mistake because they see something that should not be. Their response is to say put everything back at 100 percent. If you have ever ran a monte carlo simulation you realize that out liners have to be placed and positioned exactly the way they were to be running to get them back exactly in their position. Meaning exact match and 100 percent to monte carlo simulation are two different things. D wave computer at max can process 10 to power of 8 meaning whomever was the analyst person screwed up and did not put everyone back where they should be. This represents Mandela effect. Every single person realizing something different has had their mind transported to a different universe. Completely different. This also goes along with facts and history. Examples Skechers or Sketchers, who is the princess in Wreck it Ralph Penelope or Vanalope, When did Bob Crane die and how in his sleep 1977 shot to death 1977, beaten to death with a camera pod 1978 or strangled 1979. The timelines are so messed up several ideas are pooping up that were not here before. Abe Lincoln in my universe was a senator not a representative. First representative that I recall being a president is Bush senior. Example Rainbow Universe which says that there is a universe for each color and timeframe due to the wavelength change in the speed of light meaning there is time shifts. I have seen this on holidays and events recent Republican convention was suppose to happen in my timeframe July 6 throught the 10th. Here I believe it happened July 18th through 21st. Example I have seen on CNN BREXIST UK stay in the UK on Tuesday and UK exist the EU on a Thursday. I was kind of lost until I realized what I was seeing and started paying attention more to the color changes around me. Deity wise the only answer is technology versus nose magic or understanding whom is giving something for doing something. write me I would like to hear your answers.
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Neil Capper's profile photo
I love reading the posts on Quantum Computing because it makes me realise that no matter how much I think I've got a grasp on things, actually, I'm just a dumb ape.
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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
Pier Luigi Caffese's profile photoTao Hong's profile photoDawn Pearce's profile photo
Please do more posts with frequency. Inquiring minds want to understand.
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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.
Edgar Bull's profile photoJames Edmond Smith's profile photoAlen Mark's profile photo
such a great work!!want to help you in everything.
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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.
Oleksandr Negodiuk's profile photo
The shortest distance between two planar graphs - a surjection center of the sphere!
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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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Samvith V Rao's profile photoDan McGuirk's profile photo
now I'll help it decide, I Palindrome I
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Ecaterina Kelly's profile photo
How is that post quantum cryptography when quantum cryptography isn't there yet, Google has already soul and is alive would you agree or is that solar entanglement in information field? 
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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.
Emanuele Ziglioli's profile photoQuark Master's profile photoAprii Reaume's profile photo
+Emanuele Ziglioli it is - that's Stuart Hameroff. This article has a few links to relevant papers and some good general information: 
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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)
Joshua Maier's profile photoRicardo Salazar's profile photoKevin Olivier's profile photo
+Ricardo Salazar ever heard of Miranda's web? 
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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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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.
Oleksandr Negodiuk's profile photoBrandon Khan's profile photoEdgar Bull's profile photo
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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.