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Google Quantum A.I. Lab Team
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Jeremy O'Brien visited the Google Quantum AI Lab to deliver the talk: "Quantum Technologies." This talk took place on April 1, 2014.


The impact of quantum technology will be profound and far-reaching: secure communication networks for consumers, corporations and government; precision sensors for biomedical technology and environmental monitoring; quantum simulators for the design of new materials, pharmaceuticals and clean energy devices; and ultra-powerful quantum computers for addressing otherwise impossibly large datasets for machine learning-artificial intelligence applications. However, engineering quantum systems and controlling them is an immense technological challenge: they are inherently fragile; and information extracted from a quantum system necessarily disturbs the system itself. Despite these challenges a small number of quantum technologies are now commercially available. Delivering the full promise of these technologies will require a concerted quantum engineering effort jointly between academia and industry. We will describe our progress in the Centre for Quantum Photonics to delivering this promise using an integrated quantum photonics platform---generating, manipulating and interacting single particles of light (photons) in waveguide circuits on silicon chips.


Jeremy O'Brien is professor of physics and electrical engineering and director of the Centre for Quantum Photonics (CQP). He received his Ph.D. in physics from the University of New South Wales in 2002 for experimental work on correlated and confined electrons in organic conductors, superconductors and semiconductor nanostructures, as well as progress towards the fabrication of a phosphorus in silicon quantum computer. As a research fellow at the University of Queensland (2001-2006) he worked on quantum optics and quantum information science with single photons. CQP's efforts are focused on the fundamental and applied quantum mechanics at the heart of quantum information science and technology, ranging from prototypes for scalable quantum computing to generalised quantum measurements, quantum control, and quantum metrology.
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Eric Ladizinsky visited the Google Quantum AI Lab to give a talk "Evolving Scalable Quantum Computers." This talk took place on March 5, 2014.


"The nineteenth century was known as the machine age, the twentieth century will go down in history as the information age. I believe the twenty-first century will be the quantum age". Paul Davies

Quantum computation represents a fundamental paradigm shift in information processing. By harnessing strange, counterintuitive quantum phenomenon, quantum computers promise computational capabilities far exceeding any conceivable classical computing systems for certain applications. These applications may include the core hard problems in machine learning and artificial intelligence, complex optimization, and simulation of molecular dynamics .. the solutions of which could provide huge benefits to humanity. 

Realizing this potential requires a concerted scientific and technological effort combining multiple disciplines and institutions ... and rapidly evolving quantum processor designs and algorithms as learning evolves. D-Wave Systems has built such a mini-Manhattan project like effort and in just a under a decade, created the first, special purpose, quantum computers in a scalable architecture that can begin to address real world problems. D-Wave's first generation quantum processors (now being explored in conjunction with Google/NASA as well as Lockheed and USC) are showing encouraging signs of being at a "tipping point" .. matching state of the art solvers for some benchmark problems (and sometimes exceeding them) ... portending the exciting possibility that in a few years D-Wave processors could exceed the capabilities of any existing classical computing systems for certain classes of important problems in the areas of machine learning and optimization. 

In this lecture, Eric Ladizinsky, Co-Founder and Chief Scientist at D-Wave will describe the basic ideas behind quantum computation , Dwave's unique approach, and the current status and future development of D-Wave's processors. Included will be answers to some frequently asked questions about the D-Wave processors, clarifying some common misconceptions about quantum mechanics, quantum computing, and D-Wave quantum computers.

Speaker Info

Eric Ladizinsky is a physicist, Co-founder, and Chief Scientist of D-Wave Systems. Prior to his involvement with D-Wave, Mr. Ladizinsky was a senior member of the technical staff at TRW's Superconducting Electronics Organization (SCEO) in which he contributed to building the world's most advanced Superconducting Integrated Circuit capability intended to enable superconducting supercomputers to extend Moore's Law beyond CMOS. In 2000, with the idea of creating a quantum computing mini -Manhattan-project like effort, he conceived, proposed, won and ran a multi-million dollar, multi-institutional DARPA program to develop a prototype quantum computer using (macroscopic quantum) superconducting circuits. Frustrated with the pace of that effort Mr. Ladizinsky, in 2004, teamed with D-Wave's original founder (Geordie Rose) to transform the then primarily IP based company to a technology development company modeled on his mini-Manhattan-project vision. He is also responsible for designing the superconducting (SC) IC process that underlies the D-Wave quantum processors ... and transferring that process to state of art semiconductor production facilities to create the most advanced SC IC process in the world.
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As part of the Quantum AI speaker series John Martinis from UC Santa Barbara gave a talk on the "Design of a Superconducting Quantum Computer".

Tech Talk: John Martinis, "Design of a Superconducting Quantum Computer"
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First Quantum Computing later            Space
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An important milestone has been reached: Experimental demonstration of quantum entanglement in the D-Wave 2 processor

Ever since D-­Wave released the first version of their processors, the question was raised to what degree quantum mechanical effects play a role in the functioning of the chips. A series of publications presented evidence that co-­tunneling is present (see for example Lanting et al 2010) and that quantum mechanical models describe the behavior of the chip more accurately than classical models (look for example at Boixo et al 2012 and Boixo et al 2013). However, it remained a challenge to experimentally demonstrate quantum coherence and entanglement. Measurements described in a new preprint are beginning to address this question: In this work, the eigenstates of a coupled eight qubit system are measured via qubit tunneling spectroscopy, and equilibrium entanglement is demonstrated through several different measures and witnesses. The eight qubit equilibrium entanglement observed in this work is robust and does not vanish after the decoherence time. Of course it is important to understand that the question of whether an annealing chip is quantum does not have a simple "yes" or "no" answer. It is rather a question of degree. Therefore investigations are now turning towards refined questions such as, "How large can the energy barrier be through which the system can still tunnel?" or "Over what physical scales is entanglement present in such processors"? and finally “Up to what point do quantum effects play a functional role when solving large Ising problems"? 

Lanting et al. 2010, arXiv:1006.0028 [cond-mat.supr-con]
Boixo et al 2012, arXiv:1212.1739 [quant-ph]
Boixo et al 2013, arXiv:1304.4595 [quant-ph]
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Are the rules of quantum physics written in stone, and if they are is it possible for those rules to contradict each other because if they do contradict each other than what do we know?
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qCraft: A Beginner’s Guide To Quantum Physics in Minecraft

Last week we shared qCraft, a mod that brings basic principles of quantum physics (superposition and entanglement) to Minecraft. Since some of you have asked, here’s a closer look at how those principles come to life within Minecraft along with a few quick examples of qCraft builds.

Find out more at

#qcraft  #minecraft  #quantum
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Magnifake you mean..?
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Have them in circles
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A new theory explains the seemingly irreversible arrow of time while yielding insights into entropy, quantum computers, black holes, and the past-future divide.
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I Asked IBM Watson team but they can't make themselves share the Info.
Good news, a gift with instructions, equality under all laws.
(our unlawful status quo is about to be repaired. We citizens will still need all of your expertise. Just no special powers over the rest of us. Its not fair

To: act
Subject: Dear Becky Bond. Did you sign either of my petitions? they are important, educational to sheep, and worth the risk to status quo operations, my sister. Sincerely, Elder Jonathan L. Peterson, trying to defend our constitution for all citizen members. RE:
Date: Thu, 12 Jun 2014 17:13:18 +0000
Dear Becky Bond.  Did you sign either of my petitions? They are important, educational to sheep, which we all have been just didn't know it for sure (for obvious reasons, mostly survival based),
and worth the risk to status quo operations, my sister.
Doing what is morally correct is the right thing to do, ALWAYS.
It is that simple, so God having mercy on his children gives this gift of knowledge to mankind.
Sincerely, Elder Jonathan L. Peterson,
Prophet of God, servant of our Lord Jesus Christ.
One day soon we as a people will work together to stop unlawful laws. This is just proof that Bribery is still against our rule of law, the Constitution.
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Hidetoshi Nishimori visited the Google  Quantum AI Lab on March 28, 2014 to give a talk on the: "Theory of Quantum Annealing"


Quantum annealing is a generic framework, metaheuristic, for combinatorial optimization. I will first review the basic formulation of quantum annealing and numerical evidence for its performance, particularly in comparison with classical simulated annealing. I will then explain a few theorems to guarantee its convergence toward the solution. The final part will be devoted to recent developments concerning the order of quantum phase transitions that may take place in the process of quantum annealing and may impede efficient computation.


Hidetoshi Nishimori is a professor of Physics at Tokyo Institute of Technology, Japan. His academic interests cover statistical physics of disordered systems and quantum physics and computation, quantum annealing in particular. He was awarded Nishina Memorial Prize, IBM Science Prize and is a fellow of the Institute of Physics. He received his PhD from the University of Tokyo. After three years in the United States as a postdoc at Carnegie-Mellon University and Rutgers University, he joined Tokyo Institute of Technology, where he now serves as the Dean of the School of Science.

Hidetoshi Nishimori: "Theory of Quantum Annealing"
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Hidetoshi, the ultimate geek - we need more of them!
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Seth Lloyd from MIT visited the Quantum AI Lab to give a tech talk on "Quantum Machine Learning."


Machine learning algorithms find patterns in big data sets. This talk presents quantum machine learning algorithms that give exponential speed-ups over their best existing classical counterparts. The algorithms work by mapping the data set into a quantum state (big quantum data) that contains the data in quantum superposition. Quantum coherence is then used to reveal patterns in the data. The quantum algorithms scale as the logarithm of the size of the database.

Speaker Info: 

Seth Lloyd is one of pioneers in the quantum information science with several seminal contributions to quantum computing, quantum communication, and quantum control. He developed the first quantum algorithms for efficient simulation of many-body systems at the quantum scale. He has also introduced the first realizable model for quantum computation and is working with a variety of groups to construct and operate quantum computers and quantum communication systems. Dr. Lloyd is the author of over a hundred and fifty scientific papers, and of `Programming the Universe,' (Knopf, 2004). He is currently professor of quantum-mechanical engineering at MIT.
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Great talk!
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Where do we stand on benchmarking the D-Wave 2?

Since there’s a fresh round of discussion on the performance of the D-Wave 2 processor, we thought it might be helpful to give an interim report on where we stand with benchmarking.

Let’s quickly review how the chip is programmed. See Figure 1 in the slideshow. You program the connection strengths between variables represented by the qubits to define what mathematicians call a quadratic optimization problem. Then you ask the machine to return the optimal solution.

Since there’s an astronomical number of different problem instances you could program the chip to solve, it’s impossible to check the performance on all of them -- you need to look at subsets. So what are good sets of instances to study? If you don’t really know where to start looking, you might as well just pick instances at random, measure relative performance, and see what happens.

But a more pragmatic approach is to study problems that arise in practical applications. At this stage we’re mostly interested in answering the question: Can we find a set of problems where the hardware outperforms the best known algorithms running on classical hardware? Since quantum optimization processors are still in rapid evolution, we’re less interested in the absolute runtimes; rather, we want to see how the scaling of the runtime increases as the number of variables increases. 

The hardware outperforms off-the-shelf solvers by a large margin

In an early test we dialed up random instances and pitted the machine against popular of-the-shelf solvers -- Tabu Search, Akmaxsat and CPLEX. At 509 qubits, the machine is about 35,500 times (!) faster than the best of these solvers. (You may have heard about a 3,600-fold speedup earlier, but that was on an older chip with only 439 qubits.[1] We got both numbers using the same protocol.[2])

While this is an interesting baseline, these competitors are general-purpose solvers. You can create much tougher classical competition by writing highly optimized code that accounts for the sparse connectivity structure of the current D-Wave chip. 

Two world-class teams have done that. One is a team at ETH Zurich led by Matthias Troyer, considered to be one of the world’s strongest computational physicists. With help from Nvidia, his team managed to write classical simulated annealing code running on GPUs that achieves an incredible 200 spin updates per nanosecond. The other tailor-made classical competitor was written by Alex Selby. You may recall he won £1 million for cracking the Eternity puzzle. 

Alex devised a smart large-neighborhood search that improves subsets of the 509-variable string while keeping the complement constant. The trick is to use only subsets that lie on tree structured graphs. These tree structured neighborhoods can be searched over in linear time using dynamic programming techniques. Because of the sparse connectivity, these neighborhoods can be very large -- up to 80% of all variables. This makes this solver very powerful.[3]

Both authors were kind enough to share the code with our team. In fact, Matthias’s postdoc Sergei Isakov wrote the fast annealing codes and is now a member of our group.

A portfolio of custom solvers designed to beat the hardware on its own turf is competitive

So what do we get if we pit the hardware against these solvers designed to compete with the D-Wave hardware on its own turf? The following pattern emerges: For each solver, there are problems for which the classical solver wins or at least achieves similar performance. But the inverse is also true. For each classical solver, there are problems for which the hardware does much better.

For example, if you use random problems as a benchmark, the fast simulated annealers take about the same time as the hardware. See Figure 2 in the slideshow.

But importantly, if you move to problems with structure, then the hardware does much better. See Figure 3. This example is intriguing from a physics perspective, since it suggests co-tunneling is helping the hardware figure out that the spins in each unit cell have to to be flipped as a block to see a lower energy state.  

But if we form a portfolio of the classical solvers and keep the best solution across all of them, then this portfolio is still competitive with the current version of the hardware. Again, a good example is the structured problem in Figure 3 in the slideshow. It slows down the annealers, but Alex Selby’s code has no problem with it and obtains the solution about as fast as the hardware does.[4] 

Sparse connectivity is a major limitation

A principal reason the portfolio solver is still competitive right now is actually rather mundane -- the qubits in the current chip are still only sparsely connected. As the connectivity in future versions of quantum annealing processors gets denser, approaches such as Alex Selby’s will be much less effective.

One indication that sparse connectivity is a culprit also comes from well-understood examples such as the “Hamming weight function with a barrier” problem -- quantum annealing tackles it easily but classical annealing fails.[5] But we haven’t been able to implement such examples as benchmark problems yet because of the sparse connectivity.

There’s a list of other hardware aspects still limiting performance that future iterations will need to improve -- reduced control errors, longer coherence times, error correction, richer non-stoquastic couplings between qubits, etc.

A big data approach may lead to new conclusions

So will we have to wait for the next generation chip with higher connectivity before we can hope to see the hardware outperform the portfolio solver? Until very recently we thought so. But remember that these latest benchmarking results were obtained from relatively small datasets -- just 1000 instances in the ones that got recent attention. 

It’s easy to make premature conclusions on such small sets, as there are not enough data points from possible subsets of problem instances that might indicate a speedup. Moreover, as several groups independently discovered, such random problems tend to be too easy and don’t challenge the quantum hardware or classical solvers.[6] 

Ever since the D-Wave 2 machine became operational at NASA Ames,  the head of our benchmarking efforts, Sergio Boixo, made sure we used every second of machine time to take data from running optimization problems. Simultaneously we gave the same problems to a portfolio of the best classical solvers we’re aware of. We now have data for 400,000 problem instances. This is the largest set collected to date, and it keeps growing. 

Eyeballing this treasure trove of data, we’re now trying to identify a class of problems for which the current quantum hardware might outperform all known classical solvers. But it will take us a bit of time to publish firm conclusions, because as Rønnow et al’s recent work shows, you have to carefully exclude a number of factors that can mask or fake a speedup.

So stay tuned!

1. Engineers from IBM, the maker of CPLEX reported that they tuned the CPLEX parameters to perform better on this task but the performance was still several hundred times slower than the hardware. See the paper here:

2.  McGeoch and Wang 2013:

3. For a detailed description of Alex Seby’s code see here:

4. You can also enhance the simulated classical annealers with so-called cluster updates. But if you customize the annealers to be faster for structured problems, they’ll be slower on the random instances.

5. The problem we refer to is discussed in section III B of a paper by Ben Reichardt ( It is a variant of a problem originally proposed by Edward Farhi.

6. Figure 5 in a reference by Katzgraber and Hamze ( illustrates why this is the case. Random problems tend to have a global minimum that can be reached without having to traverse high energy barriers.

#quantumcomputing   #quantum   #quantumphysics   #quantumcomputer  
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+Stephan Berry Quantum physics
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Experimental realization of a construction that illustrates how time can emerge for an internal observer of a quantum system while for an external observer it remains static, unchanging in time.
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Interesting when considering Schwarzschild Proton physics, more so in simulation and virtual space (computational astrophysics).  If everything is in a state of entanglement, it could be because the internal observer is inside the black hole/Singularity. Time is a construct of the holographic Singularity. External stasis is not eternal. Both systems are interchangeable - think Torus and cross-section. Experiencing reality is a shift of perspective.
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qCraft: Quantum Physics In Minecraft

We built the Quantum A.I. Lab to explore the potential of quantum computing, and figure out what questions we should be asking. One question is clear: Where will future quantum computer scientists come from? 

Our best guess: Minecraft. 

Millions of kids are spending a whole lot of hours in Minecraft, not just digging caves and fighting monsters, but building assembly lines, space shuttles, and programmable computers, all in the name of experimentation and discovery. 

So how do we get these smart, creative kids excited about quantum physics? 

We talked to our friends at MinecraftEdu and Caltech’s Institute for Quantum Information and Matter and came up with a fun idea: a Minecraft modpack called qCraft. It lets players experiment with quantum behaviors inside Minecraft’s world, with new blocks that exhibit quantum entanglement, superposition, and observer dependency. 

Of course, qCraft isn’t a perfect scientific simulation, but it’s a fun way for players to experience a few parts of quantum mechanics outside of thought experiments or dense textbook examples.

We don’t even know the full potential of what you can make with qCraft, but we’re excited to see what Minecraft’s players can discover. 

You can download qCraft now as part of the Tekkit, Hexxit, or Feed The Beast Unleashed modpacks, and find out more at 

#qcraft   #minecraft
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If I know the quantum computer's power, then why isn't the distant set up to unlimited to truly show it's power? This should be a piece of cake, since distant and the blocks that fill it are just.. ones, zeros and one-zero's.. Right?
Show us some true power benchmarks.
Btw, I hope you've enabled all of the D-Wave 2's memory in Java, otherwise you'll get some serious calculation errors...
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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.