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I do not think India will do quite as well as the Standard Chartered projection.

South Korea could also re-unify with North Korea by 2030.

Projected 2030 economies can easily vary by plus 20% or minus 50% depending upon some country having a lot more problems or very good luck and policies. There is less margin of error for the developed countries like the US, Japan,Prada Handbag Online, and Europe

Even with those margin of error levels.
China's economy should be the world's largest by a comfortable margin and possibly twice as big as the USA or India. (50-90 trillion)
India and the USA and the EU-27 should be in the range of the same size in the next tier. (around 20-40 trillion)
Japan,http://www.pradahandbagoutletonline.com, Germany, UK, France, Indonesia,Prada Outlet, Brazil, Russia, South Korea and Canada will be in the third level. (around 5-10 trillion)

After removing inflation,Prada Handbag Outlet, China would have about $35 trillion in today's dollars and the US would have about 20% more economy than today at $18 trillion.

By 2030, the balance of economic power will have shifted from the West to the East: while the US, the EU and Japan represented 72% of the global economy in 2000, their share should shrink to only 29% by 2030 – a complete reversal of their importance relative to the emerging world.

By 2030 we assume the CNY will have appreciated from 6.64 this year to 4.39, and the INR from 45.5 this year to 35 in 2030.

We see the US economy growing 2.6% in 2010, only 1.9% in 2011 and 2.7% in 2012, then growing at a stronger pace, with trend growth of 2.5% over the horizon to 2030. Likewise, we see the EU-27 growing 1.8% in 2010, 1.7% in 2011 and 2.2% in 2012, reflecting a sluggish recovery from the crisis, with large output gaps and stubborn unemployment. Over the forecast horizon to 2030, the EU-27 is expected to grow at its trend growth rate of 2.5%. For Japan, the growth figures are 1.5% (2010), 1.0% (2011) and just under 2.0% (2012), and 1.0% over the forecast horizon to 2030.

China’s growth rate is forecast at 10.0% (2010), 8.5% (2011) and 8.0% (2012), averaging 6.9% over the two decades to 2030. For India, the growth figures are higher, at 8.1% (2010), 8.5% (2011) and 8.8% (2012), averaging 9.3% over the forecast horizon. When one considers these figures, it is clear we have assumed a very fragile recovery from this crisis in the West, and trend growth rates that are not exaggerated for emerging economies, using China and India as examples. For instance, there are credible arguments that India may even achieve a trend growth rate nearer 12% or so, with certain conditions in place.

IMF projection to 2017


Population Projects



Canada will have 35 million people in 2013 and could have 40 million in 2023 and 50 million in 2042. The low growth scenario is to have 40 million in 2042 and the mid-growth scenario is to have 45 million in 2040.

Canada could catch up to the population of Spain in 2042 and Germany in 2060. Germany population is projected to fall by quite a bit.











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The researchers ran instances of a lattice protein folding model, known as the Miyazawa-Jernigan model, on a D-Wave One quantum computer.

The research used 81 qubits and got the correct answer 13 times out of 10,000. However these kinds of problems usually have simple verification to determine the quality of the answer. So it cut down the search space from a huge number to 10,Prada Outlet,000. Dwave has been working on a 512 qubit chip for the last 10 months. The adiabatic chip does not have predetermined speed up amounts based on more qubits and depends upon what is being solved but in general the larger number of qubits will translate into better speed and larger problems that can be solved. I interviewed the CTO of Dwave Systems (Geordie Rose back in Dec, 2011). Usually the system is not yet faster than regular supercomputers (and often not faster than a desktop computer) for the 128 qubit chip but could be for some problems with the 512 qubit chip and should definitely be faster for many problems with an anticipated 2048 qubit chip. However, the Dwave system can run other kinds of algorithms and solutions which can do things that regular computers cannot. The system was used by Google to train image recognition systems to remove outliers in an automated way.

ABSTRACT - We present the first quantum-mechanical implementation of lattice protein models using a programmable quantum device. We were able to encode and to solve the global minima solution for a small tetrapeptide and hexapeptide chain under several experimental schemes involving 5 and 8 qubits for the four-amino-acid sequence (Hydrophobic-Polar model) and 5, 27,Prada Handbag Online, 28, and 81 qubits experiments for the six-amino-acid sequence under the Miyazawa-Jernigan model for general pairwise interactions. For the experiment with 8 qubits,Prada Handbag Outlet, we simulated the dynamics of the quantum device with a Redfield equation with no adjustable parameters, obtaining excellent agreement with experiment. Since the quantum annealing algorithm not only finds the ground state but also the low-lying excited states,http://www.pradahandbagoutletonline.com, it provides information about the relevant minimum energy compact structures of protein sequences and it is useful to evaluate designability and stability such as that found in natural protein sequences, where the global minimum of free energy is well separated in energy from other misfolded states. The approach employed here can be extended to treat other problems in biophysics and statistical mechanics such as molecular recognition, protein design, and sequence alignment.

512 qubits used for protein folding solutions would be a lot faster and should be able to solve far larger problems. Dwave should also be developing 2048 qubit chips in the next few years.

"The D-Wave computer found the ground-state conformation of six-amino acid lattice protein models. This is the first time a quantum device has been used to tackle optimization problems related to the natural sciences," said Professor Aln Aspuru-Guzik from the Department of Chemistry and Chemical Biology at Harvard University.

Proteins contribute to virtually every process that occurs within a cell. The shape of a protein is closely related to its function. Understanding the shape of a protein helps researchers understand how it behaves, accelerating advances in many different areas of life sciences, including drug and vaccine design.

A cornerstone of computational biophysics, lattice protein folding models provide useful insight into the energy landscapes of real proteins. Understanding these landscapes, and how real proteins fold into the shapes that help give them their function, is an extremely difficult problem for today's computers to solve.

Dr. Alejandro Perdomo-Ortiz, the lead author of the paper, stated that: "Knowing that we can use real quantum computers to solve hard problems in biology is an exciting and important result. The techniques developed in this report can also be used to tackle other biophysical problems such as molecular recognition, protein design, and sequence alignment."