Read an article the other day in MIT’s Technical Review, Google’s new chip is a stepping stone to quantum computing… about Google’s latest endeavor to create quantum computers. Although, digital logic or classical electronic computation has been around since mid last century, quantum logic does things differently and there are many problems that are easier to compute with quantum computing that take much longer to solve with digital computing.
Qubits are weird
Classical or digital electronic computation follows the more physical mechanistic view of the world (for the most part) and quantum computing follows the quantum mechanical view of the world. Quantum computing uses quantum bits or Qubits and the device that Google demonstrated has a 2X3 matrix of qubits, 6 in total.
Unlike a bit, which (theoretically)is a two state system that can only take on the values of 0 and 1, a qubit is a two level system but it can take on an infinitely many number of different states in reality. In practice, with a qubit, there are always two states that are distinguishable from one another but they can be any two states of the infinitely many states they can take on.
Also, reading out the state value of a qubit can be a probabilistic endeavor and can impact the “value” of the qubit that is read out afterwards.
There’s more to quantum computing and I am certainly no expert. So if your interested, I suggest starting with this Arxiv article.
Faster quantum algorithms
In any case some difficult and time consuming arenas of classical computation seem to be easier and faster with quantum computation. For example,
- Factoring large numbers – in classical computation this process takes an amount of time that is exponential to the number of bits in the “large number”, where “B” is number of bits and “E” epsilon is a constant >0, the best current algorithms take O([1+E]**B) time. But Shor’s quantum factorization algorithm takes only O(B**3) time, which is considerably faster for large numbers. This is important because RSA cryptography and most key exchange algorithms in use today, base their security on the difficulty of factoring large numbers. (See Wikipedia article on Integer Factorization for more information.
- Searching an unstructured list – in classical computation for a list of N items, it takes on the O(N). But Grover’s quantum search algorithm only takes O(sort[N]) which is considerably faster for large lists. (See Arxiv paper for more information.)
Using the Shor factorization algorithm, they were able to factor the number 15 with 7 qubits.
There are many quantum algorithms available today (see the Quantum Algorithm Zoo at NIST) with more showing up all the time. Suffice it to say that quantum computing will be a more time efficient and thus, more effective approach to certain problems than classical computing.
Quantum computers starting to scale
Now back to the chip. According to the article the new Googl chip implements a 2X3 matrix of qubits.
For those old enough to remember, this was called an Octal or 3-bit number, ranging from 0 to 7, and two octals can range from 0..64. Octals were used for a long time to represent digital information for some (mostly mini-computers) computers. This is in contrast to most computing nowadays ,which uses Hexadecimal numbers or 4-bit numbers ranging from 0..15, and with two hexadecimal numbers ranging from 0..255.
Why are octals important? Well if quantum computing can scale up multiple octal numbers, then they can start representing really large numbers. According to the article Google chose 2X3 qubit structure because it’s more easy to scale.
I assume all the piping surrounding the chip package in the above photo are cooling ports. It seems that quantum computing only works at very cold temperatures. And if this is a two octals computer, scaling these up to multiple octals is going to take lots of space.
How quickly will it scale?
For some history, Intel introduced their 4004 (4-bit) computing chip in 1971 (Wikipedia), their 8-bit Intel 8008 in 1972 (Wikipedia), their 16-bit Intel 8086 between 1976-78. So in 7 years we went from a 4-bit computer to a 16 bit computer whose (x86) architecture continues on today and rules the world.
Now the Intel 4004 had 16 4-bit registers, had a data/instruction bus that could address 4096 4-bit words, 3-level subroutine stack and was a full fledged 4 bit computer. It’s unclear what’s in Google’s chip. But if we consider that this 2×3-qubit computer, which has multiple 2×3 qubit registers, a qubit storage bus, multi-level qubit subroutine (register) stack, etc. Then we are well on our way to quantum computing being added to the worlds computational capabilities in less than 10 years.
And of course, Googles not the only large organization working on quantum computing.
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So there you have it, Google and others are in the process of making your cryptography obsolete, rapidly speeding up unstructured searching and doing multiple other computations lots faster than today.
Photo Credit(s): from the MIT Technical Review article.
At 
Elastifile’s architecture supports accessor, owner and data nodes. But these can all be colocated on the same server or segregated across different servers.
Metadata operations are persisted via journaled transactions and which are distributed across the cluster. For instance the journal entries for a mapping data object updates are written to the same file data object (OID) as the actual file data, the 4KB compressed data object.
There’s plenty of discussion on how they manage consistency for their metadata across cluster nodes. Elastifile invented and use Bizur, a key-value consensus based DB. Their chief architect Ezra Hoch (
I must do 
Using one AI tool to annotate data to train another AI tool is sort of bootstrapping AI technology. It’s acute trick but may have only limited application. I could only think of only a few more applications of similar technology:
Nonetheless, I for one am happy that the next reCAPTCHA won’t be having me identify river sounds in a matrix of 9 sound clips.