January 11, 2019
The Law of Future Uncertainty and the Law of Increasing Spin Density
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“Google’s New Chip Is a Stepping Stone to Quantum Computing Supremacy,” Technology Review; (2017)
The end of an era and spin up of a new and quantum computing supremacy
The end of an era and spin up of a new
Goodbye and so long electrons. Thanks for all the help. Please meet your evolutionary progeny the ion and photon.
We are apes stumbling around into the woods of cyber. Binary computing fell into humanity’s lap and ignited the world of cyber. It has since become a significant economic catalyst for both good and evil. We are mere children having only gone from tubes to chips (Simonite, 2017) in an evolutionary eye blink from about 1940 - 2018. We used deterministic gates in our first generation of hardware and engineering systems approach; we left probabilistic hardware models untouched during the first portion of our deployment practices -- we did know in ca. 2011 and have proved in Silicon that quantum tunneling of electrons (Tucker, 1994) does in fact happen and we allowed for this but did not leverage this phenomenon for computational services. We stumbled onto binary 1|0 during a time of war and built it over a long period of relative peace and economic stability. In terms of human lives, we paid a dearly for this evolutionary path. It is no coincidence that the realm of quantum mechanics evolved about the same time basic cyber engineering. We have come to realize the smallest things in the universe are paradoxically the most powerful. Power comes not from the big things but also from the smallest things. Think about the Atom and the bit.
This forest floor is littered with technological dead ends like gear based encryption (Sipser, 2006), vacuum tubes; i.e., valves (Vonnouman, 1945) and non-integrated transistors as well as fell out of favour like Word Perfect. One basic premise has dominated this realm since the 1940’s and it too is about to hit its technological dead end. I predict the end of reliance on the classical deterministic gate architecture (Chou, 2018). The binary gate (Zaghloul, 2006) will litter the floor of history as a notable adjacent technology while us apes stumble around in this evolutionary path and move onto non-deterministic models that more accurately model the way the universe fundamentally acts at a quantum level and we slowly dispose of our heat wasting, energy intensive, computer infrastructure models. It is interesting to know that silicon and vacuum valve representations 0|1 are direct adjacent evolutionary lines.
We have already noted a move away from static algorithmic models in software. As we discover the basic concepts of polynomial ladders (Farrell, 1982) and applications of such code as bloom filters (Broder, 2004). We are seeing that deterministic algorithms like binary search filters are highly ineffective. The lesson we have learned is that probabilistic computational models (Levchuck, 2007) are much more effective and efficient. They streamline many of the advanced computational workloads being performed in such areas as voice recognition, image recognition, and autonomous operations (like self-driving cars and drones).
Like paper cards, the adoption of deterministic gates was not a result of an endeavour to create the best way to do cyber. It was an accident traceable back to morse code and the telegraph and valves and gears. It was best available at the time and created engineering momentum.
This also led us to some very cool shortcuts like elliptical curve encryption (Stolbikova, 2016) that allowed us to represent a large quantity of sequential numbers with high efficiency, no one argues that is not a value to us today. Had we moved directly to non-deterministic computing we would have had no need for this as we are will no longer be required to perform high volumes of arithmetical number operations sequentially. Current convergence based probabilistic models (Bandeira, 2013) allow us to perform huge volumes of computations concurrently at the hardware level.
We stand at a fork in the evolutionary road. Cyber engineering can vear either way or both at the same time. One way is to the realm of classical gates done with photonic quanta (Hacker, 2016) and inefficient binary sequential operations as a move away from massive electron gates. The second way is to use smaller quanta probabilistic signalling. I predict we will take both paths.
There is no mechanism better suited for quantum signalling speeds on the I/O (Combes, 2016) layer than photonic gates. However, they will be relegated to the rx/tx model and only provide off-board pinpoint structured computational sequences and highly structured code. I don’t expect to see a lot of FORTRAN libraries running on quantum hardware compilers and SDK’s. We will always have a need to supplement the strength of probabilistic computing with deterministic methods. A new generation of high speed optical photonic processors will arise. We need deterministic algorithms for applications like systems control applications (Buttazzo, 2011) where the method is unambiguous. For example, you don’t want your implanted defibrillator sending random amounts of amperage to your heart at the signs a cardiac event. In certain use cases, we want highly constrained outcomes implemented
We already know that the universe of hardware favours qbits over bits as our entire cosmos and Euclidean/Newtonian physics classical models (Green, 2000) rely upon it for the layering of physical properties (Kellert, 1993) that are deterministic (Maier, 2015). This gives life the stability needed to evolve. But it's unseen foundation is in fact uncertainty. Could Nobel have predicted the power of atomic fission or fusion with his classical models? Unlikely. Nor can we continue to base our computational foundation on deterministic hardware.
We will see an exponential growth of qbit operational cores in data centres around the world to support advanced algorithmic interfaces into the physical world -- just like we saw in the early years of transistor density growth (Elkman, 2004). We can expect the same magnitude of growth curves for quantum spin density AKA More’s law [2016 Sinomite] The commercial incentives for the power provided by the Qubit platform is clear. This is not unlike Neurons in the brain. Once we scale past 20B qubits we will start seeing some equivalence to biological compute resources at scale (Knill, 2001). Circa 20B is likely a lower limit as it mirrors the accidental natural number of Neurons the human brain supports (Herculano-Houzel, 2009). Once we reach 20B we will see a doubling of qbtis at a rate of 20B per year until the number of total qbits outnumbers the entire inventory of Neurons in the brain of all species on the planet. This will become a non-Turing based growth factor somewhat analogous to Moore’s law for qbits (Mukerjee, 2017); we will no longer enumerate gates we will measure the qubit spin density—spin-density will be measured on nanometer scales (VanderSar, 2015). Since each ion or photon will spin at its own rate with time being proximate for each one the primary density constraint will be our ability to read the spin signals in a low noise fashion (Schlipf, 2017). Detection will be improved over time. Accidental entanglement will become an issue for our noise reduction implementations (Su, 2017).
Usage of the electron-based binary gate will fade to the same position as paper cards analog media like types such as paper tape [RFC645 - 1974] are today. They work but are not a great idea given other options available. They will continue to be useful as sensors and pre-core peripherals (Bojinov, 2011). And high temp applications or where topological superconductors (Lei, 2015) are not available.
This means we will start seeing advanced autonomous applications [2015: Lovatis] become mature and handle vast quantities of data. We will also see that conventionally encrypted secrets [FIPS 186-4 ] are easily decoded without forward knowledge of the keys. I speculate it stands to reason that it is more likely than not that British, US, Russian and Chinese intelligence services are already doing massively parallel decryption using qbits (Cramer, 2002). Our current key exchange protocols have become transparent This would follow the well-worn path of the first adoption by signals intel just like Turing in WWII.
We will see that search results will reach near precognitive levels for the purpose of mining vast amounts of data. Big data modelling and supercomputing applications like molecular biology modelling and protein simulations will be performed quickly and cheaply. New applications like RNA epigenetic prediction [2015: HOLOCH] will arise.
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