Darwin2049/ChatGPT4/PhaseShift

Overview

Quantum Computing. Quantum computing is an abstruse topic area. Just the underpinning concepts can seem to be counterintuive and perplexing. Therefore what follows is intended to only be a cursory overview of some of the main features of this revolutionary area. Advanced mathematics is advised for those wishing to further understand the crucial elements of entanglement, coherence, vector spaces or quantum algorithms. What can be said currently however is that this new computational environment will make possible solution to currently intractable problems soluble within acceptable time frames. The ability to do so will invariably carry with it great promise but also great risk.

IBM, Google, D-Wave have published reports that offer explicit timelines and project goals for their respective quantum computing objectives.
What has already been made very clear is that even with the very limited quantum computing capabilities currently available, these systems prove themselves to be orders of magnitude faster in solving difficult problems than even the most powerful classical supercomputer ensembles.
If we take a short step into the near term future then we might be obliged to attempt to assimilate and rationalize developments happening on a daily basis. Any or even all of which can have transformative implications.

As quantum computing becomes more prevalent the field of deep learning will take another leap forward which will put those who possess it at an incalculable advantage. Should it become possible to train using large to extremely large data sets in minutes or even possibly in seconds then the reality of quantum computing of deep learning systems will take a revolutionary step forward. Combinations of knowledge could be incorporated almost instantly. This would change the entire complexion of how difficult to intractable problems become soluble.

The central problem in these most recent developments arises because of the well recognized human inability to process changes that happen in a nonlinear fashion. If change is introduced in a relatively linear fashion at a slow to moderate pace then most people adapt to and accommodate the change. However if change happens geometrically like what we see in the areas of deep learning then it is much more difficult to adapt to change.

• CG.X-Quantum will completely eclipse current incarnations of CG4: Quantum computing devices are already demonstrating their ability to solve problems in seconds or minutes that classical von Neuman computing machines require decades, centuries or even millennia to solve.

• Phase Shift: Gas, Liquid, Solid. Classical physics describes how states of matter possess different properties depending upon their energy state or environment. Thus on the surface of the earth we can experience the gas of the atmosphere. In environments that are somewhat above the freezing point of water we are unaware of the fact that we inhale and exhale atmospheric gas. It is odorless, colorless and tasteless.
  

Gas. Were we to collect sufficient quantities of atmospheric gas into a sealable container it would be possible to cool the gasses comprising earth's atmosphere into liquids. The idea of wetness, shape conformability and other properties of a liquid would suddenly become evident. Yet it would be difficult to adduce wetness if we never had experience with a liquids.
Liquid. If one never had contact with the liquid state of H2O then properties as buoyancy, wetness, conformability (i.e. to the shape of a container, evaporation, discoloration and similar properties might be very difficult to imagine. It would be difficult if not even impossible to envision something that was otherwise undetectable because it was odorless, colorless and tasteless could somehow cause one's death by drowning. Yet were one to be plunged into a large enough body of water but not possess swimming skills one could very well die by drowning.

Solid. The process can be repeated. If we were to use water as a basis then we might discover another state of matter that water can exhibit. This is the state of ice. Many of us use this material (ice) to condition our beverages. That a heretofore wet liquid could become solid might also defy our ability to imagine it taking on solid form. These all consist of the same substance, i.e. H2O. Yet properties found in one state, or phase bear little or no resemblance to those in the subsequent state. We should expect to see an evolution comparable happening in fast forward motion that is very comparable to "gas to liquid, liquid to solid". When DeepMind or CG4 are re-hosted in a quantum computing environment equally unimaginable capabilities will become the norm.

A crucial factor that conditions the utility of a device operating at the quantum level is noise. Any kind of noise from heat, vibration or cosmic rays can disrupt the extremely delicate processes at the quantum level. Therefore when numbers are presented they are often not well differentiated into qu-bits that can perform useful computations relative to those that do not. A strategy for dealing with this problem has been to use large numbers of qu-bits as an error correcting means. Therefore when a quantum device is said to consist of over a thousand qu-bits then in fact it might have to use 90% of them just for maintaining quantum coherence and entanglement. In order to get meaningful results these quantum states must be maintained for the duration of the calculation. But at these levels and using this means the result is that calculations happen at scales far beyond merely electronic or even photonic speed but due to quantum realities multiple evaluations can happen in parallel. The result has been the dramatic speed up numbers that have recently been reported in various research labs and corporations. Therefore a quantum computer that is claiming to have one hundred or more coherent qu-bit capability means that they can outperform classical computers by very wide margins.

The interested observer can find a number of useful references addressing the topics of quantum bits, entanglement and superposition

• The Quantum Leap
• QuBits and Scaling
• Some very useful basics

In some cases they will prove to be fairly technical.

Going forward existing computationally intensive tasks will migrate into the quantum computing world. When this happens there will be dramatic collapses in the amount of time to turn around formulation and analysis of complex to extremely complex problems to analyzing the available results.

Existing problems that involve multiple dimensions and numerous independent variables for their solution will become child's play for these new devices. Current reports indicate that minimizing noise and enhancing stability of the entanglement condition require continued refinement and improvement. As these advances continue the power of quantum computing will eventually approach then surpass even the most powerful classical computing systems.

References

• Basics Overview and background historical context.
• IBM Quantum System Two - Osprey - 432 Q-Bit Device.
• Some Overview.
• Hadamard Gates.
• Bloch Sphere.
• Watch
• Bloch Sphere Coordinate Mapping
• Simplified
• {Zero:One} Bounded Solutions.
  
• Vector Space.Quantum computing uses vector spaces to represent potential computational outcomes; an understanding Qu-bit values requires an understanding of values are represented at a quantum level.
  
• P/NP Complete Problems.
  
• Entanglement.
• Coherence.
• Crucial recent improvements (1000 Q-Bits).
• Superposition.
• Quantum Logic: AND OR NOR NAND XOR.
  
• Error Correcting.
  
• Quantum Problem Space.
  
• Lots More
• Algorithms.
• Shor’s Algorithm. Is a powerful tool for factoring extremely large prime numbers. Quantum computers can collapse the time and effort required to factor large prime numbers. With the ability to factor large prime numbers it becomes possible to crack RSA encryption.
  
• Explained Simply.
• Using simple mathematics.
• Shor’s Algorithm – illustrated.
• Step by step.
• Grover’s Algorithm. This is an algorithm developed in 1996 by Lov Grover. It leverages quantum computing to collapse the amount of time needed to perform a search of N unordered elements. Note that a crucial factor is that in performing the search for a specific element that meets a set of desired criteria, any evaluation of an element in the set yields no information about which other element in the set of N elements might help determine the desired one.
• Somewhat abstruse video of how Grover’s Algorithm reduces search cost from N to N1/2.
• Deutsch’s Algorithm.
• Uses
• Encryption.
  
• Drug Design. Designing novel molecular substances with extremely specific structures will become possible in unexpectedly short times.
  
• Process Optimization
  The Traveling Salesman problem is one that a quantum system can solve instantly. Extending this approach to optimizing new complex processes means that the most efficient process organizations will become instantly accessible.
• AI.Deep Learning systems will be profoundly impacted by quantum computing. Learning times will collapse.
  
• DevLeliw
• Qiskit (IBM). Basic Idea
• "Sand box:" Quantum Computing practice environments.
• Useful Insights.
• Five very important quantum algorithms