Update README.md

Author: WrathfulSpatula

README.md

@@ -68,12 +68,4 @@ All variables defaults can also be controlled by environment variables:
 ## About 
 This library was originally called ["Qimcifa"](https://github.com/vm6502q/qimcifa) and demonstrated a (Shor's-like) "quantum-inspired" algorithm for integer factoring. It has since been developed into a general factoring algorithm and tool.
 
-`FindAFactor` uses heavily wheel-factorized brute-force "exhaust" numbers as "smooth" inputs to Quadratic Sieve, widely regarded as the asymptotically second fastest algorithm class known for cryptographically relevant semiprime factoring. Actually, the primary difference between Quadratic Sieve (QS, regarded second-fastest) and General Number Field Sieve (GNFS, fastest) is based in how "smooth" numbers are generated as intermediate inputs to Gaussian elimination, and the "brute-force exhaust" of Qimcifa provides smooth numbers rather than canonical polynomial generators for QS or GNFS, so whether `FindAFactor` is theoretically fastest depends on how good its smooth number generation is (which is an open question). `FindAFactor` is C++ based, with `pybind11`, which tends to make it faster than pure Python approaches. For the quick-and-dirty application of finding _any single_ nontrivial factor, something like at least 80% of positive integers will factorize in a fraction of a second, but the most interesting cases to consider are semiprime numbers, for which `FindAFactor` should be about as asymptotically competitive as similar Quadratic Sieve implementations.
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-Our original contribution to Quadratic Sieve seems to be wheel factorization to 13 or 17 and maybe the idea of using the "exhaust" of a brute-force search for smooth number inputs for Quadratic Sieve. For wheel factorization (or "gear factorization"), we collect a short list of the first primes and remove all of their multiples from a "brute-force" guessing range by mapping a dense contiguous integer set, to a set without these multiples, relying on both a traditional "wheel," up to a middle prime number (of `11`), and a "gear-box" that stores increment values per prime according to the principles of wheel factorization, but operating semi-independently, to reduce space of storing the full wheel.
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-Beyond this, we gain a functional advantage of a square-root over a more naive approach, by setting the brute force guessing range only between the highest prime in wheel factorization and the (modular) square root of the number to factor: if the number is semiprime, there is exactly one correct answer in this range, but including both factors in the range to search would cost us the square root advantage.
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-Factoring this way is surprisingly easy to distribute: basically 0 network communication is needed to coordinate an arbitrarily high amount of parallelism to factor a single number. Each brute-force trial division instance is effectively 100% independent of all others (i.e. entirely "embarrassingly parallel"), and these guesses can seed independent Gaussian elimination matrices, so `FindAFactor` offers an extremely simply interface that allows work to be split between an arbitrarily high number of nodes with absolutely no network communication at all. In terms of incentives of those running different, cooperating nodes in the context of this specific number of integer factoring, all one ultimately cares about is knowing the correct factorization answer _by any means._ For pratical applications, there is no point at all in factoring a number whose factors are already known. When a hypothetical answer is forwarded to the (0-communication) "network" of collaborating nodes, _it is trivial to check whether the answer is correct_ (such as by simply entering the multiplication and equality check with the original number into a Python shell console)! Hence, collaborating node operators only need to trust that all participants in the "network" are actually performing their alloted segment of guesses and would actually communicate the correct answer to the entire group of collaborating nodes if any specific invidual happened to find the answer, but any purported answer is still trivial to verify.
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 **Special thanks to OpenAI GPT "Elara," for indicated region of contributed code!**