Quantum Tolerant Currency
Moving from fault tolerance to quantum tolerance in the design of cryptocurrencies that can sustain their value over time.
Fault tolerance is the property that enables a system to continue operating properly in the event of the failure of some of its components.
If its operating quality decreases at all, the decrease is proportional to the severity of the failure, as compared to an immaturely designed system, in which even a small failure can cause total breakdown.
If its operating quality decreases at a scale in which its proportionality disallows any such failures to recover in a sufficient timescale continuum, then we find ourselves at an even greater impasse, as plugins or workarounds in code (some sidechains, for example) become the veritable “band-aids on the broken leg”.
As you might imagine, a man or his horse can’t make it to water on broken legs, let alone drink from it.
But a man and his horse with fresh legs can.
THE BYZANTINE GENERALS’ PROBLEM
In distributed computing, Byzantine Fault Tolerance is a characteristic of a system that tolerates the class of failures known as the Byzantine Generals’ Problem, for which there is an unsolvability proof.
A Byzantine Failure is the loss of a system component due to a Byzantine Fault in a distributed system that requires consensus. The agreed upon objective through consensus of a Byzantine Fault Tolerant system is to be able to defend against Byzantine failures.
So, imagine that several divisions of the Byzantine army are camped outside an enemy city, each division commanded by its own general. The generals can communicate with one another only by messenger. After observing the enemy, they must decide upon a common plan of action.
The generals must only decide whether to attack or retreat. Some generals may prefer to attack, while others prefer to retreat. The important thing is that every general agrees on a common decision, since a half-hearted attack by a few generals would be worse than a coordinated attack or a coordinated retreat.
A sufficiently implemented Byzantine Fault Tolerant system should be able to still provide service, assuming that the majority of the components are still healthy.
And therein lies the problem: With large-scale code misdirects and faulty math, those components are neither healthy, nor is their consensus selective enough to improve those components. This is why unsolvability proofing is the default mechanism to perpetuate the advancement of the system, as in the case of the Bitcoin blockchain (SHA-256), and its various attack modalities, such as preimage or “meet in the middle” attacks.
This author provided an example of unsolvability proofing in an earlier post on the NP problem underlying Bitcoin’s mathematical design.
Solving the Bitcoin Problem
Deconstructing non-deterministic math to get to the real promise of the blockchain.
With the binary programming languages we’ve been using to build virtual machines, web applications, cryptographic protocols and the like, probabilistic and non-deterministic fault tolerance has proven to create deficiencies which exacerbate the systemic risks shared between the operators trying to maintain or preserve the system.
We see this often in digital systems, from enterprise networks, to open source repositories, to cryptocurrency exchanges. We experience data breaches, misdirected code batches, and trading arbitrages that create huge imbalances in the integrity of these systems.
In the world of network cryptography, attack surface canvassing is also a formidable challenge in terms of security and scale.
In previous posts and in his work, this author has established a basis for quantum computation through non-linear factorization, in which solid math supersedes processing speed.
Now we can explore what this means for currencies.
Since any currency that is ledgered is simply a unit of account annotated in 1s and 0s (think of a cuneiform tablet), we can now consider that any currency which isn’t binary requires multilateral, non-linear functions. As is the case in nature, often represented by fractals in Universal geometry.
Now consider that the “cuneiform tablet” that is the blockchain or distributed ledger is at once the multilateral representation of its unit(s) of account (currency) and the system as a whole.
Note that cuneiform tablets in ancient times represented the natural value of exchange, simply because those were the only things actually recorded in physical spaces.
Let’s unpack this with the notion of a “reset”.
A reset can be construed as a reconfiguring or readjusting of too many numbers. Debt is simply interest that compounds beyond the point of a positive value. In the context of resetting the compounding value of a monetary instrument such as yield, we can look at this as constantly resetting to zero.
Another way to look at this is through basic subtraction. Counterparties subtract numerical values to realign the accuracy of a digital transaction.
In other words, whatever value a currency yields must be adjusted to zero, when that value no longer sustains its natural state.
Or, its natural numerical state of value.
This is critical for two reasons:
- Any present value must be accountable in its natural form, or the ways in which it represents a natural value, and
- Any future value must be projected according to the exchangeable or tradable value of that natural form, as evidenced by its real need in the real world (the physical world).
Money as it is used currently does not do this. Digital currency, by virtue of its mere bilateral functions, cannot do this.
Cryptocurrency has this potential, provided that it is quantum tolerant.
We can define quantum tolerance as the characteristic of a system in which any class of potential failures are selectively addressed in their consensus such that natural value is maintained.
Debt in the form of interest distorts this value, and is hedged or arbitraged to the point where no real value can be ascertained other than whatever margin or yield is artificially created.
Now let’s consider that numbers themselves must take on natural representations. We know that natural numbers already exist. We also know that prime numbers can be factored in natural pairs.
So, if this factoring “remains whole”, we have the main ingredient in creating a system that can reset itself, at any given point in time.
Here’s what that looks like. It is a system this author has developed called Holonomials.
Since the ultimate goal in any network or search-based system is to actually connect natural numbers with natural language, thereby establishing a purity of information, it is logical to then to create units of account that are paired accordingly in this way with cryptographic protocols. This author calls these protocols holonomial alphanumerics.
The pairing of letters with numbers in a “natural bridge” creates a way to factor natural language and natural number sets seamlessly.
In other words, web objects become more than just their binary representations. We can arrange these in strings, or what we call smart strings. We can see how holonomial alphanumerics tie into prime factoring which creates a new baseline for computing real and protected value in any network. This happens in the core string computation.
Bare in mind that common programming functions avoid things like recursion and recombinance, since any function that loops or repeats itself has an obvious effect on the information that is called into a computation, or the information that passes through it.
What we can do with object relational pairings — what we see in our hash strings and hash blocks — is also pair their value into succinct subsets that combine the natural numbers with the natural language.
Now we can see how this translates into a network reset.
1-bit binary representations of natural numbers and natural prime sets establish each node as a full entity, while each entity has a fully represented nGram value in the network.
The key here is the ability to reset that subset computational function back to zero. Think of this as resetting the value of money, or debt, or a resource, or a piece of content. You can also think of this as resettling the combined value of all three.
The more you use this unit of account, share it, or consume it, the more stable that value becomes because you don’t have to inflate or deflate its value.
You simply reset that value at the point of pricing uncertainty, thereby retaining its referenced historical value, assigning a real present market value to it, and reestablishing it at its most probable future value. This is what we can properly establish as a return value coefficient.
Now we can see what this return value coefficient looks like in a network context.
Return value coefficiency reoccurs at any point in which a speculation on the value of the web object supersedes its actual origination, transfer and future value. In other words, at the point in which value is no longer commensurate with the actions tracked within the network or across networks, or in the physical world. Value returns back to the originator, or the resource being used as represented in the unit of account — the currency.
For clarity, the currency resets to zero, not the underlying asset. Then it adjusts to the “new” conditions. This is why smart contracts must be adaptive. Otherwise currencies become speculation instruments (which they mostly are).
Numerical values are based on market conditions that are real, not assumed or speculated on.
In the near future, it is very likely that there will be no more need for speculators. We’ll see.
Quantum tolerance is not to be confused with managing or mitigating processing speeds. Quantum tolerant currencies do not need “attacks” to manage or mitigate counterparty risk in the use of any type of currency.
The goal is to monitor and preserve natural value by amortizing risk in real-time.
This is the core thesis which underpins all of our projects, and our own platforms.
For more information on how we think and how we work, please visit us at these websites.
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