Quantum safe cryptocurrency

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Although the software industry has eliminated GOTO from modern higher-level languages, software nevertheless continues to grow in complexity and fragility. Looking for how else such programming languages could be modified to avoid some common pitfalls, software designers can find inspiration, curiously enough, from their counterparts on the hardware side.

But programmers do this kind of sharing all the time in their software. Programmers write code that calls a function, expecting it to perform a single task. But many functions have side effects that change the shared global state, giving rise to unexpected consequences. Of course, hardware engineers can mess up, but not like you can with software, where just too many things are possible, for better or worse. Another complexity monster lurking in the software quagmire is called a null reference , meaning that a reference to a place in memory points to nothing at all.

If you try to use this reference, an error ensues. So programmers have to remember to check whether something is null before trying to read or change what it references. Nearly every popular language today has this flaw. The pioneering computer scientist Tony Hoare introduced null references in the ALGOL language back in , and it was later incorporated into numerous other languages. The architects of structured programming knew this to be true for GOTO statements and left developers no escape hatch.

History is proof that removing a dangerous feature can greatly improve the quality of code. Today, we have a slew of dangerous practices that compromise the robustness and maintainability of software. Nearly all modern programming languages have some form of null references, shared global state, and functions with side effects—things that are far worse than the GOTO ever was.

How can those flaws be eliminated? It turns out that the answer has been around for decades : purely functional programming languages. Of the top dozen functional-programming languages, Haskell is by far the most popular, judging by the number of GitHub repositories that use these languages. The first purely functional language to become popular, called Haskell , was created in So by the mids, the world of software development really had the solution to the vexing problems it still faces.

Indeed, software based on pure functions is particularly well suited to modern multicore CPUs. This allows the compiler to be optimized to produce code that runs on multiple cores efficiently and easily. As the name suggests, with purely functional programming, the developer can write only pure functions, which, by definition, cannot have side effects.

But what if a function needs to know or needs to manipulate the state of the system? In that case, the state is passed through a long chain of what are called composed functions—functions that pass their outputs to the inputs of the next function in the chain. Avoiding Null-Reference Surprises A comparison of Javascript and Purescript shows how the latter can help programmers avoid bugs. It addresses that problem by disallowing nulls. Instead, there is a construct usually called Maybe or Option in some languages.

A Maybe can be Nothing or Just some value. Working with Maybes forces developers to always consider both cases. They have no choice in the matter. They must handle the Nothing case every single time they encounter a Maybe. Doing so eliminates the many bugs that null references can spawn. Functional programming also requires that data be immutable, meaning that once you set a variable to some value, it is forever that value.

Variables are more like variables in math. So, the same value for x is used when computing x2 as is used when computing 2x. In most programming languages, there is no such restriction. You can compute x2 with one value, then change the value of x before computing 2x. By disallowing developers from changing mutating values, they can use the same reasoning they did in middle-school algebra class. Unlike most languages, functional programming languages are deeply rooted in mathematics.

Why is that? Most programming paradigms, such as object-oriented programming, have at most half a dozen decades of work behind them. They are crude and immature by comparison. In software, this kind of thing goes on all the time. Let me share an example of how programming is sloppy compared with mathematics. In math, this equation has zero solutions. It is a statement that commands the computer to take the value of x, add one to it, and put it back into a variable called x.

In functional programming, there are no statements, only expressions. Mathematical thinking that we learned in middle school can now be employed when writing code in a functional language. Thanks to functional purity, you can reason about code using algebraic substitution to help reduce code complexity in the same way you reduced the complexity of equations back in algebra class.

In non-functional languages imperative languages , there is no equivalent mechanism for reasoning about how the code works. The biggest problem with this hybrid approach is that it still allows developers to ignore the functional aspects of the language. Had we left GOTO as an option 50 years ago, we might still be struggling with spaghetti code today. You need to use languages that were designed with these principles from the start.

It comes at a cost. Learning to program according to this functional paradigm is almost like learning to program again from the beginning. So, as we were expecting, Quantum Resistant Ledger was built in mind with quantum security, and using a good candidate.

I personally do not think that we will get to a place where quantum computers can break RSA and ECC encryption in years. It would take qubits to break elliptic curve discrete logarithms, and for RSA encryption.

However, these are perfect, "logical" qubits. Because of error correction and other necessary processes, we need many more physical qubits to make one logical qubit. The quantum error correction overhead is very large. Some estimates say that 10 million physical qubits would be needed, even the lowest estimates say millions.

And different quantum hardware systems need different amounts of quantum error correction. Mosca who is at University of Waterloo, a top quantum school, and is deeply involved in quantum cryptography research and educational initiatives in this space.

The recommendations are from Under the assumption that the code provided in example. At least one issue weakens the security of the used signature scheme. In addition we identify a possibility for optimization, reducing the size of a transaction from 8, bytes to 2, bytes. So it uses the same scheme as QRL, which are, as far as we know, quantum secure. Beyond other updates, they stopped focusing on quantum proof cryptography.

IOTA's cryptography only allowed to use an address once. Reusing an address can be a point of vulnerability. That means a transaction long in the past could be used to recover the private key, and then that private key could be used again today to move coins. It is, however, not quantum safe. Ed is very vulnerable to quantum computers, in that quantum computers are exponentially faster at solving this, and even require less qubits than RSA to break.

However, IOTA believes that they can adopt new signature schemes quickly and are waiting for the recommendations for that, and upgrade once that viable scheme is found. So here, the point is, IOTA used to use quantum secure cryptography, but recently updated and is no longer using it. Cardano for example, did research a few years ago jointly with a think-tank to start exploring what it would mean for cryptocurrencies They have also continued publishing work on quantum security in the last few years, so they are definitely keeping an eye on the space, but they are not currently implementing quantum resistance into the cryptocurrency.

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Another complexity monster lurking in the software quagmire is called a null reference , meaning that a reference to a place in memory points to nothing at all. If you try to use this reference, an error ensues. So programmers have to remember to check whether something is null before trying to read or change what it references.

Nearly every popular language today has this flaw. The pioneering computer scientist Tony Hoare introduced null references in the ALGOL language back in , and it was later incorporated into numerous other languages.

The architects of structured programming knew this to be true for GOTO statements and left developers no escape hatch. History is proof that removing a dangerous feature can greatly improve the quality of code. Today, we have a slew of dangerous practices that compromise the robustness and maintainability of software.

Nearly all modern programming languages have some form of null references, shared global state, and functions with side effects—things that are far worse than the GOTO ever was. How can those flaws be eliminated? It turns out that the answer has been around for decades : purely functional programming languages. Of the top dozen functional-programming languages, Haskell is by far the most popular, judging by the number of GitHub repositories that use these languages. The first purely functional language to become popular, called Haskell , was created in So by the mids, the world of software development really had the solution to the vexing problems it still faces.

Indeed, software based on pure functions is particularly well suited to modern multicore CPUs. This allows the compiler to be optimized to produce code that runs on multiple cores efficiently and easily. As the name suggests, with purely functional programming, the developer can write only pure functions, which, by definition, cannot have side effects. But what if a function needs to know or needs to manipulate the state of the system? In that case, the state is passed through a long chain of what are called composed functions—functions that pass their outputs to the inputs of the next function in the chain.

Avoiding Null-Reference Surprises A comparison of Javascript and Purescript shows how the latter can help programmers avoid bugs. It addresses that problem by disallowing nulls. Instead, there is a construct usually called Maybe or Option in some languages. A Maybe can be Nothing or Just some value. Working with Maybes forces developers to always consider both cases. They have no choice in the matter. They must handle the Nothing case every single time they encounter a Maybe.

Doing so eliminates the many bugs that null references can spawn. Functional programming also requires that data be immutable, meaning that once you set a variable to some value, it is forever that value. Variables are more like variables in math.

So, the same value for x is used when computing x2 as is used when computing 2x. In most programming languages, there is no such restriction. You can compute x2 with one value, then change the value of x before computing 2x. By disallowing developers from changing mutating values, they can use the same reasoning they did in middle-school algebra class.

Unlike most languages, functional programming languages are deeply rooted in mathematics. Why is that? Most programming paradigms, such as object-oriented programming, have at most half a dozen decades of work behind them. They are crude and immature by comparison.

In software, this kind of thing goes on all the time. Let me share an example of how programming is sloppy compared with mathematics. In math, this equation has zero solutions. It is a statement that commands the computer to take the value of x, add one to it, and put it back into a variable called x.

In functional programming, there are no statements, only expressions. Mathematical thinking that we learned in middle school can now be employed when writing code in a functional language. Thanks to functional purity, you can reason about code using algebraic substitution to help reduce code complexity in the same way you reduced the complexity of equations back in algebra class. In non-functional languages imperative languages , there is no equivalent mechanism for reasoning about how the code works.

The biggest problem with this hybrid approach is that it still allows developers to ignore the functional aspects of the language. Had we left GOTO as an option 50 years ago, we might still be struggling with spaghetti code today. You need to use languages that were designed with these principles from the start. It comes at a cost. Learning to program according to this functional paradigm is almost like learning to program again from the beginning. More important, developers need to learn a new way of thinking.

At first this will be a burden, because they are not used to it. But with time, this new way of thinking becomes second nature and ends up reducing cognitive overhead compared with the old ways of thinking. The result is a massive gain in efficiency.

But making the transition to functional programming can be difficult. My own journey doing so a few years back is illustrative. So, as we were expecting, Quantum Resistant Ledger was built in mind with quantum security, and using a good candidate. I personally do not think that we will get to a place where quantum computers can break RSA and ECC encryption in years.

It would take qubits to break elliptic curve discrete logarithms, and for RSA encryption. However, these are perfect, "logical" qubits. Because of error correction and other necessary processes, we need many more physical qubits to make one logical qubit. The quantum error correction overhead is very large. Some estimates say that 10 million physical qubits would be needed, even the lowest estimates say millions.

And different quantum hardware systems need different amounts of quantum error correction. Mosca who is at University of Waterloo, a top quantum school, and is deeply involved in quantum cryptography research and educational initiatives in this space. The recommendations are from Under the assumption that the code provided in example.

At least one issue weakens the security of the used signature scheme. In addition we identify a possibility for optimization, reducing the size of a transaction from 8, bytes to 2, bytes. So it uses the same scheme as QRL, which are, as far as we know, quantum secure.

Beyond other updates, they stopped focusing on quantum proof cryptography. IOTA's cryptography only allowed to use an address once. Reusing an address can be a point of vulnerability. That means a transaction long in the past could be used to recover the private key, and then that private key could be used again today to move coins. It is, however, not quantum safe. Ed is very vulnerable to quantum computers, in that quantum computers are exponentially faster at solving this, and even require less qubits than RSA to break.

However, IOTA believes that they can adopt new signature schemes quickly and are waiting for the recommendations for that, and upgrade once that viable scheme is found. So here, the point is, IOTA used to use quantum secure cryptography, but recently updated and is no longer using it. Cardano for example, did research a few years ago jointly with a think-tank to start exploring what it would mean for cryptocurrencies They have also continued publishing work on quantum security in the last few years, so they are definitely keeping an eye on the space, but they are not currently implementing quantum resistance into the cryptocurrency.