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Design considerations


Refer to the conceptual documentation if you're not familiar with:



Circuits are programmable but can't (practically and efficiently) prove any algorithm. The way constraints are represented make some things more natural ("snark-friendly") to do than others.

Numbers used in constraints are not integers or floats, but finite field elements (big numbers modulo a prime pp).

So when writing a = b * c, not only don't you have the liberty to specify types for these variables (for example float or int), but you must also consider field overflow.

Some cryptographic constructs, like MiMC hash or EdDSA signature scheme were designed to work on those field elements, and are well suited to be used with zk-SNARKs. Which is why zk-SNARKs are mostly used to verify hashes, signatures or other "snark friendly" cryptographic primitives.

Examples of programming concepts used in a traditional programming language, but are un-natural in most zk-SNARK constructs are:

  • Using floating numbers
  • Using conditional statements (if and else)
  • Managing memory.

Like other projects in the zk-SNARK or blockchain space, we're actively researching ways to make zk-SNARKs more programmable. For example through using of proof recursion or zk-virtual machines.


The Proving schemes and curves section provides more insight into Prover and Verifier performance, across scheme and curve choices.

In a Circuit you want to minimize the number of constraints. The frontend package does a lot of work behind the scenes . For example, it performs lazy evaluations of linear expressions to minimize the number of constraints. That part is transparent for the circuit developer.

Division (a = b / c)

Performing a field division outside a Circuit is something algorithms tend to avoid because it is costly.

However, doing that in a Circuit is cheap. Writing a = b / c will be encoded into a constraint assert(c * a == b) (one multiplicative constraint only).

Range check (assert(a < c))

The range check operation (assert(a < c)) is costly because it involves decomposing the variables into bits (still represented on large field elements).

Large variable (variable > modulus p)

If you need variables that exceed the modulus pp, a standard method is to split the variable into a smaller basis than pp (for example (p1)/2(p-1)/2), and write the variables in this basis.

Each digit of the resulting decomposition is smaller than pp, and therefore is not reduced.