# Computation

Computation → Arithmetic Circuit → R1CS → QAP → zk-SNARK

**Computation → Arithmetic Circuit → R1CS → QAP → zk-SNARK**
The first step in turning our transaction validity function into a mathematical representation is to break down the logical steps into the smallest possible operations, creating an “arithmetic circuit”.

Similar to a boolean circuit where a program is compiled down to discrete, single steps like AND, OR, NOT, when a program is converted to an arithmetic circuit, it’s broken down into single steps consisting of the basic arithmetic operations of addition, subtraction, multiplication, and division.

An example of what an arithmetic circuit looks like for computing the expression (a+b)*(b*c) Looking at such a circuit, we can think of the input values a, b, c as “traveling” left-to-right on the wires towards the output wire. Our next step is to build what is called a Rank 1 Constraint System, or R1CS, to check that the values are “traveling correctly”. In this example, the R1CS will confirm, for instance, that the value coming out of the multiplication gate where b and c went in is b*c.

In this R1CS representation, the verifier has to check many constraints — one for almost every wire of the circuit.

A method called “bundle all these constraints into one” uses a representation of the circuit called a Quadratic Arithmetic Program (QAP). The single constraint that needs to be checked is now between polynomials rather than between numbers.

The polynomials can be quite large, but this is alright because when an identity does not hold between polynomials, it will fail to hold at most points. Therefore, you only have to check that the two polynomials match at one randomly chosen point in order to correctly verify the proof with high probability.

If the prover knew in advance which point the verifier would choose to check, they might be able to craft polynomials that are invalid, but still satisfy the identity at that point. With zk-SNARKs, sophisticated mathematical techniques such as homomorphic encryption and pairings of elliptic curves are used to evaluate polynomials “blindly” – i.e. without knowing which point is being evaluated.

The public parameters described above are used to determine which point will be checked, but in encrypted form so that neither the prover nor the verifier know what it is.

The description so far has mainly addressed how to get the S and N in “SNARKs” — how to get a short, non-interactive, single message proof — but hasn’t addressed the “zk” (zero-knowledge) part which allows the prover to maintain the confidentiality of their secret inputs. It turns out that at this stage, the “zk” part can be easily added by having the prover use “random shifts” of the original polynomials that still satisfy the required identity. Here is a step-by-step, in-depth explanation of key concepts behind zk-SNARKs in PriceAI

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