---
# System prepended metadata

title: UltraGroth Explained
tags: [groth16, lookup-tables, zkp]

---

# UltraGroth Protocol Explained

## Introduction

Lookup checks is an amazing and elegant technique to reduce the number of constraints for arithmetical circuits. Indeed, when implementing arithmetical circuits over certain zero-knowledge protocol, one frequently needs to implement _non-native operations_ such as bits shifting, XORing, or range checks. Such operations typically cost _a lot_ when implemented over, say, BN254 field: for instance, any $\ell$-bit-wise operations require at least $\ell$ constraints to, at the very least, compute the bit decomposition of the integer.

To resolve this issue, [_plookup_](https://eprint.iacr.org/2020/315) proposed the effective way to integrate lookup optimization to the PlonKish-based proving systems. With lookups, many such non-native operations required significantly less constraints (for instance, range checks over small-sized limbs cost essentially a single constraint). Soon after, another protocol called [_logup_](https://eprint.iacr.org/2023/1284) enabled lookup checks in the sumcheck manner, which proved to be more effective. 

And while PlonK and sumcheck-based protocols got more and more attention in particular because of native effective lookup support, Groth16, due to non-interactive nature, could not enjoy the same optimizations. Indeed, as we will see, lookup checks require sampling the random scalars to conduct certain checks at a random point, which, up to today, is pratically infeasible in Groth16.

And this is _very unfortunate_! Groth16, despite being almost 10 years old, is still arguably the most verifier-effective protocol in the zero-knowledge field (probably besides some [recently developed exotic SNARKs on Bitcoin](https://alpenlabs.io/glock-paper)). Its proof is constant -- just 3 points on elliptic curves ($2\mathbb{G}_1+\mathbb{G}_2$), and its verification consists of computing only 3 pairing operations. Due to such efficiency, it is currently used as the primary ZK verification mechanism in Ethereum (in particular, `ecPairing` is implemented as a precompiled contract). So wouldn't it be nice if Groth16 also had lookup checks to speed up non-native operations?

### tl;dr

**UltraGroth** is addressed to insignificantly modify Groth16 protocol to enable sampling random field elements effectively and further pass them as signals to the circuit. The primary implication of such possibility is, of course, lookup checks!

Moreover, such modification saves all the verification benefits of Groth16: in UltraGroth's basic form, it adds only a single $\mathbb{G}_1$ point to the proof, a single pairing operation, and a single hashing operation per randomness. 

One of the examples where UltraGroth has already been used is [_Bionetta_](https://mirror.xyz/0x90699B5A52BccbdFe73d5c9F3d039a33fb2D1AF6/T4lHiBlo7VoYp-5SEuitQnY7_ullpwkZbanOOwjeZI4) -- an R1CS-based zkML framework used by [_Rarimo_](https://rarimo.com/). When implementing $\text{ReLU}(x)=\max\{0,x\}$ non-linearity in-circuit, in the vanilla Groth16 one needs to perform the full bit-decomposition of $x$, thus costing $\approx 254$ constraints per call. As a result, the circuit size for the neural network is roughly $\mathcal{O}(n)$ with $n=254L$ with $L$ being the number of ReLU calls. Using UltraGroth, we reduced the number of constraints to the _sublinear complexity_: $\mathcal{O}(n/\log n)$. You can check some details in our [UltraGroth+Bionetta](https://mirror.xyz/0x90699B5A52BccbdFe73d5c9F3d039a33fb2D1AF6/T4lHiBlo7VoYp-5SEuitQnY7_ullpwkZbanOOwjeZI4) blog.

### Historical Notes

Before delving into specifics, we briefly mention the history of this protocol, which is quite curious. This blog is based on the original [Lev Soukhanov's post](https://hackmd.io/@Merlin404/Hy_O2Gi-h) in 2023. However, as we prepared the conference paper for Bionetta, we discovered that his construction is nothing but a generalization of [$\textsf{MIRAGE}$ protocol](https://eprint.iacr.org/2020/278), posted back in 2020! (in subsequent notation, $\textsf{MIRAGE}$ is an UltraGroth protocol with $d=1$). Even more surprisingly, it seems that independently of Lev Soukhanov's construction, Alex Ozdemir, Evan Laufer, and Dan Boneh introduced [$\textsf{MIRAGE+}$ protocol](https://eprint.iacr.org/2024/979) in 2024, which was used for proving RAM computations correctness. This protocol, which is hard to believe, but also coincides with the Lev Soukharnov's construction (although I believe the former generalizes the construction a bit more rigorously). That said, to the best of my knowledge, Lev Soukhanov's construction is the first to generalize $\textsf{MIRAGE}$ to the multiple-round interactive protocol.

All three papers (Bionetta included) proved completeness, soundness, and zero-knowledge of this construction, so the protocol that follows can be safely integrated into production systems. We, of course, drop formalities in this blog and recommend checking [$\textsf{MIRAGE+}$ protocol](https://eprint.iacr.org/2024/979) paper for proof specifics in case someone is interested.

Finally, my personal opinion is that this construction is vastly underestimated and it is surprising that it is not yet used in production. Indeed, integrating lookup checks comes with a tiny cost of additional pairing which enables wrapping non-native protocols in Groth16 (e.g., zk-STARKs) _much_ more effectively (up to a factor of $\times 20$). 

> [!WARNING]
> The subsequent explanations will be quite technical, so if you want easier and more straightforward overview, you can additionally check out [_our brief internal Distributed Lab presentation_](https://github.com/ZamDimon/presentations-and-talks/blob/main/ultragroth/ultragroth-static.pdf).

## Lookup Checks

Here we restate some of the key results originally presented in [_logup_](https://eprint.iacr.org/2023/1284).

Suppose lookup table is given by $\{t_i\}_{i \in [v]}$ while the part of the witness to be lookup-checked is $\{z_i\}_{i \in [n]}$: that is, we prove inclusion $\{z_i\}_{i \in [n]} \subseteq \{t_i\}_{i \in [v]}$. As an example, setting $t_i:=i$ for $v=2^w$ checks whether each $z_i$ is the $w$-bit integer. One of the ways to perform such inclusion check is what follows.

**Theorem**. Given two sequences of elements $\{t_i\}_{i \in [v]}$ and $\{z_i\}_{i \in [n]}$, the inclusion check $\{z_i\}_{i \in [n]} \subseteq \{t_i\}_{i \in [v]}$ is satisfied with overwhelming probability if and only if there exists the set of multiplicities $\{\mu_i\}_{i \in [v]}$ where $\mu_i = \#\{j \in [n]: z_j = t_i\}$ such that for randomly sampled $\gamma \gets_R \mathbb{F}$:
$$
        \sum_{i \in [n]} \frac{1}{\gamma+z_i} = \sum_{i \in [v]} \frac{\mu_i}{\gamma+t_i}
$$

More specifically, checking such equality at a random point from $\mathbb{F}$ results in the soundness error of up to $(n+v)/|\mathbb{F}|$, which becomes negligible for large $|\mathbb{F}|$.


Now, the right hand size of the equation, given that we have sampled the
randomness $\gamma \gets \mathbb{F}$, can be computed in $v$ constraints, while
the left hand side is computed in additional $n$ constraints (recall that inversion costs a single constraint to prove). Again, since Groth16 is a non-interactive protocol in its essence (meaning, compared to PlonK or sumcheck-based protocols, it is not constructed by converting an interactive protocol to non-interactive), there is no yet effective way to sample $\gamma$ (besides probably in-circuit hashing which is insanely costly). Yet, we do propose a way to sample $\gamma$ by turning the Groth16 into the "interactive-like" proof system.

The proceeding _UltraGroth_ construction is essentially devoted to sampling $\gamma$ and passing it as a signal to the circuit soundly.

### Some other implications

Sampling randomnesses overall enables numerous applications besides lookup checks. For instance, suppose you have matrices $A,B \in \mathbb{F}^{n \times n}$ and you want to write the circuit that computes their product: $C=AB$. The naive way to do that is to compute it by definition. However, such circuit costs $\mathcal{O}(n^3)$ constraints, which is a lot. Turns out that sampling randomnesses can reduce this significantly. Indeed, one can apply the _Freiveld's_ protocol: first sample $\gamma \gets_R \mathbb{F}^n$ and instead of checking $AB=C$, check whether matrices $AB$ and $C$ project $\gamma$ to the same vector (this looks somewhat similar to Schwartz-Zippel lemma, but in the world of matrices):

$$
(AB)\gamma = C\gamma \iff A(B\gamma) = C\gamma
$$

Now notice that this can be implemented in approximately $3n^2$ constraints: one first multiplies $u \gets B\gamma$, then multiplies $Au$ and compares it to the product $C\gamma$. Note that matrix-vector product can be implemented in $n^2$ constraints, which is much more effective than matrix-matrix product with $n^3$ constraints.

However, currently, in Circom there is no way to pass $\gamma$ as a signal to the circuit. Indeed, one can write `signal input gamma[n]` and mark it as public, but how can the verifier be ensured that corresponding values in witness were indeed sampled uniformly randomly by the prover? So let's see how to fix it.


## UltraGroth

### Groth16 Recap

We first remind the readers of the Groth16 construction. If you feel comfortable about your knowledge of Groth16, feel free to skip this part.

#### R1CS Arithmetization

We start with R1CS. Recall that R1CS encodes the program in the form:
$$
L\mathbf{z} \odot R\mathbf{z} = O\mathbf{z},
$$

where $L,R,O \in \mathbb{F}^{m \times n}$ are constant matrices, $\mathbf{z} \in \mathbb{F}^n$ is the _solution witness_ that encodes all intermediate computations, $m$ is the number of constraints, and $n$ is the witness size. One insanely helpful property of R1CS is that any linear combination of signals costs 0 constraints to implement, which is not present in many other arithmetization systems, such as PlonK. 

#### Quadratic Arithmetic Program (QAP)

Since zero-knowledge protocols typically operate over polynomials, we encode R1CS in the polynomial form. We build polynomials $\{\ell_i(X)\}_{i \in [n]}, \{r_i(X)\}_{i \in [n]}, \{o_i(X)\}_{i \in [n]} \subseteq \mathbb{F}^{\leq m}[X]$ that interpolate the columns of corresponding matrices $L,R,O$. That is,
$$
\ell_i(\omega^j) = L_{i,j}, \; r_i(\omega^j) = R_{i,j}, \; o_i(\omega^j)=O_{i,j}, \; i \in [n], j \in [m]
$$

The R1CS check is now expressed as the polynomial identity:
$$
\sum_{i \in [n]}z_i\ell_i(X) \cdot \sum_{i \in [n]}z_ir_i(X) = \sum_{i \in [n]}z_io_i(X) + t_{\Omega}(X)h(X),
$$

where $h(X)$ is the polynomial computed by the prover and $t_{\Omega}(X) := \prod_{h \in \Omega}(X-h)$ is the vanishing polynomial of $\Omega=\{\omega^j\}_{j \in [m]}$. We formally define the QAP relation as follows:
$$
\small\mathcal{R}_{\text{QAP}} = \left\{ 
        \begin{aligned}
            &\mathbf{x} = \{z_i\}_{i \in \mathcal{I}_{X}} \\
            &\mathbf{w} = \{z_i\}_{i \in \mathcal{I}_{W}}
        \end{aligned}
        \; \middle| \; 
        \begin{matrix}
            \sum_{i \in [n]}z_i\ell_i(X) \cdot \sum_{i \in [n]}z_ir_i(X) = \sum_{i \in [n]}z_io_i(X) + t_{\Omega}(X)h(X) \\
            \text{for some} \; h(X) \in \mathbb{F}[X]
        \end{matrix}
    \right\}
$$

Here, $\mathcal{I}_X \subseteq [n]$ is the index set of _public inputs_, while $\mathcal{I}_W := [n] \setminus \mathcal{I}_X$ encodes the _private inputs_. For concreteness, assume we have $l := |\mathcal{I}_X|$ public inputs and thus $n-l=|\mathcal{I}_W|$ private signals.

#### Groth16 Construction

Further, we write group operations multiplicatively. The Groth16 systems operates over the bilinear group $\mathcal{G}=(\mathbb{G}_1,\mathbb{G}_2,\mathbb{G}_T,e)$ with the non-degenerate efficiently computable pairing operation $e: \mathbb{G}_1 \times \mathbb{G}_2 \to \mathbb{G}_T$ that satisfies a bilinear property, which historically enabled constructing a large set of zk-SNARKs: $e(g_1^{\alpha},g_2^{\beta})=e(g_1^{\alpha\beta},g_2)=e(g_1,g_2^{\alpha\beta})=e(g_1,g_2)^{\alpha\beta}$. Let $g_1,g_2,g_T$ be the generators of corresponding groups. Now we specify the proof construction.

$\textbf{Setup}(1^{\lambda},\mathcal{R}_{\text{QAP}})$. Denote by $f_i(X):= \beta \ell_i(X)+\alpha r_i(X)+o_i(X)$ the random linear combination of $\ell_i,r_i,o_i$. During the trusted setup, one generates the toxic waste $\alpha,\beta,\gamma,\delta,\tau \gets_R \mathbb{F}$ and computes the following common parameters:
$$
\small\mathsf{pp} \gets \left(g_1^{\alpha},g_1^{\beta},g_1^{\delta},g_2^{\beta}, g_2^{\gamma}, g_2^{\delta}, \left\{g_1^{\tau^i}, g_2^{\tau^i}, g_1^{\tau^it(\tau)/\delta}\right\}_{i \in [n]}, \left\{g_1^{f_i(\tau)/\gamma}\right\}_{i \in \mathcal{I}_X}, \left\{g_1^{f_i(\tau)/\delta}\right\}_{i \in \mathcal{I}_W}\right)
$$

$$
\small\mathsf{vp} \gets \left( g_1,g_2, g_2^{\gamma}, g_2^{\delta}, g_T^{\alpha\beta}, \left\{g_1^{f_i(\tau)/\gamma}\right\}_{i \in \mathcal{I}_{X}} \right)
$$

$\textbf{Prove}(\mathsf{pp},\mathbf{x},\mathbf{w})$. The prover samples random scalars $r,s \gets_R \mathbb{F}$ and outputs the proof $\pi \gets (g_1^{a(\tau)},g_1^{c(\tau)},g_2^{b(\tau)})$ where:

$$
a(X) = \alpha + \sum_{i \in [n]}z_i\ell_i(X) + r\delta, \; b(X) = \beta + \sum_{i \in [n]}z_ir_i(X) + s\delta,
$$

$$
c(X) = \delta^{-1}\left(\sum_{i \in \mathcal{I}_W} z_if_i(X)+h(X)t_{\Omega}(X)\right) + a(X)s + b(X)r - rs\delta
$$

$\textbf{Verify}(\mathsf{vp},\mathbf{x},\pi)$. Upon receiving $\pi$, the verifier parses the proof as $(\pi_A,\pi_C,\pi_B)$ and accepts the proof if and only if the following check holds:
$$
e(\pi_A,\pi_B) = e(g_1^{\alpha},g_2^{\beta})\cdot e(g_1^{i(\tau)},g_2^{\gamma})\cdot e(\pi_C,g_2^{\delta}),
$$

where $i(X) := \gamma^{-1}\sum_{i \in \mathcal{I}_X} z_i f_i(X)$. 

#### Efficiency

Following the original Groth16 paper's notation, assume the cost of $\mathbb{G}_1$ and $\mathbb{G}_2$ exponentiations is $E_1$ and $E_2$, respectively. Assume that the pairing operation costs $E$. Then, the prover's work is $\mathcal{O}(n \cdot E_1+n \cdot E_2)$ operations while verification is $3E+\mathcal{O}(l \cdot E_1)$. 

### Two-round Interactive R1CS

Finally, this is the part where we finally introduce the new construction. Suppose we split our R1CS into multiple rounds. Consider the case where we want to implement lookup checks. In such case, it would be nice if we could compile the following interactive protocol (IP) into the zk-SNARK for a relation $\mathcal{R}_{\text{QAP}}$:
- _Round 0_: The prover runs the circuit without imposing lookup checks. The prover sends resultant witness $\mathbf{w}_0$ to verifier. Verifier responds with the random challenge $x_1 \gets_R \mathbb{F}$.
- _Round 1_: The prover computes the witness $\mathbf{w}_1$ that corresponds to the lookup check (that is, that verifies equality $\sum_{i} \frac{1}{x_1+z_i}=\sum_i \frac{\mu_i}{x_1+t_i}$) using the randomness $x_1$ provided by verifier. The prover sends $\mathbf{w}_1$ and polynomial $h(X)$ according to $\mathcal{R}_{\text{QAP}}$.
- Finally, the verifier checks the same QAP equation $\ell(X)r(X)=o(X)+t_{\Omega}(X)h(X)$.

We would like to compile such IP into zk-SNARK by applying the Fiat-Shamir heuristic. However, here is the issue: $x_1$ must be derived by hashing the previous transcript, which is the public statement $\mathbf{x}_0$ and witness $\mathbf{w}_0$. However, how can we hash the witness $\mathbf{w}_0$, given that the whole Groth16 proving system compiles the verification equation to just three points?

Now, suppose we partition the index set of public signals $\mathcal{I}_X$ into two parts (corresponding to each round): $\mathcal{I}_X^{\langle 0 \rangle}$ and $\mathcal{I}_X^{\langle 1 \rangle}$. Corresponding private signals are indexed by $\mathcal{I}_W^{\langle 0 \rangle}$ and $\mathcal{I}_W^{\langle 1 \rangle}$ which partition $\mathcal{I}_W$. In the example above, $\mathcal{I}_X^{\langle 0 \rangle}$ corresponds to the regular public signals in relation $\mathcal{R}_{\text{QAP}}$ while $\mathcal{I}_X^{\langle 1 \rangle}$ indexes the single solution witness element, corresponding to sampled randomness $x_1$.

The idea is the following: we split the polynomial $c(X)$ (which was described in Groth16 construction above) into two pieces $c_0(X)$ and $c_1(X)$. The first piece corresponds to the first round indexed by $(\mathcal{I}_X^{\langle 0 \rangle}, \mathcal{I}_W^{\langle 0 \rangle})$ and is formed as follows: sample $r_0 \gets_R \mathbb{F}$, add toxic parameter $\delta_0$, and compute the point $\pi_C^{\langle 0 \rangle} \gets g_1^{c_0(\tau)}$ where:
$$
c_0(X) = \delta_0^{-1}\sum_{j \in \mathcal{I}_W^{\langle 0 \rangle}} z_jf_j(X) + r_0\delta
$$

Notice that the point $\pi_C^{\langle 0 \rangle}$ encodes all witness elements in Round 0. Thus, to sample randomness, we can hash prover parameters and this point: $x_1 \gets \mathcal{H}(\mathsf{pp}, \pi_C^{\langle 0 \rangle})$. Then, points $\pi_A=g_1^{a(\tau)}$ and $\pi_B=g_1^{b(\tau)}$ are computed in the same way as in the regular Groth16, while the third piece $\pi_C^{\langle 1 \rangle} = g_1^{c_1(\tau)}$ is computed using:
$$
c_1(X) = \delta^{-1}\left(\sum_{j \in \mathcal{I}_W^{\langle 1 \rangle}} z_jf_j(X)+h(X)t_{\Omega}(X)\right) + a(X)s + b(X)r - r_0\delta_0 - rs\delta
$$

Essentially, such construction ensures that $\delta_0c_0(X)+\delta c_1(X)$ gives exactly the polynomial $\delta c(X)$ in the original Groth16. This allows the prover to construct the proof as a tuple $\pi=(\pi_A,\pi_B,\pi_C^{\langle 0 \rangle},\pi_C^{\langle 1 \rangle})$ and verifier to check it using:
$$
e(\pi_A,\pi_B) = e(g_1^{\alpha},g_2^{\beta}) \cdot e(g_1^{i(\tau)},g_2^{\gamma}) \cdot e(\pi_C^{\langle 0 \rangle}, g_2^{\delta_0}) \cdot e(\pi_C^{\langle 1 \rangle}, g_2^{\delta}),
$$

where $i(X) = \gamma^{-1}\sum_{i \in \mathcal{I}_X}z_if_i(X)$ as before. As in Groth16, the term $e(g_1^{\alpha}, g_2^{\beta})$ can be pre-computed, thus the cost of verification is 4 pairing operations.

> [!NOTE]
> The aforementioned protocol, as mentioned earlier, is essentially the $\textsf{MIRAGE}$ protocol, and can be implemented as is. It is complete, sound, and zero-knowledge. In the subsequent section, we will generalize this protocol for more than 1 round.

### Generalizing to multiple rounds

To generalize the aforementioned protocol, we define the $(d+1)$-round quadratic arithmetic program (or $d$QAP, for short) as follows:

$$
\small\mathcal{R}_{\text{dQAP}} = \left\{ 
        \begin{matrix} 
            \mathbf{x}_i = \{z_j\}_{j \in \mathcal{I}_{X}^{\langle i \rangle}} \\
            \mathbf{w}_i = \{z_j\}_{j \in \mathcal{I}_{W}^{\langle i \rangle}} \\
            \text{for }i \in [d+1]
        \end{matrix}
        \; \middle| \; 
        \begin{matrix}
            \sum_{i \in [n]}z_i\ell_i(X) \cdot \sum_{i \in [n]}z_ir_i(X) = \sum_{i \in [n]}z_io_i(X) + t_{\Omega}(X)h(X) \\
            \text{for some} \; h(X) \in \mathbb{F}[X]
        \end{matrix}
    \right\},
$$

where indices $\{\mathcal{I}_X^{\langle i \rangle}\}_{i \in [d+1]}$ and $\{\mathcal{I}_W^{\langle i \rangle}\}_{i \in [d+1]}$ partition $[n]$. In this notation, all $\{\mathbf{w}_i\}_{i \in [d+1]}$ are called _private witnesses_, $\mathbf{x}_0$ is a _public statement_, while $\{\mathbf{x}_i\}_{i \in [d+1]\setminus \{0\}}$ are _challenges_ (that are going to be sampled by means of UltraGroth).

Since prover and verifier participate in the interactive protocol, the prover cannot compute the whole witness on his own without verifier's randomnesses. For that reason, we define _strategy_ as the collection of functions $S=\{S_i\}_{i \in [d]}$, each of which computes the witness for the given round given previous witnesses and challenges and the current challenge, sampled by the verifier. In other words,
$$
\mathbf{w}_i = S_i(\mathbf{x}_0,\dots,\mathbf{x}_i,\mathbf{w}_0,\dots,\mathbf{w}_{i-1})
$$

> [!NOTE]
> For instance, 0QAP represents the regular Groth16 construction, where strategy $S_0$ is a regular witness calculator: $\mathbf{w}_0=S_0(\mathbf{x}_0)$. The protocol which involes lookup checks is a SNARK over 1QAP, where $S_0$ computes the R1CS witness without lookup constraints, while $S_1$, based on the sampled randomness(es) $\mathbf{x}_1$, imposes lookup check condition: $\mathbf{w}_1=S_1(\mathbf{x}_0,\mathbf{x}_1,\mathbf{w}_0)$.

Now, we can describe the _UltraGroth_ protocol over $\mathcal{R}_{\text{dQAP}}$.

$\textbf{Setup}(1^{\lambda},\mathcal{R}_{\text{dQAP}})$. As in previous sections, denote by $f_i(X):= \beta \ell_i(X)+\alpha r_i(X)+o_i(X)$ the random linear combination of $\ell_i,r_i,o_i$. During the trusted setup, one generates the toxic waste $\alpha,\beta,\gamma,\{\delta_i\}_{i \in [d+1]},\tau \gets_R \mathbb{F}$ and computes the following common parameters:
$$
\mathsf{pp} = \Big( g_1^{\alpha},g_1^{\beta},\{g_1^{\delta_i}\}_{i \in [d]},g_2^{\beta}, g_2^{\gamma}, \{g_2^{\delta_i}\}_{i \in [d]}, \Big\{g_1^{\tau^i}, g_2^{\tau^i}, g_1^{\tau^it(\tau)/\delta_d}\Big\}_{i \in [n]}, \left\{g_1^{f_i(\tau)/\gamma}\right\}_{i \in \mathcal{I}_X}, \left\{g_1^{f_j(\tau)/\delta_i}\right\}_{(i,j) \in [d] \times \mathcal{I}_W^{(i)}}\Big)
$$

$$
\mathsf{vp} = \left( g_1,g_2, g_2^{\gamma}, \{g_2^{\delta_i}\}_{i \in [d]}, g_T^{\alpha\beta}, \left\{g_1^{f_i(\tau)/\gamma}\right\}_{i \in \mathcal{I}_{X}} \right)
$$

$\textbf{Prove}(\mathsf{pp},\mathbf{x}_0,S)$. The prover initializes the accumulator $a_0 := \mathcal{H}(\mathsf{pp})$. For each round $i \in [d]$ she conducts the following steps:
- Samples $r_i \gets_R \mathbb{F}$
- Computes witness $\mathbf{w}_i \gets S_i(\mathbf{x}_0,\dots,\mathbf{x}_i,\mathbf{w}_0,\dots,\mathbf{w}_{i-1})$.
- Computes $\pi_C^{\langle i \rangle} \gets g_1^{c_i(\tau)}$ with $c_i(X) := \delta_i^{-1}\sum_{j \in \mathcal{I}_W^{\langle i \rangle}}z_jf_j(X) + r_i\delta_d$.
- Updates accumulator as $a_{i+1} \gets \mathcal{H}(a_i,\pi_C^{\langle i \rangle})$.
- If $i<d$, for each $j \in \mathcal{I}_X^{\langle i+1 \rangle}$ sets $z_j \gets \mathcal{H}(a_{i+1},g_1^j)$.

After finishing $d$ rounds, the prover does the following:
- Computes polynomial $h(X)$ from QAP as in Groth16.
- Samples random $r,s \gets \mathbb{F}$ and computes $\pi_A \gets g_1^{a(\tau)}$, $\pi_B \gets g_2^{b(\tau)}$ and the last proof piece $\pi_C^{\langle d \rangle} \gets g^{c_d(\tau)}$ where $a(X)$, $b(X)$, and $c(X)$ are defined as follows:
$$
a(X) = \alpha + \sum_{i \in [n]} z_i\ell_i(X) + r\delta_d, \;
b(X) = \beta + \sum_{i \in [n]}z_ir_i(X) + s\delta_d
$$

$$
c_d(X) = \delta_d^{-1}\left(\sum_{i \in \mathcal{I}_W^{\langle d \rangle}}z_if_i(X) + h(X)t_{\Omega}(X)\right) + a(X)s+b(X)r-\sum_{i \in [d]}r_i\delta_i - rs\delta_d
$$
- **Outputs** proof $\pi=(\pi_A,\pi_B,\pi_C^{\langle 0 \rangle}, \dots, \pi_C^{\langle d \rangle}) \in \mathbb{G}_1 \times \mathbb{G}_2 \times \mathbb{G}_1^{d+1}$.

$\textbf{Verify}(\mathsf{vp},\mathbf{x}_0,\pi)$. The verifier recomputes all the challenges $\{\mathbf{x}_i\}_{i \in [d+1] \setminus \{0\}}$ and verifies that corresponding public signals are correct. Then, she checks:
$$
e(\pi_A,\pi_B) = e(g_1^{\alpha},g_2^{\beta}) \cdot e(g_1^{i(\tau)},g_2^{\gamma}) \cdot \prod_{i \in [d+1]}e(\pi_C^{\langle i \rangle}, g_2^{\delta_i})
$$

### Efficiency Analysis

Finally, we specify the efficiency of UltraGroth.

- The prover's work is essentially the same as in Groth16: if the total witness size is $n$, the prover needs to conduct $\mathcal{O}(n \cdot E_1 + n \cdot E_2)$ operations with a couple of additional hashes (formally, $\sum_{i \in [d+1] \setminus \{0\}}|\mathbf{x}_i|$ hashes, which is a negligible amount in practice). Note however, that due to such R1CS splitting, the circuit's size $n$ is drastically decreased: in many cases it becomes sublinear in the original size.
- Verifier's work is $(d+3)E+\mathcal{O}(l \cdot E_1)$.
- Proof size is $1\;\mathbb{G}_2$ point and $(d+2)\;\mathbb{G}_1$ points.

### Practical Implementation and Further Steps

Currently, we have implemented the UltraGroth as a part of the Bionetta project for $d=1$ in the folowing repository: https://github.com/rarimo/ultragroth. Also, we have a custom [_SnarkJS_ fork](https://github.com/rarimo/ultragroth-snarkjs) that implements smart contract generation and witness verify and export operations. But the current implementation is vastly inflexible and currently primarily works in conjuction with _Bionetta's witness generator_ only. This implementation takes the regular Circom-generated `.wtns` and `.sym` files, parses them, figures out which witness element corresponds to the randomness, and during the proving procedure fills in this spot using the protocol above. 

We look forward to completing the implementation that could be easily integrated into other's projects. One nice way to implement that would be to add a special keyword to the Circom compiler. For instance, it would be nice if we could write `random signal r[N]` that fills in the vector `r` with random values, although such task is surely challenging.

### Acknowledgements

Of course, without Lev Soukhanov this post would not exist. I also thank Artem Sdobnov, Vitalii Volovyk, Yevhenii Sekhin, and Illia Dovgopoly for the UltraGroth implementation for Bionetta.

## Summary

In case you implement complex circuits involving non-native operations (e.g., wrapping the verification of some ZK protocol), it is definitely worth checking out the UltraGroth protocol. With the tiny verification overhead, one gets an immense boost to the proving procedure. In particular, it makes client-side ZK much more practical and realistic without sacrificing verification efficiency.