Added Dec 27 2017: An updated version of these notes exists on the class page.

Special Topics in Complexity Theory, Fall 2017. Instructor: Emanuele Viola

### 1 Lecture 10, Guest lecture by Justin Thaler, Scribe: Biswaroop Maiti

This is a guest lecture by Justin Thaler regarding lower bounds on approximate degree [BKT17, BT15, BT17]. Thanks to Justin for giving this lecture and for his help with the write-up. We will sketch some details of the lower bound on the approximate degree of , and some intuition about the techniques used. Recall the definition of from the previous lecture as below:

**Definition 1.** The surjectivity function , takes input where each is interpreted as an element of . has value if and only if .

Recall from the last lecture that is the block-wise composition of the function on bits and the function on bits. In general, we will denote the block-wise composition of two functions , and , where is defined on bits and is defined on bits, by . Here, the outputs of copies of are fed into (with the inputs to each copy of being pairwise disjoint). The total number of inputs to is .

#### 1.1 Lower Bound of via lower bound of AND-OR

**Claim 2.** .

We will look at only the lower bound in the claim. We interpret the input as a list of numbers from . As presented in [BKT17], the proof for the lower bound proceeds in the following steps.

- Show that to approximate , it is necessary to approximate the block-composition on inputs of Hamming weight at most . i.e., show that .
Step 1 was covered in the previous lecture, but we briefly recall a bit of intuition for why the claim in this step is reasonable. The intuition comes from the fact that the *converse* of the claim is easy to establish, i.e., it is easy to show that in order to approximate , it is *sufficient* to approximate on inputs of Hamming weight exactly .

This is because can be expressed as an (over all range items ) of the (over all inputs ) of “Is input equal to ”? Each predicate of the form in quotes is computed exactly by a polynomial of degree , since it depends on only of the input bits, and exactly of the predicates (one for each ) evaluates to TRUE.

Step 1 of the lower bound proof for in [BKT17] shows a converse, namely that the *only* way to approximate is to approximate on inputs of Hamming weight at most .

- Show that , i.e., the degree required to approximate on inputs of Hamming weight at most is at least .

In the previous lecture we also sketched this Step 2. In this lecture we give additional details of this step. As in the papers, we use the concept of a “dual witness.” The latter can be shown to be equivalent to bounded indistinguishability.

Step 2 itself proceeds via two substeps:

- Give a dual witness for that has places little mass (namely, total mass less then ) on inputs of hamming weight .
- By modifying , give a dual witness for that places zero mass on inputs of Hamming weight .

In [BKT17], both Substeps 2a and 2b proceed entirely in the dual world (i.e., they explicitly manipulate dual witnesses and ). The main goal of this section of the lecture notes is to explain how to replace Step 2b of the argument of [BKT17] with a wholly “primal” argument.

The intuition of the primal version of Step 2b that we’ll cover is as follows. First, we will show that a polynomial of degree that is bounded on the low Hamming Weight inputs, cannot be too big on the high Hamming weight inputs. In particular, we will prove the following claim.

**Claim 3.** If is a degree polynomial that satisfies on all inputs of of Hamming weight at most , then for *all* inputs .

Second, we will explain that the dual witness constructed in Step 2a has the following “primal” implication:

**Claim 4.** For , any polynomial of degree satisfying for all inputs of Hamming weight at most must satisfy for some input .

Combining Claims 3 and 4, we conclude that no polynomial of degree can satisfy

which is exactly the desired conclusion of Step 2. This is because any polynomial satisfying Equation (1) also satisfies for all of Hamming weight of most , and hence Claim 3 implies that

But Claim 4 states that any polynomial satisfying both Equations (1) and (2) requires degree strictly larger than .

In the remainder of this section, we prove Claims 3 and 4.

#### 1.2 Proof of Claim 3

Proof of Claim 3. For notational simplicity, let us prove this claim for polynomials on domain , rather than .

**Proof in the case that is symmetric.** Let us assume first that is symmetric, i.e., is only a function of the Hamming weight of its input . Then for some degree univariate polynomial (this is a direct consequence of Minsky-Papert symmetrization, which we have seen in the lectures before). We can express as below in the same spirit of Lagrange interpolation.

Here, the first term, ,is bounded in magnitude by , and . Therefore, we get the final bound:

**Proof for general .** Let us now consider the case of general (not necessarily symmetric) polynomials . Fix any input . The goal is to show that .

Let us consider a polynomial of degree obtained from by restricting each input such that to have the value 0. For example, if and , then . We will exploit three properties of :

- .
- Since for all inputs with , satisfies the analogous property: for all inputs with .
- If denotes the all-1s vector of length , then .

Property 3 means that our goal is to show that .

Let denote the symmetrized version of , i.e., , where the expectation is over a random permutation of , and . Since for all permutations , . But is symmetric, so Properties 1 and 2 together mean that the analysis from the first part of the proof implies that for all inputs . In particular, letting , we conclude that as desired.

**Discussion.** One may try to simplify the analysis of the general case in the proof Claim 3 by considering the polynomial defined via ], where the expectation is over permutations of . is a symmetric polynomial, so the analysis for symmetric polynomials immediately implies that . Unfortunately, this does *not* mean that .

This is because the symmetrized polynomial is averaging the values of over all those inputs of a given Hamming weight. So, a bound on this averaging polynomial does not preclude the case where is massively positive on some inputs of a given Hamming weight, and massively negative on other inputs of the same Hamming weight, and these values cancel out to obtain a small average value. That is, it is not enough to conclude that on the average over inputs of any given Hamming weight, the magnitude of is not too big.

Thus, we needed to make sure that when we symmetrize to , such large cancellations don’t happen, and a bound of the average value of on a given Hamming weight really gives us a bound on on the input itself. We defined so that . Since there is only *one* input in of Hamming weight , does not average ’s values on many inputs, meaning we don’t need to worry about massive cancellations.

**A note on the history of Claim 3.** Claim 3 was implicit in [RS10]. They explicitly showed a similar bound for symmetric polynomials using primal view and (implicitly) gave a different (dual) proof of the case for general polynomials.

#### 1.3 Proof of Claim 4

##### 1.3.1 Interlude Part 1: Method of Dual Polynomials [BT17]

A dual polynomial is a dual solution to a certain linear program that captures the approximate degree of any given function . These polynomials act as certificates of the high approximate degree of . The notion of strong LP duality implies that the technique is lossless, in comparison to symmetrization techniques which we saw before. For any function and any , there is always some dual polynomial that witnesses a tight -approximate degree lower bound for . A dual polynomial that witnesses the fact that is a function satisfying three properties:

##### 1.3.2 Interlude Part 2: Applying The Method of Dual Polynomials To Block-Composed Functions

For any function , we can write an LP capturing the approximate degree of . We can prove lower bounds on the approximate degree of by proving lower bounds on the value of feasible solution of this LP. One way to do this is by writing down the Dual of the LP, and exhibiting a feasible solution to the dual, thereby giving an upper bound on the value of the Dual. By the principle of LP duality, an upper bound on the Dual LP will be a lower bound of the Primal LP. Therefore, exhibiting such a feasible solution, which we call a dual witness, suffices to prove an approximate degree lower bound for .

However, for any given dual witness, some work will be required to verify that the witness indeed meets the criteria imposed by the Dual constraints.

When the function is a block-wise composition of two functions, say and , then we can try to construct a good dual witness for by looking at dual witnesses for each of and , and combining them carefully, to get the dual witness for .

The dual witness constructed in Step 2a for is expressed below in terms of the dual witness of the inner function viz. and the dual witness of the outer , viz. .

This method of combining dual witnesses for the “outer” function and for the “inner function” is referred to in [BKT17, BT17] as *dual block composition*.

##### 1.3.3 Interlude Part 3: Hamming Weight Decay Conditions

Step 2a of the proof of the lower bound from [BKT17] gave a dual witness for (with ) that had pure high degree , and also satisfies Equations (4) and (5) below.

Equation (4) is a very strong “Hamming weight decay” condition: it shows that the total mass that places on inputs of high Hamming weight is very small. Hamming weight decay conditions play an essential role in the lower bound analysis for from [BKT17]. In addition to Equations (4) and (5) themselves being Hamming weight decay conditions, [BKT17]’s proof that satisfies Equations (4) and (5) exploits the fact that the dual witness for can be chosen to simultaneously have pure high degree , and to satisfy the following weaker Hamming weight decay condition:

**Claim 5.** There exist constants such that for all ,

(We will not prove Claim 5 in these notes, we simply state it to highlight the importance of dual decay to the analysis of ).

Dual witnesses satisfying various notions of Hamming weight decay have a natural primal interpretation: they witness approximate degree lower bounds for the target function ( in the case of Equation (4), and in the case of Equation (6)) *even when the approximation is allowed to be exponentially large on inputs of high Hamming weight*. This primal interpretation of dual decay is formalized in the following claim.

**Claim 6.** Let be any function mapping to . Suppose is a dual witness for satisfying the following properties:

- (Correlation): .
- (Pure high degree): has pure high degree .
- (Dual decay): for all .

Then there is no degree polynomial such that

##### 1.3.4 Proof of Claim 4

Proof. Claim 4 follows from Equations (4) and (5), combined with Claim 6. Specifically, apply Claim 6 with , and

### 2 Generalizing the analysis for to prove a nearly linear approximate degree lower bound for

Now we take a look at how to extend this kind of analysis for to obtain even stronger approximate degree lower bounds for other functions in . Recall that can be expressed as an (over all range items ) of the (over all inputs ) of “Is input equal to ”? That is, simply evaluates on the inputs where indicates whether or not input is equal to range item .

Our analysis for can be viewed as follows: It is a way to turn the function on bits (which has approximate degree ) into a function on close to bits, with polynomially larger approximate degree (i.e. is defined on bits where, say, the value of is , i.e., it is a function on bits). So, this function is on not much more than bits, but has approximate degree , polynomially larger than the approximate degree of .

Hence, the lower bound for can be seen as a hardness amplification result. We turn the function on bits to a function on slightly more bits, but the approximate degree of the new function is significantly larger.

From this perspective, the lower bound proof for showed that in order to approximate , we need to not only approximate the function, but, additionally, instead of feeding the inputs directly to gate itself, we are further driving up the degree by feeding the input through gates. The intuition is that we cannot do much better than merely approximate the function and then approximating the block composed gates. This additional approximation of the gates give us the extra exponent in the approximate degree expression.

We will see two issues that come in the way of naive attempts at generalizing our hardness amplification technique from to more general functions.

#### 2.1 Interlude: Grover’s Algorithm

**Grover’s algorithm** [Gro96] is a quantum algorithm that finds with high probability the unique input to a black box function that produces a given output, using queries on the function, where is the size of the the domain of the function. It is originally devised as a database search algorithm that searches an unsorted database of size and determines whether or not there is a record in the database that satisfies a given property in queries. This is strictly better compared to deterministic and randomized query algorithms because they will take queries in the worst case and in expectation respectively. Grover’s algorithm is optimal up to a constant factor, for the quantum world.

#### 2.2 Issues: Why a dummy range item is necessary

In general, let us consider the problem of taking any function that does not have maximal approximate degree (say, with approximate degree ), and turning it into a function on roughly the same number of bits, but with polynomially larger approximate degree.

In analogy with how equals evaluated on inputs , where indicates whether or not , we can consider the block composition evaluated on , and hope that this function has polynomially larger approximate degree than itself.

Unfortunately, this does not work. Consider for example the case . The function evaluates to 1 on all possible vectors , since all such vectors of Hamming weight exactly .

One way to try to address this is to introduce a dummy range item, all occurrences of which are simply ignored by the function. That is, we can consider the (hopefully harder) function to interpret its input as a list of numbers from the range , rather than range , and define (note that variables , which indicate whether or not each input equals range item , are simply ignored).

In fact, in the previous lecture we already used this technique of introducing a “dummy” range item, to ease the lower bound analysis for itself. Last lecture we covered Step 1 of the lower bound proof for , and we let denote the frequency of the dummy range item, 0. The introduction of this dummy range item let us replace the condition (i.e., the sum of the frequencies of all the range items is *exactly* ) by the condition (i.e., the sum of the frequencies of all the range items is *at most* ).

#### 2.3 A dummy range item is not sufficient on its own

Unfortunately, introducing a dummy range item is not sufficient on its own. That is, even when the range is is rather than , the function may have approximate degree that is *not* polynomially larger than that of itself. An example of this is (once again) . With a dummy range item, evaluates to TRUE if and only if at least one of the inputs is *not* equal to the dummy range item . This problem has approximate degree (it can be solved using Grover search).

Therefore, the most naive approach at general hardness amplification, even with a dummy range item, does not work.

#### 2.4 The approach that works

The approach that succeeds is to consider the block composition (i.e., apply the naive approach with a dummy range item not to itself, but to ). As pointed out in Section 2.3, the gates are crucial here for the analysis to go through.

It is instructive to look at where exactly the lower bound proof for breaks down if we try to adapt it to the function (rather than the function which we analyzed to prove the lower bound for ). Then we can see why the introduction of the gates fixes the issue.

When analyzing the more naively defined function (with a dummy range item), Step 1 of the lower bound analysis for *does work* unmodified to imply that in order to approximate , it is necessary to approximate block composition of on inputs of Hamming weight at most . But Step 2 of the analysis breaks down: one can approximate on inputs of Hamming weight at most using degree just .

Why does the Step 2 analysis break down for ? If one tries to construct a dual witness for by applying dual block composition (cf. Equation (3), but with the dual witness for replaced by a dual witness for ), will not be well-correlated with .

Roughly speaking, the correlation analysis thinks of each copy of the inner dual witness as consisting of a sign, , and a magnitude , and the inner dual witness “makes an error” on if it outputs the wrong sign, i.e., if . The correlation analysis winds up performing a union bound over the probability (under the product distribution ) that *any* of the copies of the inner dual witness makes an error. Unfortunately, each copy of the inner dual witness makes an error with constant probability under the distribution . So at least one of them makes an error under the product distribution with probability very close to 1. This means that the correlation of the dual-block-composed dual witness with is poor.

But if we look at , the correlation analysis *does* go through. That is, we can give a dual witness for and a dual witness for such that the the dual-block-composition of and is well-correlated with .

This is because [BT15] showed that for , . This means that has a dual witness that “makes an error” with probability just . This probability of making an error is so low that a union bound over all copies of appearing in the dual-block-composition of and implies that with probability at least , *none* of the copies of make an error.

In summary, the key difference between and that allows the lower bound analysis to go through for the latter but not the former is that the latter has -approximate degree for , while the former only has -approximate degree if is a constant bounded away from 1.

To summarize, the lower bound can be seen as a way to turn the function into a harder function , meaning that has polynomially larger approximate degree than . The right approach to generalize the technique for arbitrary is to (a) introduce a dummy range item, all occurrences of which are effectively ignored by the harder function , *and* (b) rather than considering the “inner” function , consider the inner function , i.e., let . The gates are essential to make sure that the error in the correlation of the inner dual witness is very small, and hence the correlation analysis for the dual-block-composed dual witness goes through. Note that can be interpreted as follows: it breaks the range up into blocks, each of length , (the dummy range item is excluded from all of the blocks), and for each block it computes a bit indicating whether or not every range item in the block has frequency at least 1. It then feeds these bits into .

By recursively applying this construction, starting with , we get a function in AC with approximate degree for any desired constant .

#### 2.5 distinctness

The above mentioned very same issue also arises in [BKT17]’s proof of a lower bound on the approximate degree of the -distinctness function. Step 1 of the lower bound analysis for reduced analyzing -distinctness to analyzing (restricted to inputs of Hamming weight at most ), where is the function that evaluates to TRUE if and only if its input has Hamming weight at least . The lower bound proved in [BKT17] for -distinctness is . is the function. So, is “close” to . And we’ve seen that the correlation analysis of the dual witness obtained via dual-block-composition breaks down for .

To overcome this issue, we have to show that is harder to approximate than itself, but we have to give up some small factor in the process. We will lose some quantity compared to the lower bound for . It may seem that this loss factor is just a technical issue and not intrinsic, but this is not so. In fact, this bound is almost tight. There is an upper bound from a complicated quantum algorithm [BL11, Bel12] for -distinctness that makes that we won’t elaborate on here.

### References

[Bel12] Aleksandrs Belovs. Learning-graph-based quantum algorithm for k-distinctness. In Foundations of Computer Science (FOCS), 2012 IEEE 53rd Annual Symposium on, pages 207–216. IEEE, 2012.

[BKT17] Mark Bun, Robin Kothari, and Justin Thaler. The polynomial method strikes back: Tight quantum query bounds via dual polynomials. arXiv preprint arXiv:1710.09079, 2017.

[BL11] Aleksandrs Belovs and Troy Lee. Quantum algorithm for k-distinctness with prior knowledge on the input. arXiv preprint arXiv:1108.3022, 2011.

[BT15] Mark Bun and Justin Thaler. Hardness amplification and the approximate degree of constant-depth circuits. In International Colloquium on Automata, Languages, and Programming, pages 268–280. Springer, 2015.

[BT17] Mark Bun and Justin Thaler. A nearly optimal lower bound on the approximate degree of . arXiv preprint arXiv:1703.05784, 2017.

[Gro96] Lov K Grover. A fast quantum mechanical algorithm for database search. In Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, pages 212–219. ACM, 1996.

[RS10] Alexander A Razborov and Alexander A Sherstov. The sign-rank of . SIAM Journal on Computing, 39(5):1833–1855, 2010.