# Lecture 009 - Grover's Algorithm

## Grover's Algorithm

Grover's Algorithm: solve $\text{SAT}$ in $O(n^\frac{n}{2})$

Unique SAT: promise there is exactly one solution in a circuit SAT.

Notice for Bias Busting, we detect if a truth table is biased toward $1$ or !0. For Unique SAT, we determine which row there is a $1$. The true power is that we can first $Had$ all bits to uniform superposition, then we can compile a classical code to quantum gates just by running sign-compute on uniform superposition. ### Reflection Accross Mean In order to understand grover's algorithm, we first build some helper function such that: when given a bunch of amplitude, it calculates the mean of the amplitude and reflect every amplitude accross the mean amplitude. def reflection_accross_mean(): Had X1, X2, ..., Xn If OR(X1, X2, ..., Xn) then Minus Had X1, X2, ..., Xn  Notice the above function is $4n$ instruction where $n$ is the number of variables. After the first line, we produce some vector who's first element is the mean and some random stuff after it: H_{all} \cdot \vec{f} = \vec{g} =\begin{bmatrix} \mu\\ \alpha\\ \beta\\ \dots\\ \zeta\\ \end{bmatrix} After the second line, we add negative sign on all amplitudes except the first one. g' = \begin{bmatrix} \mu\\ -\alpha\\ -\beta\\ -\dots\\ -\zeta\\ \end{bmatrix} After the third line, we successfully get reflected version of $\vec{f}$ called $\vec{f'}$. But why? Essentially, we need to prove that H_{all} \cdot \vec{g'} = \vec{f'} #### The Proof To see why, let's write $\vec{f}$ as its mean $\mu$ plus difference $\vec{\Delta}$ \vec{f} = \mu \begin{bmatrix} 1\\ 1\\ \dots\\ 1\\ 1\\ \end{bmatrix} + \begin{bmatrix} 0.25\\ 0.25\\ \dots\\ -1.75\\ 0.25\\ \end{bmatrix} = \mu \vec{1} + \vec{\Delta} Then reflection accross mean just means we want to transform \vec{f} = \mu \vec{1} + \vec{\Delta} \to \vec{f'} = \mu \vec{1} - \vec{\Delta} To see that $\vec{f'}$ equals, let's write $\vec{g}$ differently. $$\vec{g} = \mu \begin{bmatrix} 1\ 0\ \dots\ 0\ 0\ \end{bmatrix} + \begin{bmatrix} 0\ ?\ \dots\ ?\ ?\ \end{bmatrix} = \mu \vec{10} + \vec{?}\$$ Then we have the following \begin{align*} \vec{f} =& H_{all} \cdot \vec{g} \tag{by reverseH_{all}$}\\ \mu \vec{1} + \vec{\Delta} =& H_{all} \cdot (\mu \vec{10} + \vec{?})\tag{by writing$\vec{g}$differently}\\ \mu \vec{1} + \vec{\Delta} =& H_{all}(\mu \vec{10}) + H_{all}\vec{?}\tag{expand}\\ \mu \vec{1} + \vec{\Delta} =& \mu \vec{1} + H_{all}\vec{?} \tag{by$H_{all}\mu \vec{10} = \mu \vec{1}$}\\ \vec{\Delta} =& H_{all}\vec{?} \tag{by cancelation}\\ -\vec{\Delta} =& -H_{all}\vec{?} \tag{by negation}\\ \mu \vec{1}-\vec{\Delta} =& \mu \vec{1}-H_{all}\vec{?} \tag{by adding back$\mu \vec{1}$}\\ \vec{f'} =& \mu \vec{1}-H_{all}\vec{?} \tag{by$\vec{f'} = \mu \vec{1}-\vec{\Delta}$}\\ \vec{f'} =& H_{all}(\mu \vec{10}) - H_{all}\vec{?}\tag{extract$H_{all}$out}\\ \vec{f'} =& H_{all} (\mu \vec{10} - \vec{?}\tag{extract$H_{all}$out})\\ \vec{f'} =& H_{all} \vec{g'} \tag{by definition of$\vec{g'}}\\ \end{align*} Now, we have successfully proved the correctness of reflect accross the mean. ### Actual Algorithm Problem: given $f : \{0, 1\}^n \to \{0, 1\}$, find input $x$ so that $f(x) = 1$. Think Had x1, x2, ... followed by a signed compute as a way to do start some parallel computation by brute force trying all inputs but storing the result into negative amplitude. @require x1, x2, ... = 0 def solve_sat(f): // prepare uniform superposition Had x1, x2, ... // repeat following two steps If f() then Minus // reflection accross 0 reflection_accross_mean()  Let's trace down the states \begin{align*} [+1, +1, +1, +1, +1, +1, +1] \tag{uniform superposition}\\ [+1, +1, +1, +1, -1, +1, +1] \tag{iff()$then minus}\\ [+1, +1, +1, +1, +3, +1, +1] \tag{reflection accross mean}\\ [+1, +1, +1, +1, -3, +1, +1] \tag{if$f()$then minus}\\ [+1, +1, +1, +1, +5, +1, +1] \tag{reflection accross mean}\\ \dots\tag{as long as the mean is$+1^{ish}\$}\\ \end{align*}

For $n$ variables with $T$, we need $O(T)$ instructions for if f() then minus and $O(n)$ instructions for reflection accross mean. If we repeat $k$ times, then it is $O(knT)$

For small enough $k \leq 0.01\sqrt{2^n}$, amplitude of $x^*$ is about $2k + 1$. Which is about $\frac{1}{2500}$ probability.

To get $Pr\{\text{print out } x^*\} \simeq 100\%$, we need:

k = \sqrt{N} \cdot \frac{\pi}{4} = \sqrt{2^n} \cdot \frac{\pi}{4}

// QUESTION: why is the following equivalent H on A CNOT B A H on A

H on B CNOT A B H on B

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