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| -rw-r--r-- | content/know/concept/material-derivative/index.pdc | 8 | ||||
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9 files changed, 866 insertions, 15 deletions
@@ -1 +1,4 @@ /public/* +/sources/**/*.aux +/sources/**/*.log +/sources/**/*.pdf diff --git a/content/know/concept/cauchy-strain-tensor/index.pdc b/content/know/concept/cauchy-strain-tensor/index.pdc index f150723..cb48377 100644 --- a/content/know/concept/cauchy-strain-tensor/index.pdc +++ b/content/know/concept/cauchy-strain-tensor/index.pdc @@ -82,7 +82,7 @@ we expand the middle term to first order in $\va{a}$: $$\begin{aligned} \va{u}(\va{x} + \va{a}) \approx \va{u}(\va{x}) + a_x \pdv{\va{u}}{x} + a_y \pdv{\va{u}}{y} + a_z \pdv{\va{u}}{z} - = \va{u}(\va{x}) + \va{a} \cdot \nabla \va{u}(\va{x}) + = \va{u}(\va{x}) + (\va{a} \cdot \nabla) \va{u}(\va{x}) \end{aligned}$$ With this, we can now define the "shift" $\delta\va{a}$ @@ -91,7 +91,7 @@ as the difference between $\va{a}$ and $\va{A}$ like so: $$\begin{aligned} \delta{\va{a}} \equiv \va{a} - \va{A} - = \va{a} \cdot \nabla \va{u}(\va{x}) + = (\va{a} \cdot \nabla) \va{u}(\va{x}) \end{aligned}$$ In index notation, we write this expression as follows, @@ -245,8 +245,8 @@ is easy to express using the displacement field $\va{u}$: $$\begin{aligned} \boxed{ \delta(\dd{\va{l}}) - = \dd{\va{l}} \cdot \nabla \va{u} - = (\nabla \vec{u})^\top \cdot \dd{\va{l}} + = (\dd{\va{l}} \cdot \nabla) \va{u} + %= (\nabla \vec{u})^\top \cdot \dd{\va{l}} } \end{aligned}$$ @@ -259,9 +259,9 @@ $$\begin{aligned} = \delta(\va{a} \cross \va{b} \cdot \va{c}) &= \delta\va{a} \cross \va{b} \cdot \va{c} + \va{a} \cross \delta\va{b} \cdot \va{c} + \va{a} \cross \va{b} \cdot \delta\va{c} \\ - &= (\va{a} \cdot \nabla\va{u}) \cross \va{b} \cdot \va{c} - + \va{a} \cross (\va{b} \cdot \nabla\va{u}) \cdot \va{c} - + \va{a} \cross \va{b} \cdot (\va{c} \cdot \nabla\va{u}) + &= (\va{a} \cdot \nabla) \va{u} \cross \va{b} \cdot \va{c} + + \va{a} \cross (\va{b} \cdot \nabla )\va{u} \cdot \va{c} + + \va{a} \cross \va{b} \cdot (\va{c} \cdot \nabla) \va{u} \end{aligned}$$ We can reorder the factors like so @@ -303,7 +303,7 @@ $$\begin{aligned} \delta(\dd{V}) = \delta(\va{c} \cdot \dd{\va{S}}) = \delta\va{c} \cdot \dd{\va{S}} + \va{c} \cdot \delta(\dd{\va{S}}) - = (\va{c} \cdot \nabla\va{u}) \cdot \dd{\va{S}} + \va{c} \cdot \delta(\dd{\va{S}}) + = (\va{c} \cdot \nabla) \va{u} \cdot \dd{\va{S}} + \va{c} \cdot \delta(\dd{\va{S}}) \end{aligned}$$ By comparing this to the previous result for $\delta(\dd{V})$, @@ -311,7 +311,7 @@ we arrive at the following equation: $$\begin{aligned} \nabla \cdot \va{u} (\va{c} \cdot \dd{\va{S}}) - = (\va{c} \cdot \nabla\va{u}) \cdot \dd{\va{S}} + \va{c} \cdot \delta(\dd{\va{S}}) + = (\va{c} \cdot \nabla) \va{u} \cdot \dd{\va{S}} + \va{c} \cdot \delta(\dd{\va{S}}) \end{aligned}$$ Since $\va{c}$ is dot-multiplied at the front of each term, diff --git a/content/know/concept/deutsch-jozsa-algorithm/deutsch-circuit.png b/content/know/concept/deutsch-jozsa-algorithm/deutsch-circuit.png Binary files differnew file mode 100644 index 0000000..04f81fe --- /dev/null +++ b/content/know/concept/deutsch-jozsa-algorithm/deutsch-circuit.png diff --git a/content/know/concept/deutsch-jozsa-algorithm/deutsch-jozsa-circuit.png b/content/know/concept/deutsch-jozsa-algorithm/deutsch-jozsa-circuit.png Binary files differnew file mode 100644 index 0000000..a43ae6a --- /dev/null +++ b/content/know/concept/deutsch-jozsa-algorithm/deutsch-jozsa-circuit.png diff --git a/content/know/concept/deutsch-jozsa-algorithm/index.pdc b/content/know/concept/deutsch-jozsa-algorithm/index.pdc new file mode 100644 index 0000000..b2b3d98 --- /dev/null +++ b/content/know/concept/deutsch-jozsa-algorithm/index.pdc @@ -0,0 +1,234 @@ +--- +title: "Deutsch-Jozsa algorithm" +firstLetter: "D" +publishDate: 2021-04-08 +categories: +- Quantum information + +date: 2021-04-08T10:31:45+02:00 +draft: false +markup: pandoc +--- + +# Deutsch-Jozsa algorithm + +The **Deutsch algorithm** and its extension, the **Deutsch-Jozsa algorithm**, +were first to prove that quantum computers can +solve certain problems more efficiently +than any classical system. + +Given an unknown "black box" binary function $f(x)$ of one or more bits $x$, +the goal is determine whether $f$ is +**constant** (i.e. $f(x)$ is the same for all $x$) +or **balanced** (i.e. exactly 50\% of all $x$-values yield $f(x) = 0$, +and the other 50\% yield $f(x) = 1$). +We can query $f$ as many times as we want with inputs of our choice, +but we want to solve the problem using as few queries as possible. + +The problem is extremely artificial and of no practical use, +but quantum computers can solve it with a single query, +while classical computers need up to $2^{N - 1} + 1$ queries +for an $N$-bit $x$. + + +## Deutsch algorithm + +The Deutsch algorithm handles the simplest case, +where $x$ is only a single bit. +Only four $f$ exist: + ++ **Constant**: $(f(0) = f(1) = 0)$ or $(f(0) = f(1) = 1)$. ++ **Balanced**: $(f(0) = 0, f(1) = 1)$, or $(f(0) = 1, f(1) = 0)$. + +In other words, we only need to determine if $f(0) = f(1)$ or $f(0) \neq f(1)$. +To do this, we use the following quantum circuit, +where $U_f$ is the oracle we query: + +<a href="deutsch-circuit.png"> +<img src="deutsch-circuit.png" style="width:50%;display:block;margin:auto;"> +</a> + +Due to unitarity constraints, +the action of $U_f$ is defined to be as follows, +with $\oplus$ meaning XOR: + +$$\begin{aligned} + \ket{x} \ket{y} + \quad \to \boxed{U_f} \to \quad + \ket{x} \ket{y \oplus f(x)} +\end{aligned}$$ + +Starting on the left from two qubits $\ket{0}$ and $\ket{1}$, +we apply the Hadamard gate $H$ to both: + +$$\begin{aligned} + \ket{0} \ket{1} + \quad \to \boxed{H^{\otimes 2}} \to \quad + \ket{+} \ket{-} + = \frac{1}{2} \Big( \ket{0} + \ket{1} \Big) \Big( \ket{0} - \ket{1} \Big) +\end{aligned}$$ + +Feeding this result into the oracle $U_f$ then leads us to: + +$$\begin{aligned} + \to \boxed{U_f} \to \quad + \frac{1}{2} \ket{0} \Big( \ket{0 \oplus f(0)} - \ket{1 \oplus f(0)} \Big) + + \frac{1}{2} \ket{1} \Big( \ket{0 \oplus f(1)} - \ket{1 \oplus f(1)} \Big) +\end{aligned}$$ + +The parenthesized superpositions can be reduced. +Assuming that $f(b) = 0$, we notice: + +$$\begin{aligned} + \ket{0 \oplus f(b)} - \ket{1 \oplus f(b)} + = \ket{0 \oplus 0} - \ket{1 \oplus 0} + = \ket{0} - \ket{1} +\end{aligned}$$ + +On the other hand, if we assume that $f(b) = 1$, +we get the opposite result: + +$$\begin{aligned} + \ket{0 \oplus f(b)} - \ket{1 \oplus f(b)} + = \ket{0 \oplus 1} - \ket{1 \oplus 1} + = - \big(\ket{0} - \ket{1}\big) +\end{aligned}$$ + +We can thus combine both cases, $f(b) = 0$ or $f(b) = 1$, +into the following single expression: + +$$\begin{aligned} + \ket{0 \oplus f(b)} - \ket{1 \oplus f(b)} + = (-1)^{f(b)} \big(\ket{0} - \ket{1}\big) +\end{aligned}$$ + +Using this, we rewrite the intermediate state of the quantum circuit like so: + +$$\begin{aligned} + \ket{0} \ket{1} + \quad \to \boxed{H^{\otimes 2}} \to \boxed{U_f} \to \quad + \frac{1}{2} \Big( (-1)^{f(0)} \ket{0} + (-1)^{f(1)} \ket{1} \Big) \Big( \ket{0} - \ket{1} \Big) +\end{aligned}$$ + +The second qubit in state $\ket{-}$ is garbage; it is no longer of interest. +The first qubit is given by: + +$$\begin{aligned} + \frac{1}{\sqrt{2}} \Big( (-1)^{f(0)} \ket{0} + (-1)^{f(1)} \ket{1} \Big) + = \frac{(-1)^{f(0)}}{\sqrt{2}} \Big( \ket{0} + (-1)^{f(0) \oplus f(1)} \ket{1} \Big) +\end{aligned}$$ + +If $f$ is constant, then $f(0) \oplus f(1) = 0$, +meaning this state is $(-1)^{f(0)} \ket{+}$. +On the other hand, if $f$ is balanced, then $f(0) \oplus f(1) = 1$, +meaning this state is $(-1)^{f(0)} \ket{-}$. +Taking the Hadamard transform of this qubit therefore yields: + +$$\begin{aligned} + \to \boxed{H} \to \quad + (-1)^{f(0)} \ket{f(0) \oplus f(1)} +\end{aligned}$$ + +Depending on whether $f$ is constant or balanced, +the mearurement outcome of this state will be $\ket{0}$ or $\ket{1}$ +with 100\% probability. We have solved the problem! + +Note that we only consulted the oracle (i.e. applied $U_f$) once. +A classical computer would need to query it twice, +once with input $x = 0$, and again with $x = 1$. + + +## Full Deutsch-Jozsa algorithm + +The Deutsch-Jozsa algorithm generalizes the above to $N$-bit inputs $x$. +We are promised that $f(x)$ is either constant or balanced; +other possibilities are assumed to be impossible. +This algorithm is then implemented by the following quantum circuit: + +<a href="deutsch-jozsa-circuit.png"> +<img src="deutsch-jozsa-circuit.png" style="width:50%;display:block;margin:auto;"> +</a> + +There are $N$ qubits in initial state $\ket{0}$, and one in $\ket{1}$. +For clarity, the oracle $U_f$ works like so: + +$$\begin{aligned} + \ket{x_1} \ket{x_2} \cdots \ket{x_N} \ket{y} + \quad \to \boxed{U_f} \to \quad + \ket{x_1} \cdots \ket{x_N} \ket{y \oplus f(x_1, ..., x_N)} +\end{aligned}$$ + +Applying the $N + 1$ Hadamard gates to the initial state +yields the following superposition: + +$$\begin{aligned} + \ket{0}^{\otimes N} \ket{1} + \quad \to \boxed{H^{\otimes N + 1}} \to \quad + \ket{+}^{\otimes N} \ket{-} + = \frac{1}{\sqrt{2^N}} \sum_{x = 0}^{2^N - 1} \ket{x} \ket{-} +\end{aligned}$$ + +Where $\ket{x} = \ket{x_1} \cdots \ket{x_N}$ denotes a classical binary state. +For example, if $x = 5 = 2^0 + 2^2$ in the summation, +then $\ket{x} = \ket{1} \ket{0} \ket{1} \ket{0}^{\otimes N-3}$ +(from least to most significant). + +We give this state to the oracle, +and, by the same logic as for the Deutsch algorithm, +get back: + +$$\begin{aligned} + \to \boxed{U_f} \to \quad + \frac{1}{\sqrt{2^N}} \sum_{x = 0}^{2^N - 1} (-1)^{f(x)} \ket{x} \ket{-} +\end{aligned}$$ + +The last qubit $\ket{-}$ is garbage. +Next, applying the Hadamard transform to the other $N$ gives: + +$$\begin{aligned} + \to \boxed{H^{\otimes N}} \to \quad + \frac{1}{\sqrt{2^N}} \sum_{x = 0}^{2^N - 1} (-1)^{f(x)} + \bigg( \frac{1}{\sqrt{2^N}} \sum_{y = 0}^{2^N - 1} (-1)^{x \cdot y} \ket{y} \bigg) +\end{aligned}$$ + +Where $x \cdot y$ is the bitwise dot product of the binary representations of $x$ and $y$, +so, for example, if $N = 2$, then $x \cdot y = x_1 y_1 + x_2 y_2$. +Note that the above expression has not been reduced at all; +it follows from the definition of the Hadamard transform. +We can rewrite it like so: + +$$\begin{aligned} + \frac{1}{2^N} \sum_{x = 0}^{2^N - 1} \sum_{y = 0}^{2^N - 1} (-1)^{f(x) + x \cdot y} \ket{y} + = \sum_{y = 0}^{2^N - 1} \bigg( \frac{1}{2^N} \sum_{x = 0}^{2^N - 1} (-1)^{f(x) + x \cdot y} \bigg) \ket{y} + = \sum_{y = 0}^{2^N - 1} c_y \ket{y} +\end{aligned}$$ + +The parenthesized expression can be interpreted as the coefficients +of a superposition of several $y$-values. +Therefore, the probability that a measurement yields $y = 0$, +i.e. $\ket{y} = \ket{0}^{\otimes N}$, is: + +$$\begin{aligned} + |c_0|^2 + = \bigg| \frac{1}{2^N} \sum_{x = 0}^{2^N - 1} (-1)^{f(x)} \bigg|^2 +\end{aligned}$$ + +The summation always contains an even number of terms, for all values of $N$. +Consequently, if $f$ is constant, then $|c_0|^2 = |\!\pm\! 2^N / 2^N|^2 = 1$. +Otherwise, if $f$ is balanced, all the terms cancel out, so we are left with $|c_0|^2 = 0$. +In other words, we reach the same result as the Deutsch algorithm: +we only need to measure the $N$ qubits once; +$f$ is constant if and only if all are zero. + +The Deutsch-Jozsa algorithm needs only one oracle query to give an error-free result, +whereas a classical computer needs $2^{N-1} + 1$ queries in the worst case; +a revolutionary discovery. + + +## References +1. J.S. Neergaard-Nielsen, + *Quantum information: lectures notes*, + 2021, unpublished. +2. S. Aaronson, + *Introduction to quantum information science: lecture notes*, + 2018, unpublished. diff --git a/content/know/concept/euler-equations/index.pdc b/content/know/concept/euler-equations/index.pdc index 37d2fea..cedfd93 100644 --- a/content/know/concept/euler-equations/index.pdc +++ b/content/know/concept/euler-equations/index.pdc @@ -149,7 +149,7 @@ to which we apply a vector identity: $$\begin{aligned} 0 = \dv{\rho}{t} + \nabla \cdot (\rho \va{v}) - = \dv{\rho}{t} + \va{v} \cdot \nabla \rho + \rho (\nabla \cdot \va{v}) + = \dv{\rho}{t} + (\va{v} \cdot \nabla) \rho + \rho (\nabla \cdot \va{v}) \end{aligned}$$ Thanks to incompressibility, the last term disappears, @@ -159,7 +159,7 @@ $$\begin{aligned} \boxed{ 0 = \frac{\mathrm{D} \rho}{\mathrm{D} t} - = \dv{\rho}{t} + \va{v} \cdot \nabla \rho + = \dv{\rho}{t} + (\va{v} \cdot \nabla) \rho } \end{aligned}$$ diff --git a/content/know/concept/material-derivative/index.pdc b/content/know/concept/material-derivative/index.pdc index af65ca0..1c6bfdc 100644 --- a/content/know/concept/material-derivative/index.pdc +++ b/content/know/concept/material-derivative/index.pdc @@ -57,7 +57,7 @@ then we can rewrite this expression like so: $$\begin{aligned} \dv{t} f\big(x(t), y(t), z(t), t\big) - &= \pdv{f}{t} + \va{v} \cdot \nabla f + &= \pdv{f}{t} + (\va{v} \cdot \nabla) f \end{aligned}$$ Note that $\va{v} = \va{v}(\va{r}, t)$, @@ -73,7 +73,7 @@ and is known as the **material derivative** or **comoving derivative**: $$\begin{aligned} \boxed{ \frac{\mathrm{D}f}{\mathrm{D}t} - \equiv \pdv{f}{t} + \va{v} \cdot \nabla f + \equiv \pdv{f}{t} + (\va{v} \cdot \nabla) f } \end{aligned}$$ @@ -89,14 +89,14 @@ but in fact the definition also works for vector fields $\va{U}(\va{r}, t)$: $$\begin{aligned} \boxed{ \frac{\mathrm{D} \va{U}}{\mathrm{D}t} - \equiv \pdv{f}{t} + \va{v} \cdot \nabla \va{U} + \equiv \pdv{\va{U}}{t} + (\va{v} \cdot \nabla) \va{U} } \end{aligned}$$ Where the advective term is to be evaluated in the following way 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