Fix mathjax/latex

website/_config.yml

@@ -39,6 +39,9 @@ baseurl:  ""
 
 # Build settings
 markdown: kramdown
+kramdown:
+  math_engine: mathjax
+  syntax_highlighter: rouge
 plugins:
   - jekyll-feed
   - jekyll-latex

website/quantum-computing-primer/03_qubits/index.md

@@ -11,19 +11,32 @@ In classical computing, the fundamental unit of information is the **bit**, whic
 
 $$|\psi\rangle = \alpha|0\rangle + \beta|1\rangle$$
 
-where \(\alpha\) and \(\beta\) are complex numbers representing the probability amplitudes of the qubit being in the \(|0\rangle\) and \(|1\rangle\) states, respectively. The probabilities of measuring the qubit in either state are given by \(|\alpha|^2\) and \(|\beta|^2\), and they must satisfy the normalization condition:
+{% raw %}
+where $\alpha$ and $\beta$ are complex numbers representing the probability 
+amplitudes of the qubit being in the $|0\rangle$ and $|1\rangle$ states, 
+respectively. The probabilities of measuring the qubit in either state are given
+by $|\alpha|^2$ and $|\beta|^2$, and they must satisfy the normalization condition:
+{% endraw %}
 
 $$|\alpha|^2 + |\beta|^2 = 1$$
 
 ## 3.2 Representing Qubits
 
-Qubits can be visualized using the **Bloch sphere**, a geometric representation of the quantum state of a single qubit. The Bloch sphere is a unit sphere where the north and south poles represent the \(|0\rangle\) and \(|1\rangle\) states, respectively. Any point on the surface of the sphere represents a valid quantum state of the qubit.
+Qubits can be visualized using the **Bloch sphere**, a geometric representation 
+of the quantum state of a single qubit. The Bloch sphere is a unit sphere where 
+the north and south poles represent the $|0\rangle$ and $|1\rangle$ states, 
+respectively. Any point on the surface of the sphere represents a valid quantum 
+state of the qubit.
 
-The state of a qubit can also be described using a **state vector** in a two-dimensional complex vector space. For example, the state \(|0\rangle\) is represented as:
+The state of a qubit can also be described using a **state vector** in a 
+two-dimensional complex vector space. For example, the state $|0\rangle$ is 
+represented as:
 
 $$|0\rangle = \begin{pmatrix} 1 \\ 0 \end{pmatrix}$$
 
-and the state \(|1\rangle\) is represented as:
+{% raw %}
+and the state $|1\rangle$ is represented as:
+{% endraw %}
 
 $$|1\rangle = \begin{pmatrix} 0 \\ 1 \end{pmatrix}$$
 
@@ -51,7 +64,9 @@ In this example, we initialize a quantum circuit with one qubit using the qiskit
 
 ### Example: Applying a Hadamard Gate
 
-The Hadamard gate ((H)) is a fundamental quantum gate that puts a qubit into a superposition state. Applying the Hadamard gate to a qubit initially in the (|0\rangle) state results in the state:
+The Hadamard gate ((H)) is a fundamental quantum gate that puts a qubit into a 
+superposition state. Applying the Hadamard gate to a qubit initially in the 
+$|0\rangle$ state results in the state:
 
 $$H|0\rangle = \frac{1}{\sqrt{2}}(|0\rangle + |1\rangle)$$
 
@@ -66,7 +81,9 @@ result = qc.execute_circuit()
 print(result)  
 ```
 
-In this example, the Hadamard gate is applied to the qubit at index 0, and the circuit is executed to obtain the measurement results. The output will show the probabilities of measuring the qubit in the (|0\rangle) and (|1\rangle) states.
+In this example, the Hadamard gate is applied to the qubit at index 0, and the 
+circuit is executed to obtain the measurement results. The output will show the 
+probabilities of measuring the qubit in the $|0\rangle$ and $|1\rangle$ states.
 
 ### Visualizing the Circuit
 

website/quantum-computing-primer/04_quantum_gates/index.md

@@ -36,10 +36,13 @@ print(result)
 
 ## 4.2 Multi-Qubit Gates
 
-Multi-qubit gates operate on two or more qubits, enabling entanglement and more complex quantum operations. Some of the most common multi-qubit gates include:
+Multi-qubit gates operate on two or more qubits, enabling entanglement and more 
+complex quantum operations. Some of the most common multi-qubit gates include:
 
-- **CNOT Gate (Controlled-NOT)**: Flips the target qubit if the control qubit is in the state |1⟩.
-- **Toffoli Gate (CCNOT)**: A controlled-controlled-NOT gate that flips the target qubit if both control qubits are in the state |1⟩.
+- **CNOT Gate (Controlled-NOT)**: Flips the target qubit if the control qubit is 
+in the state $|1\rangle$.
+- **Toffoli Gate (CCNOT)**: A controlled-controlled-NOT gate that flips the 
+target qubit if both control qubits are in the state $|1\rangle$.
 - **SWAP Gate**: Exchanges the states of two qubits.
 
 ### Example: Applying a CNOT Gate

website/quantum-computing-primer/07_quantum_algorithms/index.md

@@ -11,13 +11,20 @@ Quantum algorithms leverage the unique properties of quantum mechanics, such as
 
 ## 7.1 Deutsch-Jozsa Algorithm
 
-The Deutsch-Jozsa algorithm is one of the earliest quantum algorithms that demonstrates the potential of quantum computing. It solves a specific problem exponentially faster than any classical algorithm.
+The Deutsch-Jozsa algorithm is one of the earliest quantum algorithms that 
+demonstrates the potential of quantum computing. It solves a specific problem 
+exponentially faster than any classical algorithm.
 
 ### Problem Statement
-Given a function \( f: \{0,1\}^n \rightarrow \{0,1\} \), determine whether the function is **constant** (returns the same value for all inputs) or **balanced** (returns 0 for half of the inputs and 1 for the other half).
+Given a function $ f: \{0,1\}^n \rightarrow \{0,1\} $, determine whether the 
+function is **constant** (returns the same value for all inputs) or **balanced** 
+(returns 0 for half of the inputs and 1 for the other half).
 
 ### Quantum Solution
-The Deutsch-Jozsa algorithm uses quantum parallelism to evaluate the function over all possible inputs simultaneously. It requires only **one query** to the function, whereas a classical algorithm would need \( 2^{n-1} + 1 \) queries in the worst case.
+The Deutsch-Jozsa algorithm uses quantum parallelism to evaluate the function 
+over all possible inputs simultaneously. It requires only **one query** to the 
+function, whereas a classical algorithm would need $ 2^{n-1} + 1 $ queries in 
+the worst case.
 
 ### Implementation with `qumat`
 Here’s how you can implement the Deutsch-Jozsa algorithm using `qumat`:
@@ -55,10 +62,12 @@ print(result)
 
 ## 7.2 Grover's Algorithm
 
-Grover's algorithm is a quantum search algorithm that can search an unsorted database of \( N \) items in \( O(\sqrt{N}) \) time, compared to \( O(N) \) for classical algorithms.
+Grover's algorithm is a quantum search algorithm that can search an unsorted 
+database of $ N $ items in $ O(\sqrt{N}) $ time, compared to $ O(N) $ for 
+classical algorithms.
 
 ### Problem Statement
-Given an unsorted database of \( N \) items, find a specific item (marked by an oracle) with as few queries as possible.
+Given an unsorted database of $ N $ items, find a specific item (marked by an oracle) with as few queries as possible.
 
 ### Quantum Solution
 Grover's algorithm uses amplitude amplification to increase the probability of measuring the marked item. It consists of two main steps:
@@ -109,7 +118,7 @@ print(result)
 ```
 
 ### Explanation
-- The oracle marks the desired state (e.g., `|110>`).
+- The oracle marks the desired state (e.g., $|110\rangle$).
 - The diffusion operator amplifies the probability of measuring the marked state.
 - After running the algorithm, the marked state will have a higher probability of being measured.
 

website/quantum-computing-primer/10_advanced_topics/index.md

@@ -25,11 +25,11 @@ qc.create_empty_circuit(3)
 
 # Apply the Quantum Fourier Transform
 def apply_qft(qc, n_qubits):  
-for qubit in range(n_qubits):  
-qc.apply_hadamard_gate(qubit)  
-for next_qubit in range(qubit + 1, n_qubits):  
-angle = 2 * 3.14159 / (2 ** (next_qubit - qubit + 1))  
-qc.apply_cu_gate(next_qubit, qubit, angle)
+    for qubit in range(n_qubits):  
+        qc.apply_hadamard_gate(qubit)  
+        for next_qubit in range(qubit + 1, n_qubits):  
+            angle = 2 * 3.14159 / (2 ** (next_qubit - qubit + 1))  
+            qc.apply_cu_gate(next_qubit, qubit, angle)
 
 apply_qft(qc, 3)
 
@@ -56,11 +56,11 @@ qc.create_empty_circuit(3)
 
 # Apply the Quantum Phase Estimation
 def apply_qpe(qc, n_qubits):  
-for qubit in range(n_qubits):  
-qc.apply_hadamard_gate(qubit)  
-# Apply controlled unitary operations (simplified example)  
-qc.apply_cu_gate(1, 0, 3.14159 / 2)  
-qc.apply_cu_gate(2, 1, 3.14159 / 4)  
+    for qubit in range(n_qubits):  
+        qc.apply_hadamard_gate(qubit)  
+        # Apply controlled unitary operations (simplified example)  
+        qc.apply_cu_gate(1, 0, 3.14159 / 2)  
+        qc.apply_cu_gate(2, 1, 3.14159 / 4)  
 # Inverse QFT  
 apply_qft(qc, n_qubits)
 
@@ -89,13 +89,13 @@ qc.create_empty_circuit(2)
 
 # Apply the Quantum Annealing process
 def apply_quantum_annealing(qc, n_qubits):  
-for qubit in range(n_qubits):  
-qc.apply_hadamard_gate(qubit)  
-# Apply a simple Hamiltonian (simplified example)  
-qc.apply_rx_gate(0, 3.14159 / 2)  
-qc.apply_ry_gate(1, 3.14159 / 2)  
-# Measure the qubits  
-qc.execute_circuit()
+    for qubit in range(n_qubits):  
+        qc.apply_hadamard_gate(qubit)  
+        # Apply a simple Hamiltonian (simplified example)  
+        qc.apply_rx_gate(0, 3.14159 / 2)  
+        qc.apply_ry_gate(1, 3.14159 / 2)  
+    # Measure the qubits  
+    qc.execute_circuit()
 
 apply_quantum_annealing(qc, 2)