Entanglement

Entanglement doesn't mean that when you actively force one qubit to change, the other one magically updates in real-time to match it. That is a super common sci-fi misconception (often called "quantum telepathy" or instantaneous communication), but that's not quite how the physics works.

What happens if you modify one qubit?

This is where the distinction matters most. Let's look at two different scenarios:

Scenario A: You measure one qubit (The "Collapse")

If you look at Qubit A and see it collapse into a |0>, Qubit B will instantly collapse into a |0> as well, no matter how far apart they are. This looks like an instantaneous change, but it is actually the extraction of a pre-existing quantum correlation. You cannot use this to send information, because the outcome of your measurement was completely random.

Scenario B: You manipulate one qubit (The "Gate Action")

If you take Qubit A and actively zap it with a laser or a microwave pulse to deliberately change its state from |0> to |1>, Qubit B does not change.

Instead, by forcing a local change on Qubit A without interacting with Qubit B, you actually break the entanglement. Qubit A spins off into its own independent state, and the quantum link between them is destroyed.

While quantum entanglement feels like instantaneous action at a distance, it cannot be used to send data, messages, or signals faster than light.

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Quantum teleportation does not instantly transport physical objects through space like a sci-fi transporter.

Instead, it is a protocol that allows you to move the exact, fragile quantum state of a particle (like an electron's spin or a photon's polarization) from one location to another, without physically moving the particle itself and without sending any quantum information through the air.

Because of the universal speed limit we just talked about, quantum teleportation cannot happen faster than light. It strictly requires both a quantum channel (entangled qubits) and a classical channel (like a standard fiber line) to work.

Imagine Alice wants to teleport the unknown quantum state of a qubit (let's call it Qubit X) to Bob.

• To do this, Alice and Bob must first share a pair of entangled resource qubits (Qubit A goes to Alice, Qubit B goes to Bob).

Here is the exact step-by-step procedure they follow to make the teleportation happen:

1.Entanglement Distribution:Prerequisite.

Alice and Bob are given two qubits (A and B) that are deeply entangled with each other. Bob takes his qubit (B) far away to his lab. Alice holds onto her entangled qubit (A) and the mystery qubit (X) she wants to teleport.

2.The Bell State Measurement:Alice's Lab.

Alice performs a specific, joint quantum measurement on her two qubits (the mystery Qubit X and her entangled Qubit A). This measurement forces the two qubits to entangle with each other.

The Catch: This measurement completely destroys the original quantum state of Qubit X (respecting the No-Cloning Theorem). Alice's measurement yields two standard, classical bits of data (e.g., 00, 01, 10, or 11). It is 2 bits because: When Alice performs her measurement in Step 2, she isn't just looking at the qubits to see if they are 0 or 1. Instead, she performs a specialized quantum operation called a Bell State Measurement. This measurement forces her two independent qubits (the mystery Qubit X and her entangled Qubit A) to merge and choose one of exactly four possible joint quantum configurations (called the four Bell States). Because there are exactly 4 distinct structural outcomes to this measurement, it takes exactly 2 classical bits of information to describe which outcome

3.Classical Transmission:Speed of Light Baseline.

Alice takes those two ordinary classical bits and sends them to Bob using a standard communication channel (like an internet text or a radio signal). This step is bounded by the speed of light.

4.Bob's Transformation:Bob's Lab.

When Alice's measurement occurred in Step 2, Bob's qubit (B) instantly reacted due to entanglement, twisting into a state that is highly related to the original Qubit X. However, it is slightly garbled.

Once Bob receives Alice's two classical bits from Step 3, they act as a "decryption key." Depending on the bits (00, 01, etc.), Bob applies a specific quantum gate (like a Pauli-X or Pauli-Z rotation) to his qubit. This un-garbles the qubit, making it a perfect replica of the original Qubit X state.

บริษัทในไทยที่ใช้ AWS cloud & GPC

มีนิสิตไปฝึกงานอยู่ 

https://nttdata-solutions.com/th/

การประกอบอาชีพในยุค AI

ปัจจุบันมนุษย์ถูกเอไอแย่งบทบาทการวิเคราะห์ไป มนุษย์ทำหน้าที่เพียง

1. คิดโจทย์ให้ AI
2. ตรวจคำตอบของ AI
3. ทำ Governance (กำกับปกตรอง) AI
4. รับผิดชอบผลการใช้AI

การจะตรวจคำตอบและกำกับปกตรอง AI ได้เราต้องมีทักษะ meta-analysis คือวิเคราะห์คุณภาพของกระบวนการวิเคราะห์และผลการวิเคราะห์ของเอไอได้ จึงจำเป็นต้องฝึกทักษะรากฐานของ meta-analysis ก่อนนั่นคือทักษะการคิดวิเคราะห์

Lorem Ipsum

 ลอ-เร็ม อิป-ซัม

คือ dummy text ที่ใช้ในงานออกแบบและจัดหน้าเอกสาร เช่น งานเว็บ หนังสือ หรือสิ่งพิมพ์ เพื่อให้เห็นรูปแบบเลย์เอาต์ก่อนใส่เนื้อหาจริง

แพลตฟอร์มควอนตัมเพื่อการเรียนรู้ ประเมิน และติดตามระดับชาติ

 https://pqc-learn.ncsa.or.th/

Classical vs. Quantum Bits

While the math for the total number of combinations is the same for both classical bits and qubits, how they hold that information is entirely different:

• Classical Bits: A 100-bit classical register can only hold one of those $2^{100}$ combinations at any single point in time (e.g., just the string 01101...).
• Quantum Qubits: Thanks to superposition and entanglement, a 100-qubit system can exist in a mathematical blend of all 2^100 states simultaneously.

1 electron represents 1 qubit.

An electron acts like a tiny, microscopic bar magnet. When you place it inside a magnetic field, its spin orientation can only point in one of two directions:

• State |0> (the ground state): Spin-Down (aligned with the magnetic field, which is its lowest energy state). This state represents a classical bit 0.

• State |1> (the excited state): Spin-Up (pointing against the magnetic field, a higher energy state). This state represents a classical bit 1.

In quantum computing, we map these spatial directions to mathematical states using a tool called the Bloch Sphere. By convention, the Z-axis is chosen as our standard computational baseline (our classical 0 and 1).

Qubit count

When a company says they have a "100-qubit quantum computer," it means they have built a physical chip containing 100 individual quantum bits.

 At the start of a program, these qubits usually sit in a completely unentangled, base state (all zeros).

 As the quantum program runs, the computer uses quantum gates to entangle specific pairs or small groups of qubits to perform calculations.

A 100-qubit quantum computer has the potential to entangle combinations of those 100 qubits to map out a massive computational space (2^{100} states), but it does not mean all 100 are permanently locked in a giant entangled web.

CTF

การแข่งขัน CTF (Capture The Flag) ในบริบทความปลอดภัยทางไซเบอร์ คือเกมจำลองสถานการณ์เพื่อทดสอบและพัฒนาทักษะด้านความปลอดภัยของคอมพิวเตอร์ โดยผู้เข้าแข่งขันจะต้องค้นหา "ธง" (Flag) ที่ซ่อนอยู่ผ่านการแฮ็ก เจาะระบบ หรือแก้โจทย์รูปแบบต่างๆ

QKD vs. PQC

While both protect data against future quantum computers, they approach the problem from completely opposite directions.

Confidence interval (CI)

 The range of values used to estimate a population mean.

Instead of saying:

“The true mean is exactly 50”

statistics says:

“The true mean is probably between 47 and 53.”

That range is the confidence interval.

Central limit theorem (CLT)

It states that the distribution of sample means approximates a Gaussian distribution (normal distribution) as the sample size grows, regardless of the population's original distribution. This is crucial for making inferences about populations based on sample data.

Understanding the CLT can greatly enhance your data analysis skills, providing a solid foundation for hypothesis testing and confidence interval estimation. However, it does have some limitations:

• Sample Size: The CLT requires a sufficiently large sample size (>=30) to be effective. Small samples may not produce accurate results.Independence: The samples must be independent. Dependencies among data points can skew results.
• Identical Distribution: Samples must come from the same distribution. Note: This applies to the classical CLT (Lindeberg-Levy), but newer versions like Lyapunov or Lindeberg-Feller relax this condition.
• Identical Distribution: Samples must come from the same distribution. Note: This applies to the classical CLT (Lindeberg-Levy), but newer versions like Lyapunov or Lindeberg-Feller relax this condition.

The left graph: x-axis represents the actual values (e.g. incomes) of observations in the population. 

y-axis representins probability density or relative probability/frequency.

The right graph is a “distribution of averages,” not a distribution of raw data. X-axis represents the sample mean computed from a sample of size n (out of population size N where n << N). For example:

Step 1: Start with a population

Suppose the population values are:

$$2,\ 4,\ 6,\ 8,\ 10$$

This population may have any shape.

---

Step 2: Take many samples

Take samples of size $n=2$.

Example samples:

• Sample A: $(2,4)$
• Sample B: $(4,10)$
• Sample C: $(6,8)$
• Sample D: $(2,10)$

---

Step 3: Compute a mean for each sample

Each sample produces ONE sample mean:

Sample	Mean
(2,4)	3
(4,10)	7
(6,8)	7
(2,10)	6

So now we have many values of:

$$\bar{x}$$

namely:

$$3,\ 7,\ 7,\ 6,\dots$$

---

Step 4: Plot all those means

The right graph plots the frequencies/probabilities of these sample means.

So the x-axis contains many possible values of:

$$\bar{x}$$

because different samples produce different averages.

คำถามสัมภาษณ์ผู้สมัครเรียนป.เอก

• แนะนำตัวด้านการศึกษาและประสบการณ์ทำงานด้านการศึกษาและประสบการณ์ทำงาน
• ทำไมสนใจเรียนต่อระดับปริญญาเอก จะทำอะไรหลังเรียนจบ
• ถามทำไมสนใจเรียนที่นี่
• ภาระส่วนตัวเยอะไหมเพื่อประเมินความเสี่ยง
• ความเข้าใจในขั้นตอนกระบวนการเรียนต่อปริญญาเอก

Bipartite cache

A bipartite cache is represented as a bipartite graph (aka bigraph), a graph with two disjoint sets of vertices (disjoint set is a set whose no member is shared with other sets) such that every edge connects a vertex in the first set to another vertex in the other set and there are no two vertices in the same set are connected to each other.

Namely proceedings can actually be a journal

Proceedings of the ACM on Measurement and Analysis of Computing Systems (https://www.scimagojr.com/journalsearch.php?q=21101048533&tip=sid&clean=0)

Softmax function

Used in MLP for multiclass classification.

#output nodes = #classes

The output node with highest probability is a returned class. 

https://adeveloperdiary.com/data-science/deep-learning/neural-network-with-softmax-in-python/

Large Multimodal Model (LMM)

A Large Multimodal Model (LMM) represents the next evolution of AI beyond text-only Large Language Models (LLMs). While a traditional LLM is like a brilliant scholar who has only ever read books, an LMM is like that same scholar who can now also see, hear, and create.

At its core, an LMM is a single AI system capable of processing and generating information across multiple "modalities"—such as text, images, audio, and video—all within a unified framework.

CNN vs LSTM

CNNs are capable of extracting spatial correlations among independent variables, while LSTM excels at capturing temporal correlations within input sequences. (https://dl.acm.org/doi/10.1145/3690771.3690774)

The input to CNN must be arranged into 2D array or "feature map" with the shape (Time Steps × Features). Following the input layer is filter (aka kernel, sliding window). With a filter size of 3, this filter looks at 3 time steps and across the features simultaneously. By doing this, the CNN captures how the 12 independent variables interact with each other within a small local window. The input may be 245 time steps and 12 features (i.e., WxL), the first Conv layer may output 245 time steps, 10 features, 32 filters (WxLxDepth), the second Conv layer may output 245 time steps, 8 features, 1 filter. This reduction means the CNN has successfully compressed the 12 feaures into a single optimized "8 feature representation" that carries the most significant spatial information. So the CNN helps "distill" the most important spatial correlations before passing them to the LSTM.

The input to LSTM can have multiple layer as inside an LSTM cell, there are "gates" (Forget, Input, and Output gates). Each gate is essentially a small neural network.