This illustration depicts the quantum states of two qubits, labeled as qubit 0 and qubit 1, represented on Bloch spheres Quantum Simulation in Google colab.
- Each sphere represents the possible states of a single qubit.
- The arrow on each sphere shows the current state of that qubit.
- Qubit 0 is shown in a superposition state, meaning it’s a combination of both |0⟩ and |1⟩ (the arrow points away from the top/bottom poles).
- Qubit 1 is shown in the |0⟩ state (the arrow points straight up).
This visualization is likely generated from a quantum simulation that calculated the overall state of the system (a “statevector”).
To understand the relevance of simulation, I am demonstrating the Qubits vs classical RAM required to support or match computing.
| Qubits | How much classical RAM Needed |
|---|
| 10 | 16 KB |
| 20 | 16 MB |
| 30 | 16 GB |
| 40 | 16 TB |
| 50 | 16 PB (petabytes!) |
By Now you might have realized why Quantum Simulation is needed
Here i simulated 10 qubits in my Google Colab,
Quantum state simulation is like pretending to run a quantum computer using a regular computer.
It’s a way to figure out exactly what a real quantum computer would do without needing the actual (expensive) machine.
Think of it like this:
Imagine you have a special coin that can be both heads and tails at the same time.
Quantum simulation is using math to describe that special coin perfectly before you flip it – looking at both sides together.
Instead of flipping the coin and just seeing heads OR tails, the simulation shows you:
- How much it’s like heads
- How much it’s like tails
- All the possibilities, all at once
A bit of concept before we deep dive to coding and coding summary:
The True Magic of Quantum Qubits
Superposition:
In the world of classical computing, bits are like those indecisive friends who can only choose between two options—0 or 1. But qubits? Oh, they’re the life of the party! They can be 0, 1, or even both at the same time—talk about multitasking! This means that while classical bits are stuck in their binary ways, qubits can explore a whole universe of possibilities all at once, like a quantum buffet where you can have your cake and eat it too!
Entanglement:
Now, let’s talk about entanglement. Imagine if your best friend and you could communicate telepathically, no matter how far apart you were. That’s what happens with qubits! When they get entangled, they become linked in such a way that changing one qubit instantly affects the other, no matter the distance. It’s like having a cosmic connection that makes long-distance friendships a breeze!
What Is This (Code)?
This code removes the final ‘check’ steps (measurements) from a quantum setup and calculates the full quantum state, called a “statevector.”
Instead of running on a real quantum computer or getting measured results, it uses math to find the exact quantum state after the steps.
Why Use Quantum State Simulation?
Today’s real quantum computers are difficult to build and can be unreliable (noisy).
Simulating quantum setups lets us see and understand the pure quantum behavior without errors or randomness getting in the way.
You can clearly see and understand special quantum tricks like being in multiple states at once (superposition) or how quantum things interact (interference and entanglement).
In short:
Simulation = quick, cheap, and perfect for learning and finding problems in your quantum ideas.
Benefits of This Approach
- See Everything at Once: Get the full picture of all possible results, not just the one you’d get from a single measurement.
- See It Visually: You can draw pictures of the states (like on Bloch spheres) or see the math clearly.
- Understand Better: Helps you really “get” the quantum ideas like superposition and entanglement.
- No Hiding: Measuring forces a quantum state into one result. Here, you explore the state before that happens.
Code Summary
| Step | What It Does |
|---|---|
| Import Tools | Load the helpers needed from Qiskit. |
| Remove Measurements | Get rid of the final ‘check’ steps. |
| Create Statevector | Calculate the full quantum state. |
| Display Statevector | Show the state using math symbols for clarity. |
Export to Sheets
Results Summary
You get a statevector, which is like a list showing the strength of each basic quantum possibility (like |00⟩, |01⟩, etc.).
The output tells you:
- Which quantum states are possible.
- How likely each state is (by squaring the number shown).
- No guessing: It’s the exact math description of your quantum system at that moment.
Example Output (for putting a single qubit in a special state):
21∣0⟩+2
1∣1⟩
This means:
- There’s a 50% chance if you measured, you’d get 0.
- There’s a 50% chance if you measured, you’d get 1.
- The qubit is in a mixed quantum state (superposition) before any check is done.
Code Summary
Real Value of Using Quantum Qubits
Superposition:
- Think of regular bits like a light switch: strictly ON (1) or OFF (0).
- A qubit is like a dimmer switch: it can be ON, OFF, or somewhere in between at the same time.
- This lets a small group of qubits represent and work with many different options all together.
Entanglement:
- Qubits can get mysteriously linked.
- If you look at one linked qubit, it instantly tells you something about the others, no matter how far apart they are.
- This creates a special, coordinated power that regular bits can’t match.
Quantum Interference:
- Quantum effects can make certain outcomes more likely and others less likely.
- This is used in quantum problem-solving to boost the correct answer’s signal while making wrong answers fade away.
Exponential Parallelism:
- Using ‘n’ qubits allows you to handle an incredibly large number of possibilities at once (specifically 2n possibilities).
- A standard computer would need a vast amount of memory and time to do the same thing.
Real-World Needs Where Quantum Can Help
Here are specific areas where qubits can offer real advantages:
| Area | Why Quantum Helps |
|---|---|
| Cryptography | Breaking complex codes: Quantum computers can find the secret numbers used in some modern encryption much, much faster than regular computers. |
| Chemistry and Materials | Designing new stuff: They can figure out how tiny particles (like atoms and molecules) work together, which is key for making new medicines, better batteries, or stronger materials. |
| Optimization Problems | Solving huge planning puzzles: Excellent at finding the very best way to organize really complicated things, like figuring out the most efficient delivery routes or managing busy schedules. |
| Finance | Handling tricky money situations: Useful for understanding risky investments, predicting market behaviors, and managing money where many unpredictable things are happening. |
| Machine Learning | Making AI smarter/faster: Can help artificial intelligence learn and process information more quickly and efficiently. |