Sat. Dec 2nd, 2023
Beating Your Opponent with Quantum Rock Paper Scissors

In the classic game of rock paper scissors, one player’s success relies heavily on their opponent’s move.

But what if you could predict your opponent’s choice before they made it?

With the help of quantum computing, you might just be able to do that.

In this blog post, we will explore how quantum computing could potentially revolutionize the game of rock paper scissors by introducing a new element of strategy, allowing players to beat their opponents more consistently.

Read on to learn more about quantum rock paper scissors and how it could change the way we play the game.

Introducing quantum computing

Quantum computing is an exciting and rapidly advancing field of technology that has the potential to revolutionize the way we approach computing problems.

Unlike classical computing, which is based on binary digits (bits) that can only be in one of two states (0 or 1), quantum computing is based on quantum bits (qubits) that can exist in multiple states simultaneously thanks to a phenomenon known as superposition.

This unique property of qubits gives quantum computers an incredible amount of computational power that could be harnessed to solve complex problems that are currently beyond the capabilities of classical computers.

One fascinating application of quantum computing that has recently gained popularity is using it to play games.

Specifically, it has been used to create a quantum version of the classic game Rock Paper Scissors. While this might seem like a simple game, the addition of quantum computing takes it to a whole new level and opens up new strategic possibilities.

In this blog post, we will explore the power of superposition in quantum computing and how it can be used to beat your opponent every time in Quantum Rock Paper Scissors.

Example program

To help explain how quantum computing can be applied to the game of rock paper scissors, let’s take a look at an example program.

In this program, we will be using a quantum algorithm called Grover’s algorithm to increase our chances of winning against our opponent.

First, we need to set up the game of rock paper scissors in a way that can be interpreted by a quantum computer.

We can do this by assigning a binary value to each move: 00 for rock, 01 for paper, and 10 for scissors. Then, we will use qubits to represent our moves and our opponent’s moves.

For this example, let’s say we are playing against someone who always plays rock.

We will set up our moves using qubits like this:

  • Our move: 00⟩
  • Opponent’s move: 00⟩
    Next, we will apply Hadamard gates to both of our qubits to put them in a superposition of all possible moves.
  • This creates a state like this:
  • Our move: ( 00⟩ + 01⟩ + 10⟩)/√3
  • Opponent’s move: 00⟩
    Now, we will apply Grover’s algorithm to find the optimal move that will beat our opponent. This algorithm essentially amplifies the probability of finding the correct solution by repeatedly applying a set of gates to our qubits.

After a few iterations of the algorithm, we will have a new state that looks like this:

  • Our move: ( 01⟩ + 10⟩)/√2
  • Opponent’s move: 00⟩
    We can see that the probability of us choosing either paper or scissors has been increased, and the probability of us choosing rock has decreased. Therefore, we should choose either paper or scissors as our move to have a higher chance of winning.

By using this program and the principles of quantum computing, we can beat our opponent in rock paper scissors with a higher probability than by using traditional strategies. Of course, this is just a simplified example, but it demonstrates the potential of quantum computing in game theory and other fields.


import random

# Define the possible moves
moves = ['Rock', 'Paper', 'Scissors']

# Define the rules of the game
rules = {
    'Rock': 'Scissors',
    'Paper': 'Rock',
    'Scissors': 'Paper'

# Simulate a quantum measurement
def measure(qubit):
    # Randomly determine the measurement outcome
    outcome = random.choice(['0', '1'])
    if outcome == '0':
        return qubit[0]
        return qubit[1]

# Play a round of the game
def play_round(player_move):
    # Generate a random quantum qubit in superposition
    qubit = random.choice([['Rock', 'Paper'], ['Rock', 'Scissors'], ['Paper', 'Scissors']])

    # Measure the quantum qubit to determine the opponent's move
    opponent_move = measure(qubit)

    # Determine the winner
    if player_move == opponent_move:
        result = "It's a tie!"
    elif rules[player_move] == opponent_move:
        result = "You win!"
        result = "You lose!"

    # Print the moves and result
    print(f"Your move: {player_move}")
    print(f"Opponent's move: {opponent_move}")

# Main game loop
while True:
    # Get the player's move
    player_move = input("Enter your move (Rock, Paper, or Scissors): ")

    # Check if the input is valid
    if player_move not in moves:
        print("Invalid move. Please try again.")

    # Play a round of the game

    # Ask if the player wants to play again
    play_again = input("Do you want to play again? (yes/no): ")
    if play_again.lower() != "yes":


Enter your move (Rock, Paper, or Scissors): Rock
Your move: Rock
Opponent's move: Paper
You lose!
Do you want to play again? (yes/no): yes
Enter your move (Rock, Paper, or Scissors): Scissors
Your move: Scissors
Opponent's move: Scissors
It's a tie!
Do you want to play again? (yes/no): yes
Enter your move (Rock, Paper, or Scissors): Paper
Your move: Paper
Opponent's move: Rock
You win!
Do you want to play again? (yes/no): no

By Hari Haran

I'm Aspiring data scientist who want to know about more AI. I'm very keen in learning many sources in AI.

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