Variational Principle

The Variational Principle is a fundamental concept in quantum mechanics that provides an approximate methodology for finding the ground state (lowest energy state) of a quantum system.

In quantum mechanics, the “ground state” refers to the state of a system where it has the lowest possible energy. It’s like a ball sitting in the lowest point of a valley, rather than rolling around on the slopes. Systems naturally gravitate towards their ground states, as they’re the most stable configurations.

The Variational Principle is a mathematical tool that lets us approximate what this ground state might look like without having to solve the full, often very complicated, quantum equations from scratch. The idea is to start with a “guess” at what the ground state could be, and then to tweak this guess to minimize the energy. We can’t easily find the exact ground state for complex systems due to the limitations of both computational power and mathematical techniques, but the Variational Principle allows us to get very close.

Here’s a simplified outline of how it works:

  1. Make an Initial Guess: Use a mathematical function to describe what you think the ground state might look like. This is your “variational wavefunction.”
  2. Compute the Energy: Use quantum mechanics to calculate the energy of the system when it’s in the state described by your variational wavefunction.
  3. Iterate: Adjust the variational wavefunction slightly and re-calculate the energy.
  4. Find the Minimum: Keep adjusting until you find the function that gives the lowest possible energy. This is your approximate ground state.

The genius of the Variational Principle is that it guarantees you’ll at least get an upper bound for the ground state energy. That means if you find a state with a certain energy using this method, you know the ground state energy must be lower than or equal to that value. This makes it an incredibly useful tool for getting good approximations for complex quantum systems.

In terms of practical applications, this is particularly useful for solving real-world problems where knowing the ground state can provide key insights, like material science for superconductors, chemical structures in drug discovery, and even in finance for portfolio optimization.


  • Supply Chain Optimization: For businesses with intricate supply chains, variational algorithms can calculate the most efficient distribution routes, potentially saving millions in logistics costs.
  • Financial Modeling: Companies in finance can use variational methods to optimize portfolio selections in ways that classical computers struggle to achieve in real-time.
  • Drug Discovery: In pharmaceuticals, finding the ground state of a molecule can lead to the discovery of new drugs. Variational algorithms can accelerate this process.

Variational algorithms can be run on today’s noisy intermediate-scale quantum (NISQ) computers, making them relevant not just as a future consideration, but for present-day pilot projects.


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