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Quantum Dynamic Programming

Goal

To implement algorithms that demonstrate a quantum speedup on dynamic-programming solutions to certain problems.

Description

The problems that will be taken up are -

  1. Path in the hypercube
  2. Vertex ordering problems
  3. Graph bandwidth
  4. Travelling salesman problem
  5. Feedback arc set
  6. Minimum set cover

The quantum algorithms will precompute solutions for a part of the subsets using DP, and then use Grover's search on the remaining subsets to find the solution to the problem.

Background

Quantum computing is a fairly new and rapidly developing paradigm that harnesses the 'weirdness' of quantum particles to perform computational tasks efficiently. As computer scientists designing these algorithms, we do not need to know why or how the weirdness happens - we just need to know what it is, and think of ways to use it advantageously.

Dynamic programming is a technique used in computer science to solve a certain class of problems that require computing the solutions of subproblems to solve the bigger optimization problem. There are many NP-complete problems that have dynamic programming solutions which take exponential time. The paper we are implementing in this project uses quantum computing to gain a speedup on these classical DP solutions - the results are still exponential time, but with a better complexity (Eg. $O(1.5^{n})$ is better than $O(2^n)$, even though both are exponential complexities).

Plan of Action

  • Week 1 - A few introductory exercises in quantum computing - learn Python, and what qubits and quantum gates are
  • Week 2 - Basic quantum algorithms
  • Week 3 - Pick a problem and understand the classical solution
  • Week 4-7 - Understand the strategies in the paper, and implement the selected algorithm
  • Week 8 - Final presentations

Prerequisites and Techstack

  • Some programming experience, preferably in Python - but you can learn on the way too
  • The QC framework we will use is Qiskit
  • Participants can also pick a different framework of their choice - here is a list
  • Note that a knowledge of quantum mechanics is NOT required

Expectations and Deliverables

  • Clean, readable code
  • A working Jupyter notebook with an implementation of the selected algorithm and an analysis and explanation of the problem, solution, and speedup
  • Enthusiasm!

Mentor

Ayushi Dubal - 2019A7PS0099G

References

https://arxiv.org/abs/1807.05209