This notebook explains the quantum processing unit (QPU) programming model used for factoring, and shows how to implement it.

The notebook has the following sections:

1. Factoring as a Constraint Satisfaction Problem describes the factoring problem as an example CSP.
2. Formulating the Problem for a D-Wave Quantum Computer shows how such CSPs can be formulated for solution on a quantum computer.
3. A Simple Example codes a small CSP to clarify the solution technique.
4. Factoring on the Quantum Computer codes a factoring problem for solution on a D-Wave quantum computer.
5. Further Information details some points touched on in previous sections and examines more closely the results returned from the quantum computer.

The complexity class for classical integer factoring is believed to be between P and NP-hard. Although research has yielded algorithms that perform faster than the intuitive trial division, including Fermat's algorithm, Pollard's two algorithms, and sieve algorithms, it's still an open question whether a classical algorithm exists that can factor in polynomial time. For quantum computing, Shor's algorithm runs in polynomial time (D-Wave quantum computers do not run this algorithm).

This notebook solves factoring on a D-Wave quantum computer by formulating it as a constraint satisfaction problem. CSPs require that all a problem's variables be assigned values that result in the satisfying of all constraints. For factoring, the problem's constraints are that the two variables representing factors, a and b, be assigned only natural numbers and that their multiplication be equal to the factored number, P.

You can run this example without installation in cloud-based IDEs that support the Development Containers specification (aka "devcontainers").

For development environments that do not support devcontainers, install requirements:

pip install -r requirements.txt

If you are cloning the repo to your local system, working in a virtual environment is recommended.

Your development environment should be configured to access Leap’s Solvers. You can see information about supported IDEs and authorizing access to your Leap account here.

The notebook can be opened by clicking on the 01-factoring-overview.ipynb file in VS Code-based IDEs.

To run a locally installed notebook:

jupyter notebook

Released under the Apache License 2.0. See LICENSE file.