Qubit-Efficient Encoding Techniques for Solving QUBO Problems

Qubit-Efficient Encoding Techniques for Solving QUBO Problems | Supreeth Mysore Venkatesh

Towards scalable encoding strategies to solve QUBO problems.

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Here’s the gist of the post in a nutshell:

Classical solvers start choking when you push binary‐variable problems into the tens or hundreds of thousands, thanks to a brutal combinatorial blow-up. Quantum Approximate Optimization (QAOA) could help by exploring many solutions at once, but it needs one qubit per variable—way beyond what today’s hardware can handle. Enter qubit-efficient tricks that crush that requirement down to logarithmic qubit counts.

The author walks you through an unsupervised image-segmentation demo: turn pixels into a graph, recast min-cut as a QUBO, then smash it with three Variational Quantum Algorithms—Parametric Gate Encoding, Ancilla Basis Encoding, and Adaptive Cost Encoding. These methods are NISQ-friendly, scalable, and work for pretty much any binary combinatorial optimization you can dream up.