Buzinga

Inspiration We were inspired by the challenge of implementing quantum algorithms on near-term quantum devices. The ambition was to develop a proof-of-concept that demonstrated key ideas from Shor's algorithm—particularly modular exponentiation—using a simplified yet genuine quantum arithmetic circuit.

What it does This project implements a simplified version of Shor’s algorithm that factors small composite numbers (e.g., semiprime 143). It leverages quantum phase estimation and a basic quantum modular multiplication unitary to illustrate the core quantum subroutines behind the factorization process.

How we built it The project was built in Python using QuantumRingsLib along with Qiskit components for constructing and simulating quantum circuits. We developed a custom modular multiplication gate that maps computational basis states to their modular product, and integrated it into the circuit as a controlled operation. Classical post-processing, including continued fractions for period extraction, completes the factorization process.

Challenges we ran into Some challenges included:

Adapting deprecated library modules and handling API incompatibilities (e.g., updating UnitaryGate imports).

Overcoming limitations of QuantumRingsLib's qubit addressing, which required custom handling rather than standard Python slice operations.

Balancing the need for a genuine quantum subroutine while keeping the implementation simple enough for demonstration purposes.

Accomplishments that we're proud of We are proud to have successfully implemented a nontrivial quantum modular multiplication unitary, integrated it into a full quantum circuit, and effectively demonstrated the separation between counting and working registers. This achievement shows a promising step toward illustrating quantum advantage in a tangible way on current quantum devices.

What we learned Throughout this project, we deepened our understanding of quantum circuit design, especially regarding modular arithmetic and phase estimation. We gained practical experience in troubleshooting API and compatibility issues, and we improved our ability to translate theoretical quantum algorithms into executable code on modern quantum computing platforms.

What's next for Buzinga Looking forward, we plan to:

Optimize and extend our quantum arithmetic circuits to handle larger and more complex instances.

Explore error mitigation techniques and performance enhancements on real quantum hardware.

Investigate additional quantum algorithms that could benefit from similar modular quantum arithmetic subroutines, pushing the boundaries of what can be demonstrated with near-term devices.