arXiv:2603.15886v3 Announce Type: replace Abstract: We present PhasorFlow, an open-source Python library for computing on the $S^1$ unit circle. Inputs are encoded as complex phasors $z=e^{i\phi}$ on the $N$-torus ($\mathbb{T}^N$); as computation proceeds through unitary wave-interference gates, global norm is preserved while components drift into $\mathbb{C}^N$, letting algorithms leverage continuous geometric gradients. PhasorFlow makes three contributions. First, we formalize the Phasor Circuit model ($N$ threads, $M$ gates) with a 22-gate library spanning standard-unitary, non-linear, neur
The proliferation of advanced AI research necessitates more efficient and specialized computational models, driving innovation in areas like quantum-inspired or geometric computing.
This development introduces a novel computational paradigm that could enhance the efficiency and capabilities of AI algorithms, particularly those leveraging continuous geometric gradients, potentially leading to breakthroughs in machine learning.
Traditional linear or tensor-based AI computations gain a new 'unit-circle based' alternative, offering distinct advantages for certain types of problems by preserving global norm and enabling geometric gradients.
- · AI researchers and developers
- · Machine learning platforms
- · Open-source software communities
- · Developers reliant solely on classical computational models
- · Legacy AI frameworks with limited adaptability
PhasorFlow could enable more robust and efficient training of AI models, particularly in domains sensitive to geometric properties or requiring 'unitary' computation.
Wider adoption of unit-circle based computing could lead to new classes of AI algorithms and applications previously impractical under traditional computational constraints.
This could spark a broader shift in how numerical methods are applied within AI, potentially influencing future hardware designs optimized for such computations.
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Read at arXiv cs.LG