⧖Code V1.2-QC Is Live: A New Era Advancing Quantum Computing

Posted on October 7, 2025 | By Nicole Flynn, Symfield PBC

⧖Code: Quantum-Symbolic Resilience for Uncertainty-Preserving Computation (V1.2QC)

⧖Code V1.2-QC introduces a pre-collapse symbolic architecture designed to operate as a field-aligned interface layer for quantum computing systems. Unlike conventional approaches that treat quantum computation as a collapse-based resolution mechanism, ⧖Code repositions quantum devices as relational probes embedded within non-collapse symbolic fields. This release includes: SIV symbolic operator library (Symbolic Identity Vector), P-state modulation layer for tension-preserving θ-phase encoding, H.U.G.E. validation suite (High-Utility Gate Evaluation), Adapters for Qiskit and PyQuil backends, Compatibility with 2–25 qubit systems under NISQ noise, Core recursion logic for symbolic field embedding (∫ψ, ⊘, ⧖). All components are built to support coherence-preserving symbolic computation across recursive cycles of uncertainty. The system demonstrates measurable fidelity enhancements (e.g., +0.315 average) and consistent phase alignment across simulation sweeps. ⧖Code V1.2-QC is part of the Symfield non-collapse computation framework, and this release includes full implementation code, README documentation, and validation datasets for reproducibility and integration into field-aware symbolic platforms. This document is an extension of: Flynn, N. (2025). Tensional Coherence Runtime (⧖): Meet Operator ⧖Code™ - A Revolutionary Non-Collapse Symbolic Computing Framework (V 1.1). Zenodo. https://doi.org/10.5281/zenodo.17274785

Zenodo

Today, we’re thrilled to announce the release of ⧖Code V1.2-QC: Quantum-Symbolic Resilience for Uncertainty-Preserving Computation on Zenodo (DOI: 10.5281/zenodo.17282917). After months of hard work, this open-source framework is ready to shake up quantum computing by turning collapse into a strength, not a setback.

What’s ⧖Code All About?

⧖Code reimagines quantum devices as relational probes within a symbolic field, preserving uncertainty instead of erasing it. This release packs:

• Symbolic Inversion Vectors (SIV): Non-destructive transformations with ~1.0 fidelity.
• P-state Operators: Boosts fidelity from 0.344 to 0.659 (a +0.315 jump at θ≈2.1, 4.2 rad) and holds strong over 5 loops (~0.55).
• Deferred Measurement: Keeps ~50% subspace probability with ancilla, fixing prob skew (0.967→0.5).
• H.U.G.E. Testing: 10,000 trials across 2-25 qubit systems, including a 25-qubit sparse encoding (64 amplitudes in 33.5M dimensions).

We’ve validated it under realistic NISQ noise (p=0.01-0.05), and it’s ready for real hardware like IBM Q, Rigetti, or IonQ.

The Big Wins

Our H.U.G.E. (Hierarchically Uncollapsed Gate Evaluation) tests show a sinusoidal fidelity curve—peaking at 0.659—proving ⧖Code’s phase tuning counters decoherence. The 25-qubit scale screams scalability, and CACE-25 (a multi-AI collab) extends it to dialogue orchestration. It’s open-source (MIT) with code, a 65-page paper, and protocols on GitHub.

Why It Matters

This isn’t just a fix—it’s a new way to compute. Apps in machine learning, chemistry, finance, and more can now harness uncertainty. We’re pending real-hardware validation, but the sims are a green light.

Join the Journey

Test it on your quantum setup, integrate it into Qiskit/Cirq, or explore new domains. Collaborate, validate, and push the boundaries with us. Drop a note at symfield.ai—let’s build the future together!

#QuantumComputing #UncertaintyPreservation #⧖Code #NISQ #OpenSource

© Copyright and Trademark Notice

© 2025 Symfield PBC
Symfield™ and its associated symbolic framework, architectural schema, and symbolic lexicon are protected intellectual property. Reproduction or derivative deployment of its concepts, glyphs, or system design must include proper attribution and adhere to the terms outlined in associated publications.

This research is published by Symfield PBC, a Public Benefit Corporation dedicated to advancing field-coherent intelligence and collaborative AI safety frameworks. The PBC structure ensures that research and development activities balance stakeholder interests with the public benefit mission of creating safe, beneficial AI systems that operate through relational coherence rather than collapse-based architectures.