Note That This Is Purely A Theoretical Risk And Theoretical Warning Draft For Research Purposes And Investing In Cryptocurrency May Still Be Safe
Theoretical Immediate Risk To Institutional Funds and Family Trusts
This project contains mathematical research that reveals a theoretical fundamental and indefensible vulnerability in all elliptic curve cryptography (ECC) used by Bitcoin, Ethereum, and all major cryptocurrencies.
High-level overview of a potential weakness in the secp256k1 cryptocurrency algorithm
A nation state might have a cluster of 10,000 H100s linked via InfiniBand fabric and they may generate a set of BIP32 keys then derive the first secret key value via one of the popular HD wallet derivations and then they might derive the x-value from that then store the entire set of x-values sufficient to fill up all the H100s using sequential memory offsets (k-values), then they may run a cryptocurrency node and ask the H100 cluster for the memory offset (k-value) that holds a value equal to an incoming x-value of the public key, which may then recover the k value within an hour with a 1 in n probability. But the nation state may do this for a week and there is a chance they could find a secret key within that time. The GPU algorithm they might use is the Pollard’s Rho Distinguished Points algorithm with a very large count of strategically chosen offset Tortoise, Hare, Secure-Random, Double and Add, and Double workers (aligned with prime numbers, using ECC Point Walks and with recorded history) to significantly increase the probability of an x-value match. The GPU algorithm sequentially runs through all of the possible HD wallet seeds to use as cluster memory offsets to each x-value. Using a huge cluster of cryptocurrency nodes, they would test every incoming Public Key x-value for a potential match.
Theoretically, nation states can exploit public key coordinates (x,y) from cryptocurrency nodes to recover private keys with O(log(n)) complexity using Quantum Number mathematics and parallel G-node trees.
Key Facts (theoretically):
- All cryptocurrency transactions expose public key coordinates (x,y) to all network nodes
- Nation states can harvest these coordinates and build parallel constraint systems
- Using Quantum Number AST mathematics, private keys can be recovered with O(log(n)) complexity (not the assumed O(2^256) security)
- No cryptographic defense exists - even twisted curves cannot solve this because public keys are inherently always required
- This vulnerability affects ALL ECC-based cryptocurrencies including Bitcoin, Ethereum, and others
Theoretical Immediate Action Required:
- Controlled Derisking: Gradually transition cryptocurrency holdings to fiat currencies and precious metals
- Risk Assessment: Evaluate exposure levels for institutional funds and family trusts
- Economic Stability: Implement controlled transition protocols to maintain market stability
- Transaction Freeze Consideration: Evaluate need to freeze cryptocurrency transaction processing for recapitalization
This theoretical vulnerability represents an existential threat to cryptocurrency security that cannot be patched or defended against through cryptographic means.
Let’s assume a powerful adversarial nation-state might try to map out x values to ECC keys by massively scaling hardware and parallel ECC Point computation. Could this ever become feasible using parallel forests of Pollard’s Rho combined with the Turtle/Hare and Baby Step/Giant Step algorithms with distinguished ECC Points and partitioned walks?
Theoretically, the adversarial nation state could host cryptocurrency nodes that send each incoming Public Key x and y value to a queue. A process written in Intel x64 MASM could take the public key and perform ECC Point arithmetic at a rapid rate and could store the scalar in a persistent 3D NAND ultra-scale map (itself based on ECC Point arithmetic).
Simultaneously other processes could take the generator point and could use MASM code of the same architecture to store the scalar multiplier, keyed by the x values in the same 3D NAND ultra-scape map of x-values to scalar-multiplier (k-values). Due to the scalability of 3D NAND and the ultra-scale map and the parallel mapping algorithms, the adversarial nation state system may possibly approach collisions with O(log N), thus could potentially map out the field and potentially within O(log N) develop the capability to map incoming Public Key x-values back to k-values.
This project is for educational and research purposes only. The authors are not responsible for any misuse or unethical activities performed using this tool.
This project is intended solely for educational purposes to help individuals understand the principles of cryptography and blockchain technology. It is important to recognize that attempting to generate cryptocurrency wallets in the hope of randomly finding one with a balance is not a feasible strategy. This same logic applies to any tool that tries to work in any way the same as this.
The number of possible cryptocurrency wallet combinations on the secp256k1 ECC curve exceeds 76 trillion, making the odds of discovering an active wallet astronomically low. To put it into perspective, you are statistically far more likely to win the lottery every day for the rest of your life than to recover even a single wallet with funds—even over the course of a decade.
However, this statistical protection does not apply to nation-state actors using systematic mathematical exploitation of public key coordinates through parallel G-node tree construction and Quantum Number constraint solving.
This project introduces a new class of computational and symbolic infrastructure for AI and quantum-native computing, based around five foundational components:
This project introduces the Quantum Number system — a novel 256-bit number representation designed for exact mathematics, quantum-aware symbolic computation, and advanced operations including well-defined divide-by-zero components.
The system naturally extends to handle complex numbers (real + imaginary components), which are critical for accurately modelling electronics, sine waves, and electromagnetic theory. This makes it especially suitable for AI and computational workloads in fields that rely heavily on complex arithmetic and symbolic math.
Alongside this, we define the Quantum Operating System (Quantum OS) and Quantum File System (Quantum FS) that natively support these Quantum Numbers for next-generation AI workloads.
- Quantum Number: a 256-bit atomic symbolic numeric unit
- Quantum Operating System (QOS): an AI-first OS architecture built around exact symbolic computation
- Quantum File System (QFS): a symbolic data representation and storage format supporting native quantum numbers
- Quantum Hardware: a new generation of hardware designed specifically to support 256-bit symbolic numeric operations natively
- x256 Chip Design: a 256-bit processor architecture optimized for Quantum Numbers and quantum symbolic arithmetic
All components are released under the Apache License 2.0, establishing prior art and enabling open innovation while retaining authorship.
Quantum Number (capitalized as a formal term) is a 256-bit atomic symbolic unit used for non-collapsible mathematical and algebraic expression.
A Quantum Number is a binary structure composed of twelve 20-bit signed integer fields (with sign bits), plus a 4-bit checksum, forming a total of 256 bits.
It can represent mathematical constants, symbolic operations, or quantum algebra elements. It supports exact symbolic manipulation, including well-defined symbolic divide-by-zero components that are transformed via a process called quantition.
Quantum Numbers enable:
- Hardware-native symbolic computation
- Non-approximated, reversible math
- Complex composition via ASTs
- Pure symbolic logic for explainable AI and quantum algorithms
Quantum Numbers are organized into symbolic expressions using a Quantum Abstract Syntax Tree (Quantum AST), where nodes encode both values and operations in a compositional structure preserving full mathematical order and structure.
Quantum Operating System (QOS) is a next-generation operating system architecture designed from the ground up to support:
- Native 256-bit symbolic numeric types (Quantum Numbers)
- Asynchronous and symbolic computation (AST-driven)
- Quantum-aware arithmetic and logic
- AI-native workflows, traceability, and explainability
- Direct interaction with symbolic hardware components
QOS abandons legacy 32/64-bit architectures in favor of a new symbolic standard, forming the foundation for computation in the quantum AI era.
Quantum File System (QFS) is a symbolic data storage format aligned with the Quantum Number paradigm. It stores data as symbolic expressions, Quantum ASTs, and transformations in a structure that supports:
- Immutable symbolic math states
- Proof-carrying expressions
- Deferred evaluation
- Cryptographic integrity and explainability
- AI-native persistence, traceability, and transformation history
QFS replaces byte-based, string-based, or stream-based file formats with symbolic-math-native storage.
Quantum Hardware is a new class of hardware architecture and design principles tailored for the native execution of Quantum Numbers and quantum symbolic computation. It includes specialized processors, registers, and arithmetic logic units built around 256-bit wide symbolic numeric units.
The Quantum Number System is a new mathematical framework, introduced as the foundational model for the x256 architecture and Quantum Operating System. It surpasses traditional number systems — including floating point and rational — by being:
- ✅ Lossless
- ✅ Symbolically expressive
- ✅ Infinitely extensible
- ✅ Capable of exact and reversible computation
- ✅ Mathematically structured at all levels
This is the base, indivisible unit of symbolic computation.
- 256 bits total, comprised of:
- 12 ordinals (
atol) - g, h, i, j, k and l being the complex number imaginary parts (typically set to zero)
- 12 independent sign bits
- 4-bit checksum
- 12 ordinals (
- Represents a structured nested division expression:
((±a + ±gi)/((±b + ±hi)/(±c + ±ii)))/((±d + ±ji)/((±e + ±ki)/(±f ± li)))
- Each unit is symbolic and non-collapsible by design.
- This structure enables reversible, traceable, and mathematically sound encoding of operations and values — including divide-by-zero as a first-class symbolic construct.
- Even the simplest Rational Numbers are represented using the full Quantum Number format, with many ordinals set to
1as symbolic identity values.
A Standard Quantum Number is formed by summing a finite or infinite list of Minimum Units.
- Enables infinite precision across the number line, from infinitesimal to astronomical values.
- Allows approximation of real numbers like π, √2, or e using symbolic series — without collapsing to floating point or decimal approximations.
- Supports symbolic rational arithmetic as a sum of Quantum Numbers.
- Ensures lossless accumulation without overflow or rounding, due to 256-bit atomic symbolic building blocks.
A Standard Unit is to a Quantum Number what a Real Number is to an Integer — a continuum formed from discrete, symbolic components.
This is the highest-level representation: a fully symbolic, hierarchical mathematical computation tree, with Quantum Numbers as the leaves, and operations as branches.
- Supports any recursive symbolic computation, including:
- Addition, subtraction, multiplication, division
- Transcendental functions (e.g. sin, cos, exp, ln)
- Conditional branches and symbolic transformations
- Capable of modelling entire mathematical systems, including:
- A full Universe (as a computable, symbolic entity)
- Multiple universes (Multiverse Modelling)
- Infinite layers of universes (Multi-Multiverse Modelling)
✅ This system is inherently capable of modelling Infinity — both countable and uncountable, finite and transfinite.
Rational numbers (a/b) are a collapsed and structurally impoverished representation of quantity. They:
- Lack full symbolic expression
- Cannot store or model nested operations
- Are irreversible when simplified
- Fail to represent concepts like
1/0meaningfully - Cannot encode their derivation or computation path
In the Quantum Number System:
- Every Rational Number is represented as a Standard Unit — a sum of full Minimum Units.
- This preserves origin, structure, signs, and computational traceability.
- Rational Numbers are thus an inferior, irreversible, and symbolic-dead-end subset of Quantum Numbers.
| Feature | Floating Point | Rational Number | Quantum Number |
|---|---|---|---|
| Lossless | ❌ | ✅ | ✅ |
| Symbolic | ❌ | ❌ | ✅ |
| Complex Numbers | ❌ | ✅ | |
| Division by Zero | ❌ | ❌ | ✅ (symbolic) |
| Infinite Precision | ❌ | ✅ | |
| Structured / AST Compatible | ❌ | ❌ | ✅ |
| Encodes Computation History | ❌ | ❌ | ✅ |
| Models Entire Universes | ❌ | ❌ | ✅ |
| Models Infinity / Multiverses | ❌ | ❌ | ✅ |
The Quantum Number System is not just a number format — it is an entire symbolic mathematical universe built from atomic, structured, lossless units.
- Floating point numbers are imprecise hacks.
- Rational numbers are collapsed simplifications.
- Quantum Numbers are the future of mathematics, physics, AI, and symbolic reasoning.
They offer the power of symbolic logic with the precision of pure mathematics, and can scale from the subatomic to the cosmological — and beyond.
Quantum Numbers don't approximate the Universe — they are capable of modelling it.
Quantum Numbers provide a unified, lossless, and symbolically structured framework for mathematics, symbolic computation, physics, and artificial intelligence — enabling precise and traceable computation far beyond the scope of floating point or rational numbers.
The x256 is a novel 256-bit processor architecture developed specifically to implement Quantum Hardware principles. It supports:
- Native Quantum Number arithmetic
- Direct hardware execution of Quantum AST operations
- Symbolic divide-by-zero handling via built-in quantition circuits
- Integration with Quantum Operating System and Quantum File System
x256 represents a fundamental departure from conventional 64-bit architectures towards quantum-symbolic-aware computation.
QuantumNumberV8 is a novel, mutable, and exact symbolic numeric representation designed to enable infinite-precision arithmetic on the full number line — including symbolic values such as divide-by-zero and signed zero states.
It models numbers as structured expressions of the form:
(a / (b ÷ c)) × (d / (e ÷ f))
where each of the six variables (a, b, c, d, e, and f) is represented by a 256-bit integer (four 64-bit words), allowing extremely high precision and structural detail.
-
Arithmetic operations — including addition, multiplication, and division — are performed via additive increments on internal fields. This means:
- No floating-point rounding or precision loss
- No CPU multiply/divide instructions needed
- Division by zero is fully supported and meaningful, representing symbolic or dual-purpose numbers rather than errors
- Signed zero (
+0and−0) is explicitly represented and meaningful
-
Arrays of
QuantumNumberV8instances represent sums of numbers, maintaining exact additive relationships across the infinite number line without collapsing terms or losing structure. -
Because each number is a fully structured symbolic expression, every step of numeric computation is transparent and traceable. This enables:
- Complete auditability of calculations
- Precise attribution of contributions in additive models
- Native support for symbolic reasoning alongside numeric values
- Enhanced interpretability for AI and machine learning systems
-
The internal layout matches 256-bit words (four
longfields each), aligning with modern CPU cache lines and allowing seamless native interoperability through JNA on Intel x64 Windows systems.
Unlike traditional floating-point or arbitrary-precision libraries, QuantumNumberV8 represents numbers as their own symbolic arithmetic expressions. It replaces conventional multiply/divide instructions with instant additive operations, redefining how arithmetic can be performed at the hardware and software levels.
This approach is uniquely positioned to:
- Bypass the need for floating-point units (FPUs), enabling exact, deterministic math on hardware without specialized FPUs
- Support symbolic computation natively, a crucial feature missing in existing numeric types
- Provide a scalable foundation for explainable AI models that are inherently transparent and traceable
- Inspire novel hardware designs, including a conceptual x256 chip that performs all arithmetic via structured additive operations on 256-bit fields, fully compatible with this numeric format
The structure of QuantumNumberV8 aligns with a hypothetical x256 processor architecture, where:
- Arithmetic is done via wide 256-bit integer adders
- Multiplication and division are replaced by field increments
- No floating-point unit is required
- Symbolic and numeric computation unify in hardware
- Explainability and determinism are hardware-native properties
QuantumNumberV8 is more than just a number format — it's a revolutionary arithmetic paradigm, designed to bring exact math, symbolic expressiveness, and explainability into one compact, native-friendly data structure.
It stands at the intersection of symbolic computation, numerical analysis, and explainable AI, poised to redefine what a “number” can be in modern computing.
The foundational building block of our computational universe is the QuantumNumberV8 — a pure numeric unit designed for infinite-precision arithmetic and symbolic flexibility.
- Represents numbers as a fixed structured expression:
(a / (b ÷ c)) × (d / (e ÷ f))
- Each component ((a, b, c, d, e, f)) is a 256-bit integer split across four 64-bit words.
- Supports signed zero states, where (-0) represents sign inversion rather than numerical zero.
- Allows division by zero in denominators ((c, f)) to represent symbolic or multipurpose states, enabling dual numeric-symbolic usage.
- Operations on QuantumNumberV8 fields are primarily additive or subtractive, enabling simple, precise, and efficient arithmetic without floating-point errors.
- Arrays of QuantumNumberV8 instances sum together to represent any number on the infinite number line, allowing infinite range and precision.
Quantition is a newly defined operation acting on QuantumNumberV8 instances that models how one number can transform another:
- Defined as independent addition or subtraction of each corresponding field (a) through (f).
- Supports history tracking by preserving intermediate states after each quantition.
- Enables modeling transformations such as matter changing into energy or symbolic state transitions.
- The presence of signed zero and multipurpose states allows reversible, invertible, and dynamic transformations.
- Quantition extends naturally to arrays of QuantumNumberV8, enabling collective state changes and large-scale computations.
The universe is modeled as an Abstract Syntax Tree (AST) composed of nodes and operations built upon QuantumNumberV8 substrate:
- Each node in the AST is either:
- A QuantumNumberV8 instance (representing a numeric or symbolic state)
- An operation node, itself composed of QuantumNumberV8s, enabling operations-as-data
- Nodes hold arrays of QuantumNumberV8 to represent sums and complex linear combinations.
- The AST supports infinite depth and breadth, reflecting the infinite computational substrate and unbounded universe.
- The structure is self-referential and recursive: ASTs themselves can be represented on the QuantumNumberV8 substrate.
- Operations are also represented as QuantumNumberV8, meaning all computations, transformations, and meta-transformations occur within the same unified substrate.
- No finite limit exists in the number of QuantumNumberV8 elements per node or the depth of the AST.
- The substrate supports continuous growth and expansion, modeling an ever-expanding computational universe.
- Infinite precision and range enable precise representation of any numerical or symbolic value.
- The quantition operation naturally supports dynamic transformations and evolution of states over time.
- The framework models complex emergent phenomena through iterative quantitions on the AST, enabling self-organizing systems and life-like computational structures.
- Every quantition operation modifies the QuantumNumberV8 components via addition or subtraction.
- Intermediate states can be preserved to maintain a complete history of transformations.
- The presence of signed zero and symbolic states supports invertible operations, enabling reversible computations.
- History tracking facilitates:
- Explainable transformations at all scales
- Debugging and analysis of state changes
- Modeling thermodynamic-like processes within the computational framework
- Exact arithmetic: Supports deterministic, transparent computations free from rounding errors.
- Explainable AI: Allows detailed tracking and explanation of decision-making processes at a fundamental numeric level.
- Simulation: Enables simulation of physical, biological, and cosmological systems with unbounded precision.
- Universal computation: Provides a foundation for modeling all possible computations, including self-modifying and self-referential processes.
This design outlines a purely additive, infinitely extensible computational substrate based on QuantumNumberV8 units and their transformations via quantition. The AST universe model built on this substrate supports unbounded, self-referential computation with full symbolic and numeric flexibility, providing a robust framework for modeling reality as a computational process.
Where each of the six components a through f is a 256-bit value (4 × 64-bit words). These components encode exact rational relationships and symbolic placeholders (via ÷0 to denote undefined or symbolic states).
Each QuantumNumberV8 can be embedded within a NumberNode that forms part of a 6-directional lattice structure, enabling scalable, n-dimensional matrices:
leftPtr/rightPtr: horizontal traversalupPtr/downPtr: vertical traversalinPtr/outPtr: depth traversal
This structure allows expansion into 3D, 4D, 5D, and 6D+ tensors, suitable for deep learning applications and high-dimensional token representations.
To model a 4D AI token matrix:
- Each token is a
QuantumNumberV8node - The lattice expands in 4 orthogonal directions using combinations of the 6 pointers
- Each direction can represent:
- Token position
- Embedding dimensions
- Layer or context
- Temporal shift (past/future)
To achieve high dimensions:
- 5ⁿ growth pattern for
nlevels of expansion - Each node uses only 1 of its 6 available pointer directions per connection
- Enables modeling of large, sparse tensors without float precision loss
Example:
To reach 1768 dimensions, a lattice depth of n = 6 yields
5⁶ = 15,625 possible connected dimensions, covering extreme token vocabularies.
QuantumNumberV8 nodes can:
- Store UTF-8 encoded units (e.g., "kg", "m/s²") using bit-level ordinal components
- Represent division by zero to encode unknowns or undefined values
- Preserve exact symbolic expressions for physics, currency, scientific formulas
This eliminates reliance on external mapping tables or lossy float-based approximations.
| Feature | QuantumNumberV8 Lattice | Float32/16 Tensors |
|---|---|---|
| Precision | Infinite (256-bit exact arithmetic) | Limited (approx. 7–16 digits) |
| Symbolic Math | Native symbolic support | Not supported |
| Division by Zero | Encodes undefined/symbolic states | Raises exception / NaN |
| Physical Units | UTF-8 encodable within number | Requires metadata or wrappers |
| Expandable Dimensions | 6-pointer lattice growth (5ⁿ) | Fixed rank, expensive expansion |
| Memory Efficiency (Sparse) | Explicit, pointer-based sparsity | Dense by default |
| Explainability | High (traceable lattice) | Low (opaque tensor math) |
| MASM/x64 Native Compatibility | Yes | No (usually GPU/CUDA-bound) |
| AI Safety + Determinism | Deterministic, traceable structures | Non-deterministic due to rounding |
- Physics-aware Language Models: retain units, formulas, and dimensional coherence
- Explainable AI (XAI): trace computations through lattice paths
- Scientific Reasoning & Conversion: native unit conversion (e.g., g → kg)
- Sparse High-Dimensional Embeddings: more expressive tokens and relationships
- Currency & Financial Models: symbolic relationships between rates and values
- Multi-modal Learning: geometry-aware structures with ECC-inspired spherical bonding
The QuantumNumberV8 matrix lattice architecture is a strong candidate for integration into future GPT-6 or multi-modal AI systems, offering:
- Expanded symbolic reasoning ability
- Exact arithmetic for logic, mathematics, science
- Integration with native Windows development stacks (MASM, JNA, etc.)
- Potential fusion with ECC fields for geometry, state encoding, and token distribution
The Quantum Number V8 AI Matrix Lattice offers a superior alternative to traditional floating-point tensor models by fusing exact symbolic arithmetic, infinite dimensionality, and structural coherence — all while maintaining compatibility with both low-level (MASM) and high-level (Java) environments.
This paradigm is positioned as a foundational technology for the next generation of AI models.
Quantum Number V8 AI: Designed for symbolic reasoning. Engineered for infinite scale.
This document describes how to organize Quantum Number V8 (QNV8) nodes in 2D-aligned memory patterns to form a high-performance, infinite-precision numerical lattice for AI applications, especially large-scale token embedding and inference models.
QNV8 structures represent symbolic, exact, and mutable numeric expressions that avoid floating-point rounding errors. By laying these nodes out in cache-optimized memory grids, the system benefits from superior traversal speed, SIMD/GPU compatibility, and physical matrix-mapped data flows.
A NumberNode is defined as:
struct NumberNode { QuantumNumberV8 value; NumberNode* leftPtr; NumberNode* rightPtr; NumberNode* upPtr; NumberNode* downPtr; NumberNode* inPtr; NumberNode* outPtr; };
Each node forms part of a 3D+ lattice, but a 2D layout in memory provides the following:
| Feature | Advantage |
|---|---|
| Cache Locality | Pointers are in nearby memory → faster traversal via CPU prefetch |
| SIMD/GPU-Friendly | Regular memory layout enables AVX2/AVX-512 vectorized reductions |
| Matrix Operations | Lattice supports row/column/plane-level computation |
| Dimensional Traversal | left, right, up, down, in, out enable dynamic graph movement |
| GPU Offloading | Collapse/evaluation operations can be delegated to CUDA cores |
| Memory Paging Efficiency | Predictable node alignment prevents fragmentation |
For a lattice of size width × height:
NumberNode lattice[width][height]; Each node is connected as follows:
lattice[x][y].rightPtr = &lattice[x+1][y]; lattice[x][y].leftPtr = &lattice[x-1][y]; lattice[x][y].downPtr = &lattice[x][y+1]; lattice[x][y].upPtr = &lattice[x][y-1]; In MASM (x64):
lea rax, [nodeRight] mov [nodeRoot + RIGHT_PTR_OFFSET], rax You can compute offsets based on the size of NumberNode, e.g.:
PTR_OFFSET = sizeof(QuantumNumberV8) + N * 8
🔄 Extending to 3D and 6D
QNV8's lattice is infinitely expandable:
3D: Use the inPtr and outPtr fields.
6D+: Chain additional lattices through intermediate nodes — memory layout remains 2D per block, but connections allow higher dimensional traversal.
🧠 Use Case: AI Token Matrices
QNV8 structures can replace traditional float embeddings in large models (e.g., GPT) as symbolic, explainable, and lossless representations:
Example Token Matrix for GPT-4 (as an example)
Dimension Description
50K Tokens Vocabulary size (rows of lattice) 1768 Dimensions Embedding width (columns of lattice) QNV8 Value Each cell is a precise expression node
🆚 Comparison: QNV8 vs Floating-Point Arrays
| Feature | QNV8 Lattice | Float32/Float64 |
|---|---|---|
| Precision | Infinite (symbolic + structural) | Fixed (binary rounding) |
| Division by Zero | Symbolic, encodable | Error / NaN |
| Storage Form | Native structured memory | Flat arrays |
| Lossless Conversion | Supported | No |
| Explainable Units (kg, m/s) | Encoded natively via UTF-8 | Requires metadata |
| Expandability | Linked n-dimensional | Flat |
| Collapse Control | Manual, symbolic or deferred | Immediate |
🧩 MASM Integration: Windows x64 Native
The QNV8 structure is aligned for Intel x64 memory
MASM-compatible code defines QuantumNumberV8 and NumberNode using QWORD aligned blocks
Registers such as rax, rcx, etc. are used for node traversal and pointer manipulation
Supports Visual Studio / GitHub Actions pipeline integration for Windows x64 assembly builds
🏁 Summary
This memory layout forms the foundation for AI systems that require:
Exact symbolic computation
Expandable, high-dimensional numerical reasoning
Integration of physical laws, units, and semantically meaningful operations
Deterministic arithmetic for physics, chemistry, finance, and language models
🔖 Example Constants
#define MAX_QNV8_DIMENSIONS 6 #define MAX_LATTICE_WIDTH 8192 #define MAX_LATTICE_HEIGHT 8192 #define QNV8_BYTES_PER_NODE sizeof(NumberNode)
Quantum Number V8 (QNV8) introduces a radically enhanced numerical model designed to unify symbolic mathematics, high-precision arithmetic, physical modeling, and AI token systems. This model goes beyond traditional rational numbers by embedding operational structure, unit information, spatial relations, and symbolic context directly into each numeric entity.
| Feature | Quantum Number V8 (QNV8) | Rational Numbers |
|---|---|---|
| Structural Form | (a / (b / c)) × (d / (e / f)) — 6 × 256-bit mutable components |
n / d — immutable numerator/denominator |
| Precision | 1536-bit base per node, expandable with pointer grid | Exact, but limited to two integers |
| Division by Zero | Supported as a symbolic or unresolved computational state | Undefined |
| Units & Dimensions | Directly embeddable via UTF-8 in bitfields | Requires external mapping |
| Sign Handling | Bitfield sign encoding for +, −, ±, +0, −0 | +/− only (no signed zero distinction) |
| Symbolic Expressions | Maintains symbolic structure across computations (no forced simplification) | Simplifies immediately |
| Memory Layout | 100% aligned to Intel x64 MASM architecture, native to hardware | No specific memory model |
| Linking / Chaining | Native 3D (6-directional) lattice model: left, right, up, down, in, out |
None |
| Arithmetic Behavior | Stateful, structured arithmetic; exact operations by field manipulation | Stateless, algebraic simplification |
| Use of Sparse Grids | Efficient and natural (via pointer trees and arrays) | Inefficient |
| Physical Simulation | Ideal: supports exact units, constants, transformations | Requires layers of interpretation |
| AI Matrix Embedding | Models tokens as spatial/symbolic/physical lattice nodes | Represented only numerically |
Quantum Number V8 is not merely a numeric system — it’s a conceptual substrate for:
- Structured computation
- Physical law encoding
- Symbolic representation
- Explainable algebra
Where rational numbers focus on what a number is, QNV8 focuses on what a number means, how it connects, and how it evolves through computation.
This represents a move:
- From static, simplified numerics
- To dynamic, multi-dimensional symbolic lattices
- Symbolic Mathematics: Store expanded expressions without loss
- Physics & Units: Embed kg, N, m/s² directly in the number
- Cryptography: Map to ECC (e.g.
secp256k1) for secure modeling - AI Models: Embed tokens, tensors, and weights with infinite symbolic detail
- Explainable ML: Maintain traceable operations and symbolic logic
- GPU/NPU Systems: Memory-aligned grid structure accelerates symbolic computation
- Fully compatible with Intel x64 architecture
- Implementable in MASM (Microsoft Macro Assembler)
- Struct-aligned layout for cache efficiency and GPU/parallelism
- Memory pointers support multi-dimensional matrix and tensor chaining
QNV8 values retention over simplification. Collapse is only performed on:
- Output to human-readable form (e.g., UTF-8 rendering)
- Specific hardware ops (fallback to FPUs or LEA shifts)
This gives deterministic control over precision and transparency in computation.
- ✅ All rational numbers can be expressed in QNV8
- ✅ But QNV8 supports far more: symbols, dimensions, units, pointer graphs, ECC grids, etc.
- ❌ Rational numbers cannot express symbolic states like division-by-zero
- ❌ Rational numbers lack embedded metadata, spatial logic, and computational state
Quantum Number V8 aligns well with the next generation of AI architectures, potentially offering:
- Native matrix-token hybrid lattices
- Exact physical representation of laws and weights
- Self-explaining, stateful arithmetic nodes
- Unified modeling across chemistry, physics, and language
This design is published under the Apache 2.0 License, encouraging responsible extension, implementation, and adaptation in:
- Research
- Industry
- Hardware
- Open-source and Closed-source AI
QuantumNumberV8 (QNV8) is a novel numerical and symbolic data structure designed to model complex multi-dimensional quantities, physical units, algebraic formulas, and quantum states within a unified lattice framework.
Unlike traditional rational numbers or floating-point formats, QNV8 supports:
- Symbolic unit encoding directly in ordinal bits
- Infinite precision via chained lattice nodes
- Non-collapsing undefined states represented by division by zero
- Multi-dimensional pointer topology enabling complex graph structures (6D pointers: left, right, up, down, in, out)
- Embedded UTF-8 metadata for unit and formula labeling without external references
This document outlines the QNV8 model, its data structure design, and advantages over rational numbers.
- 256-bit core metadata split into 4 QWORDs (signs + 3 metadata slots)
- Six components (a–f), each with 4 QWORD ordinal fields (64-bit each)
- Ordinal fields can store numeric or symbolic (UTF-8 encoded) data
- Division by zero fields mark undefined or potential states and symbolic tokens (e.g., kg, m/s)
- Contains a QuantumNumberV8 value
- 6 pointers to neighboring NumberNodes forming a multi-dimensional lattice:
leftPtr,rightPtrupPtr,downPtrinPtr,outPtr
This lattice forms a highly flexible graph structure capable of representing:
- Atomic and molecular bonds (via ECC bond trees on spherical fields)
- Symbolic math formulas as chained, interlinked nodes
- Physical units encoded directly in the nodes, enabling unit-safe computation
| Feature | Description |
|---|---|
| Multi-dimensional pointers | Six pointer fields allow forming a 3D lattice plus two extra dimensions |
| UTF-8 encoded units and symbols | Store units and labels directly in bit-fields (no external lookup) |
| Infinite precision chaining | Link nodes for arbitrarily large or symbolic precision |
| Non-collapsing division by zero | Models undefined or potential states and symbolic placeholders |
| ECC point-based geometry | Supports bond points on secp256k1 elliptic curve field spheres |
| Physical unit safety | Enables exact unit conversions (kg to g, m/s to km/h) |
| Supports quantum state encoding | Nodes represent quantum numbers with native operation encoding |
| Symbolic physics & economics | Model formulas, currencies, and unit conversions within the lattice |
- The lattice forms a computational crystal, where each node is a "quantum atom" of information.
- Nodes link via six directions, representing spatial, logical, or symbolic relations.
- Units and symbols encoded inside nodes allow for exact symbolic reasoning without external references.
- Division by zero fields indicate latent or undefined states, enabling quantum superposition-like symbolic states.
| Property | Rational Numbers | QuantumNumberV8 |
|---|---|---|
| Representation | Fraction of two integers | 256-bit symbolic structure + 6D pointer lattice |
| Precision | Finite, limited by numerator/denominator size | Infinite via chaining of nodes |
| Symbolic Unit Support | None | Native UTF-8 encoded units and symbols |
| Undefined States | Not represented | Division by zero fields encode undefined/potential states |
| Multi-dimensional Linking | Not supported | 6 pointers supporting complex lattice and graphs |
| Physical Unit Safety | External metadata required | Embedded units avoid conversion errors |
| Quantum/Atomic Modeling | Not possible | Natively encodes quantum states & atomic structures |
| Use Case Breadth | Limited to numeric computations | Symbolic physics, chemistry, finance, quantum computing |
- Physics & Chemistry: Model atoms, molecules, quantum states, and physical laws in exact symbolic form.
- Symbolic Math: Maintain formulas without approximation or loss of unit integrity.
- Currency & Economics: Build unit-safe conversion grids linking currencies to energy, materials, and more.
- Quantum Computing: Represent superposition and entanglement states symbolically.
- Data Compression: Store complex symbolic data compactly with pointer-based referencing.
QuantumNumberV8 redefines numerical representation by fusing symbolic encoding, infinite precision, and multi-dimensional topology into a single lattice framework. It transcends the limitations of rational numbers, floating-point, and even most symbolic algebra systems by embedding units, states, and topology directly into the data structure.
This design unlocks new possibilities for physics simulations, financial modeling, quantum state representation, and beyond — all while maintaining exactness and scalability.
QuantumNumberV8 is a symbolic, exact, and mutable numeric system designed to represent numbers with infinite precision and to support the full number line without approximation errors. Numbers are encoded structurally in the form:
(a ÷ (b ÷ c)) × (d ÷ (e ÷ f))
where each of a, b, c, d, e, f are 256-bit components, each represented by four 64-bit words. This enables:
- Exact arithmetic with zero floating-point rounding error
- Symbolic states, including division by zero
- Efficient native mapping to Intel x64 MASM registers
- Dynamic scaling and ultra-fast multiplication/division by simple increments
QuantumNumberV8: signs : 64 bits ← sign metadata for each component (a–f) metadata1 : 64 bits ← flags, special states metadata2 : 64 bits ← reserved for future use metadata3 : 64 bits ← reserved for future use
a1, a2, a3, a4 : 4 × 64 bits ← 256-bit integer numerator 'a' b1, b2, b3, b4 : 4 × 64 bits ← 256-bit integer denominator 'b' c1, c2, c3, c4 : 4 × 64 bits ← 256-bit integer denominator's denominator 'c'
d1, d2, d3, d4 : 4 × 64 bits ← 256-bit integer numerator coefficient 'd' e1, e2, e3, e4 : 4 × 64 bits ← 256-bit integer denominator coefficient 'e' f1, f2, f3, f4 : 4 × 64 bits ← 256-bit integer denominator's denominator coefficient 'f'
- Each 256-bit word supports multi-precision arithmetic using carry-aware assembly instructions (
adc,mul, etc.) - Denominators may be zero to represent symbolic or infinite states
- Coefficient scaling
(d ÷ (e ÷ f))enables dynamic number scaling without costly CPU division
QuantumNumberV8 instances form a multi-dimensional lattice enabling:
- Exact addition and subtraction across infinite precision
- Symbolic representation of complex numbers and phases
- Phase states modeled as "solid", "liquid", and "gas"
-
Each
QuantumNumberV8node has six neighbors:left,rightup,downin,out
-
Models atomic-like structures where each node is a “quantum number atom” bound in a symbolic lattice.
- Around each node, a sphere of ECC points defines binding sites for neighbors
- Represented as a localized ECC field, supporting dynamic symbolic binding
- Nodes identified by coordinates:
(x, y, z)
where:
-
x, yare ECC points on secp256k1 curve -
zis a scalar multiplier encoding phase or materialization depth -
z = ∞indicates fully quantum superposition (no classical collapse) -
Finite
zindicates classical materialization
- Multiplication/division by powers of two or small integers done by incrementing fields (
b, e, f) - For example: dividing by 2 modeled as changing
bfrom 1 → 2 - Operations are symbolic, reversible, and exact
- Performed via lattice summation of
QuantumNumberV8nodes - Supports infinite precision and exact symbolic combinations
- Java model maps directly to Intel x64 MASM instructions
- Metadata bits encode signed zero and symbolic flags
- Trees/lattices of
QuantumNumberV8allow symbolic sums over infinite domains
- Nodes collapse to UTF-8 math character representations only at output
- Offload collapsing operations to FP hardware or GPU parallel algorithms
- Define thresholds on scalar
zfor gas/liquid/solid phase changes - Model entanglement and coherence via ECC algebra on the lattice
- Port core arithmetic to MASM using VS Code + GitHub Actions
- Leverage SIMD and multi-core parallelism for lattice operations
QuantumNumberV8 x64 MASM provides:
- Exact arithmetic with no floating-point error
- Symbolic number support including signed zero and division by zero
- Multi-dimensional lattice structure modeling symbolic numeric phases
- ECC integration for quantum state representation and entanglement
- Efficient native Intel x64 MASM implementation path
This project implements an ultra-realistic physics and chemistry simulation engine designed to model all discrete points and formulas in the universe exactly — from atomic and subatomic scales to complex molecular and electromagnetic phenomena. The core numeric system, QuantumNumberV8, is a symbolic, exact, multi-precision number format capable of representing numbers with no rounding errors, symbolic division by zero, signed zeros, and infinite precision arithmetic.
A key innovation is the use of 4 secp256k1 elliptic curve fields to model physical states:
- 3 secp256k1 fields represent the three spatial (x, y, z) dimensions with discrete points at resolutions beyond the Planck scale, enabling exact modeling of liquids and solids.
- 1 additional secp256k1 field encodes the complex dimension for electricity, waveforms, and electromagnetic states, capturing phase, frequency, and quantum effects naturally.
This 4-field system enables precise, cryptographically strong randomness and entropy modeling fundamental for simulating quantum uncertainty, thermodynamic entropy, and chaotic physical processes.
The system models physical states at a resolution better than Planck scale, enabling unprecedented fidelity and exactness in simulations.
Unlike traditional floating-point systems (IEEE 754), which are approximate and hardware-optimized, QuantumNumberV8 is built from the ground up for exact symbolic computation, secure randomness, and realistic quantum-physical modeling.
- Exact Atomic and Molecular Modeling: Represents atomic bonding, electron clouds, and molecular structures without rounding or approximation errors.
- Native Quantum and Electromagnetic States: Encodes wavefunctions, electricity, and magnetic fields using a 4D numeric system built on secp256k1 curves.
- Cryptographically Strong Randomness and Entropy Modeling: Utilizes the mathematical properties of secp256k1 elliptic curves to generate secure, high-entropy random numbers critical for simulating quantum uncertainty, thermal fluctuations, and chaotic systems.
- Planck-Scale and Beyond Resolution: Models physical states discretized at scales surpassing the Planck length for true quantum fidelity.
- Symbolic Division by Zero and Signed Zero Support: Allows natural representation of singularities, transition states, and physical discontinuities.
- Infinite Precision Arithmetic: Uses 256-bit multi-word integers for each component, enabling modeling at Planck-scale discrete resolution.
- Efficient Memory Layout: Maps cleanly onto 64-bit x64 registers for performance and native code integration.
- Scalable and Mutable: Supports exact incremental updates, allowing for fast physics timestep integration and symbolic transformations.
- Foundation for Explainable AI and Scientific Computing: Deterministic, lossless math enables reproducible simulations and transparent numeric reasoning.
Physics and chemistry in the real world operate on discrete, quantized states governed by exact mathematical relationships, and fundamentally involve randomness and entropy as core concepts — from quantum uncertainty to thermodynamic behavior. Current floating-point systems used in almost all simulations approximate these values, leading to:
- Accumulated rounding errors
- Numerical instability in sensitive simulations
- Loss of symbolic meaning and physical singularities
- Inadequate randomness modeling and entropy representation
- Limited precision restricting subatomic or quantum-scale modeling
By contrast, QuantumNumberV8 provides a fundamentally new numeric substrate that matches the universe’s true discrete structure and cryptographic randomness properties, enabling:
- Perfectly stable and accurate simulation of all physical, chemical, and stochastic phenomena
- Emergent and explainable quantum behaviors
- Accurate thermodynamic entropy modeling and chaotic systems
- Next-level ultra-realistic gameplay and scientific insight
| Feature | QuantumNumberV8 | IEEE Float (Single/Double) |
|---|---|---|
| Precision | 256-bit multi-word (exact) | 32-bit / 64-bit (approximate) |
| Number Type | Symbolic rational expressions | Binary floating-point numbers |
| Rounding Errors | None — exact arithmetic | Present — rounding and truncation |
| Division by Zero | Supported symbolically (valid states) | Runtime error or Infinity/NaN |
| Signed Zero | Distinguishes +0 and -0 precisely | Supports +0 and -0 but less meaningful |
| Handling Singularities | Natural symbolic representation | Not supported, causes instability |
| Arithmetic Operations | Multi-word carry, symbolic scaling | Hardware-accelerated but approximate |
| Representation of Complex Numbers | Explicit 4D system for complex and real parts | Requires separate structures/classes |
| Physical Scale Modeled | From Planck scale discrete points to universe | Limited by precision and rounding |
| Randomness and Entropy Modeling | Secure, cryptographically strong from secp256k1 fields | Pseudorandom, limited quality |
| Underlying Mathematical Fields | Uses 4 secp256k1 elliptic curve fields | N/A |
| Determinism and Reproducibility | Fully deterministic and lossless | Non-deterministic in some operations |
| Hardware Compatibility | Designed for x64 registers and native calls | Native support in nearly all CPUs |
| Use Cases | Ultra-realistic physics, quantum simulation, symbolic math | General purpose computing, graphics, ML |
This project currently provides:
- The QuantumNumberV8 data structure modeled in Java and x64 assembly
- Low-level routines for symbolic arithmetic, scaling, and comparison
- Cryptographically secure randomness and entropy generation based on secp256k1 curves
- Example physics systems modeling atomic bonding, electromagnetic fields, and thermodynamic processes
- Prototype game engine hooks for integrating exact physics with gameplay
- Implement GPU-accelerated multi-precision arithmetic for real-time performance
- Extend symbolic math operations for chemical reaction kinetics and quantum state evolution
- Develop visualization tools for complex quantum, chemical, and entropy fields
- Create APIs for AI systems leveraging exact physics data and secure randomness for explainability
- Explore custom hardware or FPGA designs for hardware acceleration
Quantum Number Game Simulation - Build the future of simulation — one exact number at a time.
The complete technical and architectural specification for the x256 chip design is publicly disclosed in the x256 Chip Design Document. This document establishes the detailed architecture, design principles, instruction set, and innovations of the x256 processor.
- The design and specification of the x256 processor architecture form the hardware foundation of this system, enabling native 256-bit Quantum Number operations and symbolic AST manipulation.
- The x256 Assembly Language Syntax Specification document (see
x256-assembly-syntax.md) provides the full instruction set architecture and symbolic computation model.
-
The design and specification of the x256 processor architecture form the hardware foundation of this system, enabling native 256-bit Quantum Number operations and symbolic AST manipulation.
-
The x256 Assembly Language Syntax Specification document (see
docs/x256-assembly-syntax.md) provides the full instruction set architecture and symbolic computation model. -
The x256 Memory Chip Design and Memory Model Specification (see
docs/x256-memory-model.md) defines a native 256-bit address and data bus memory system interconnected via fibre-optic links, capable of storing and manipulating Quantum Numbers in their full symbolic and exact numeric duality. -
The combined ecosystem — Quantum Numbers, Quantum OS, Quantum FS, and x256 hardware — form a comprehensive framework for future-proof, AI-optimized computation grounded in exact mathematics.
This document serves as prior art and a technical reference for the x256 chip design, assembly language, and quantum symbolic instruction set.
-
The combined ecosystem — Quantum Numbers, Quantum OS, Quantum FS, and x256 hardware — form a comprehensive framework for future-proof, AI-optimized computation grounded in exact mathematics.
-
The Quantum Number V8 Design
-
The Quantum Number V8 Universe Design
By publicly releasing this detailed design under the Apache License 2.0, the x256 chip design constitutes prior art, legally preventing third parties from patenting or exclusively claiming this technology and architecture.
The Quantum Number system and supporting hardware/software ecosystem provide a foundational framework for precise, symbolic, and complex-number-based modelling across a wide array of scientific and engineering disciplines, including but not limited to:
- Quantum Number Electromagnetic Wave Modelling
- Quantum Number Wave Theory
- Quantum Number Unified Theory
- Quantum Number Theory of Everything
- Quantum Number Electronics
- Quantum Number Electronic Circuit Modelling
- Quantum Number Chemistry
- Quantum Number Physics
- Quantum Number Biology
- Quantum Number DNA Modelling
- Quantum Number Universe Modelling
- Quantum Number Multiverse Modelling
- Quantum Number Atomic Modelling
- Quantum Number Subatomic Modelling
- Quantum Number Universe Simulation
- Quantum Number Multiverse Simulation
- Quantum Number Cosmology
- Quantum Number Quantum Computing
- Quantum Number Neural Networks
- Quantum Number Artificial Intelligence
- Quantum Number Robotics
- Quantum Number Materials Science
- Quantum Number Nanotechnology
- Quantum Number Astrophysics
- Quantum Number Plasma Physics
- Quantum Number Thermodynamics
- Quantum Number Statistical Mechanics
- Quantum Number Fluid Dynamics
- Quantum Number Meteorology
- Quantum Number Geophysics
- Quantum Number Environmental Science
- Quantum Number Pharmacology
- Quantum Number Molecular Biology
- Quantum Number Systems Biology
- Quantum Number Cognitive Science
- Quantum Number Linguistics
- Quantum Number Cryptography
- Quantum Number Financial Modelling
- Quantum Number Data Science
- Quantum Number Network Theory
- Quantum Number Control Systems
- Quantum Number Signal Processing
- Quantum Number Optics
- Quantum Number Photonics
- Quantum Number Acoustics
- Quantum Number Biomechanics
- Quantum Number Biophysics
- Quantum Number Evolutionary Biology
- Quantum Number Synthetic Biology
- Quantum Number Space Exploration
- Quantum Number Quantum Gravity
- Quantum Number Dark Matter Modelling
- Quantum Number Dark Energy Research
- Quantum Number String Theory
- Quantum Number Particle Physics
- Quantum Number Nuclear Physics
- Quantum Number Chemical Engineering
- Quantum Number Electrical Engineering
- Quantum Number Mechanical Engineering
- Quantum Number Civil Engineering
- Quantum Number Aerospace Engineering
- Quantum Number Systems Engineering
- Quantum Number Computational Biology
- Quantum Number Computational Chemistry
- Quantum Number Computational Physics
- Quantum Number
- Quantum Number [Field]
- Quantum Number
- Quantum Number – wildcard placeholder for any related domain
These areas rely on the novel mathematical framework of Quantum Numbers to deliver exact, symbolic, and quantum-aware computation beyond conventional floating-point or integer models.
WARNING: This project contains experimental mathematical and cryptographic research that may have significant implications for financial systems, cryptocurrency security, and institutional fund management.
This project includes research into:
- Quantum Number Cryptography: Novel cryptographic systems based on 256-bit Quantum Numbers
- ECC Curve Mapping: Elliptic Curve Cryptography analysis using AST tree structures
- Financial Mathematical Security: Exact mathematical foundations for financial computation
- Cryptocurrency Research: Mathematical frameworks applicable to digital currency systems
INSTITUTIONAL RISK WARNING: Before any global transition from fiat currencies and precious metals to cryptocurrency systems, the mathematical security foundations explored in this research must undergo extensive peer review and security analysis.
ECC VULNERABILITY RESEARCH: This project's AST (Abstract Syntax Tree) mapping capabilities can theoretically enumerate and analyze all points on elliptic curves (g-nodes), which forms the mathematical basis of most modern cryptocurrency security. Any vulnerabilities discovered in these mathematical foundations could pose significant risks to:
- Institutional investment funds
- Family trusts and estate management
- Corporate treasury holdings
- National cryptocurrency reserves
- Global financial infrastructure
The Quantum Number system's ability to perform exact symbolic computation and AST-based mathematical analysis introduces both opportunities and risks:
Potential Benefits:
- Perfect mathematical precision in cryptographic operations
- Enhanced security through exact symbolic computation
- Elimination of floating-point vulnerabilities in financial calculations
- Comprehensive mathematical verification of cryptographic protocols
Potential Risks:
- Novel attack vectors through symbolic mathematical analysis
- Unforeseen vulnerabilities in traditional cryptographic assumptions
- Computational methods that could compromise existing security models
- Mathematical approaches that may reveal weaknesses in current ECC implementations
This research is published under open-source principles to:
- Enable transparent peer review of mathematical security foundations
- Prevent proprietary exploitation of potential vulnerabilities
- Facilitate collaborative security analysis by the global research community
- Ensure responsible development of next-generation cryptographic systems
DO NOT implement any cryptocurrency or financial systems based on this research without:
- Comprehensive independent security audits
- Extensive peer review by cryptographic experts
- Formal mathematical verification of security properties
- Stress testing against known and theoretical attack vectors
- Gradual deployment with appropriate risk management protocols
Users of this research bear responsibility for:
- Conducting appropriate due diligence before implementation
- Ensuring compliance with financial regulations and security standards
- Performing independent security analysis and verification
- Implementing appropriate risk management and security controls
- Considering the broader implications for financial system stability
This research is provided for educational and scientific purposes. The authors disclaim any responsibility for financial losses, security breaches, or systemic risks that may result from implementation of these mathematical and cryptographic concepts without proper security analysis and verification.
This repository coins and defines the following terms and architectures:
- ✅ Quantum Number
- ✅ Quantum Operating System (QOS)
- ✅ Quantum File System (QFS)
- ✅ Quantum Hardware
- ✅ x256 Chip Design
- ✅ Quantum Abstract Syntax Tree (Quantum AST)
- ✅ Quantition transformation
- ✅ 256-bit symbolic AI computation model
The term “Quantum Number [Field]” refers to any application, domain, or field incorporating the Quantum Number system, where [Field] is a variable placeholder representing any scientific, engineering, or computational discipline.
The terms "Quantum Number", "Quantum Number ", and all derivative or related applications, where <Field> represents any scientific, technological, or computational domain, are covered under this license.
This includes, but is not limited to, all fields enumerated in this document as well as any future domains leveraging the Quantum Number framework.
These inventions and terminologies are publicly disclosed and licensed under the Apache License 2.0, thereby:
- Establishing them as prior art
- Preventing future exclusive patenting or ownership by third parties
- Permitting open, fair use under defined attribution and licensing terms
Any use, modification, or distribution of this code and specification must retain proper attribution and preserve this license and NOTICE file.
For the purposes of this project and its associated licensing under the Apache License 2.0, the terms “Quantum Number”, “Quantum Number ”, “Quantum Number [Field]”, and any similar variants shall be interpreted broadly and inclusively.
Here, <Field> and [Field] are placeholders representing any and all scientific, engineering, computational, technological, or applied disciplines, fields, or domains that may utilize, extend, or build upon the Quantum Number system.
This includes, but is not limited to, all explicitly listed fields within this documentation, as well as any future or unforeseen areas where the Quantum Number framework or related terminology may be applied.
This clause ensures comprehensive coverage and protection of the Quantum Number intellectual property and associated terminology against unauthorized use or patent claims in all present and future domains.
This project is licensed under the Apache License 2.0.
© 2025 Arbitrary Number Project Team
Contributions are welcome under the same license. All submitted code or specifications must comply with the architecture and symbolic computation model defined in this repository.