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# Hachimoji DNA Encoding Syntax — Formal Specification
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**Version:** 1.0
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**Date:** 2026-06-23
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**Status:** Active
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**Purpose:** Computational substrate for manifold/QUBO/eigenvalue work.
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---
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## 1. Alphabet
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### 1.1 Base Set
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Eight bases, ordered by ASCII value for monotone lexicographic sorting:
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| Index | Base | ASCII | Phase | Binary (3-bit) |
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|-------|------|-------|-------|----------------|
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| 0 | A | 0x41 | 0° | 000 |
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| 1 | B | 0x42 | 45° | 001 |
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| 2 | C | 0x43 | 90° | 010 |
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| 3 | G | 0x47 | 135° | 011 |
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| 4 | P | 0x50 | 180° | 100 |
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| 5 | S | 0x53 | 225° | 101 |
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| 6 | T | 0x54 | 270° | 110 |
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| 7 | Z | 0x5A | 315° | 111 |
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### 1.2 Ordering Axiom
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```
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A < B < C < G < P < S < T < Z
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```
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This ordering is **canonical** and **immutable**. It satisfies:
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1. **ASCII order = index order.** `ord(A) < ord(B) < ... < ord(Z)`.
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2. **Index order = lexicographic rank.** For any two sequences of equal length, `s₁ < s₂` (lexicographic) if and only if `dna_to_int(s₁) < dna_to_int(s₂)`.
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3. **Monotone encoding.** Assigning sequences by increasing integer rank produces lexicographically sorted output.
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**Proof:** The bases are chosen such that their ASCII codes are in ascending order: 0x41 < 0x42 < 0x43 < 0x47 < 0x50 < 0x53 < 0x54 < 0x5A. Since lexicographic comparison proceeds character-by-character using ASCII ordering, and our index ordering matches ASCII ordering, integer rank ordering implies lexicographic ordering. ∎
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---
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## 2. Integer ↔ DNA Conversion
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### 2.1 Encoding (integer → DNA)
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```
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int_to_dna(value: int, length: int) → string
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```
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Converts a non-negative integer to a fixed-length DNA sequence using base-8 representation, most-significant digit first.
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```
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Algorithm:
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seq = []
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for i in 1..length:
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seq.append(BASES[value mod 8])
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value = value ÷ 8
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return reverse(seq)
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```
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**Constraints:**
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- `value ≥ 0`
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- `length ≥ 1`
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- `value < 8^length` (otherwise the sequence cannot represent the value)
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**Examples:**
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```
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int_to_dna(0, 3) → "AAA"
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int_to_dna(1, 3) → "AAB"
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int_to_dna(7, 3) → "AAZ"
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int_to_dna(8, 3) → "ABA"
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int_to_dna(511, 3) → "ZZZ"
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```
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### 2.2 Decoding (DNA → integer)
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```
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dna_to_int(sequence: string) → int
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```
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Converts a DNA sequence back to its integer value.
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```
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Algorithm:
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value = 0
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for each base b in sequence:
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value = value × 8 + BASE_TO_INDEX[b]
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return value
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```
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**Examples:**
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```
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dna_to_int("AAA") → 0
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dna_to_int("AAB") → 1
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dna_to_int("ABA") → 8
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dna_to_int("ZZZ") → 511
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```
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### 2.3 Roundtrip Axiom
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```
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∀ value ∈ [0, 8^length):
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dna_to_int(int_to_dna(value, length)) = value
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```
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### 2.4 Lexicographic Ordering Axiom
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```
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∀ v₁, v₂ ∈ [0, 8^length):
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v₁ < v₂ ⟺ int_to_dna(v₁, length) < int_to_dna(v₂, length)
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(where < on strings is lexicographic comparison)
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```
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---
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## 3. Symbol Encoding
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### 3.1 Chunks
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A **chunk** is a contiguous group of bytes treated as a single symbol.
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| Chunk size | Range | Symbols | Bases needed |
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|---|---|---|---|
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| 1 byte | 0x00–0xFF | 256 | 3 (8³ = 512 ≥ 256) |
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| 2 bytes | 0x0000–0xFFFF | 65,536 | 6 (8⁶ = 262,144 ≥ 65,536) |
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| n unique | — | n | ⌈log₈(n)⌉ |
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### 3.2 Bases Per Symbol
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```
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bases_needed(n_symbols: int) → int
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length = 1
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while 8^length < n_symbols:
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length += 1
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return length
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```
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---
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## 4. Monotone LUT
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### 4.1 Definition
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A **monotone LUT** is a bijection:
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```
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L: {0, 1, ..., n-1} → DNA_sequences × Solutions × Energies
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```
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such that:
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```
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∀ i < j: L(i).energy ≤ L(j).energy
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```
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and:
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```
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∀ i < j: L(i).sequence < L(j).sequence (lexicographic)
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```
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### 4.2 Construction
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```
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build_monotone_lut(solutions, energies) → LUT
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Algorithm:
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1. Sort solutions by energy (ascending)
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2. Assign DNA sequences in order:
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rank 0 → int_to_dna(0, seq_len)
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rank 1 → int_to_dna(1, seq_len)
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...
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rank n-1 → int_to_dna(n-1, seq_len)
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3. Return LUT: sequence → (solution, energy)
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```
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### 4.3 Properties
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1. **Monotonicity.** Lexicographic sort of sequences = energy sort of solutions.
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2. **Completeness.** Every solution has exactly one DNA sequence.
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3. **Injectivity.** Every DNA sequence maps to at most one solution.
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4. **Minimal encoding.** The optimal solution always maps to `AAA...A` (the lexicographically smallest sequence).
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### 4.4 Verification
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```
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verify_monotone(lut) → (bool, float)
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is_monotone = (sort_by_sequence(lut) == sort_by_energy(lut))
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rank_correlation = spearman(sequence_indices, energy_ranks)
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return (is_monotone, rank_correlation)
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```
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A valid monotone LUT has `is_monotone = true` and `rank_correlation = 1.0`.
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---
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## 5. File Formats
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### 5.1 DNA File (`.dna`)
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Plain text file containing a single DNA sequence.
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```
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Format: [ACGTBPSZ]+
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Encoding: ASCII
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Line ending: LF (optional)
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```
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### 5.2 LUT File (`.lut`)
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JSON file mapping DNA sequences to solutions and energies.
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```json
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{
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"format": "hachimoji_monotone_lut_v1",
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"bases": "ABCGPSTZ",
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"n_vars": 20,
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"n_solutions": 1048576,
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"seq_length": 7,
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"encoding": "monotone",
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"monotone": true,
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"rank_correlation": 1.0,
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"qubo_matrix": [[...]],
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"entries": {
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"AAAAAAA": {"x": [0,0,...,0], "energy": 0.0},
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"AAAAAAB": {"x": [1,0,...,0], "energy": 3.074},
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...
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}
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}
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```
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**Required fields:**
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- `format` — always `"hachimoji_monotone_lut_v1"`
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- `bases` — the base alphabet (must be `"ABCGPSTZ"`)
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- `n_vars` — number of variables in the problem
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- `n_solutions` — total number of entries
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- `seq_length` — bases per sequence
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- `encoding` — always `"monotone"`
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- `monotone` — must be `true` for a valid LUT
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- `rank_correlation` — must be `1.0` for a valid LUT
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- `entries` — the mapping: sequence → {x, energy}
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### 5.3 Metadata File (`.json`)
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Problem-level metadata (optional).
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```json
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{
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"problem": "banded_qubo_20var",
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"n_vars": 20,
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"n_solutions": 1048576,
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"optimal": {"x": [...], "energy": 0.0, "seq": "AAAAAAA"},
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"worst": {"x": [...], "energy": 60.66, "seq": "GZZZZZZ"},
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"timing": {"generate": 0.185, "energy": 0.113, "sort": 0.096, "total": 0.394}
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}
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```
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---
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## 6. Operations
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### 6.1 Encode
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```
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encode(data: bytes, chunk_size: int) → (dna: string, lut: dict)
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1. Split data into chunks of chunk_size bytes
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2. Rank chunks by frequency (most frequent → rank 0)
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3. Assign DNA sequences by rank
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4. Concatenate sequences
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5. Return (dna_string, decode_lut)
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```
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### 6.2 Decode
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```
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decode(dna: string, lut: dict, bases_per_symbol: int) → bytes
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1. Split dna into groups of bases_per_symbol
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2. Look up each group in lut
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3. Concatenate results
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4. Return bytes
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```
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### 6.3 Roundtrip
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```
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decode(encode(data)) = data
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```
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This must hold for all valid inputs. Verified at encode time.
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---
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## 7. QUBO Integration
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### 7.1 Problem Encoding
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A QUBO problem `minimize x^T Q x` over `x ∈ {0,1}^n` is encoded as:
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1. **Matrix:** QUBO matrix Q encoded as bytes → DNA (via `encode`)
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2. **Solutions:** All (or sampled) solutions encoded as DNA sequences (via monotone LUT)
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3. **LUT:** The monotone LUT maps DNA sequences to (solution, energy) pairs
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### 7.2 Solving
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```
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solve_qubo(Q) → (optimal_x, optimal_energy, optimal_seq)
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1. Enumerate all 2^n solutions (or sample)
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2. Compute energies: E_i = x_i^T Q x_i
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3. Build monotone LUT
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4. Return: optimal = LUT["AAA...A"]
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```
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### 7.3 Sorting as Computation
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The act of sorting DNA sequences IS the act of solving the QUBO:
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```
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sorted(dna_sequences) → solutions in energy order
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first(sorted) = optimal solution
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last(sorted) = worst solution
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```
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This is the core insight: **sorting is solving**.
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---
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## 8. GPU Integration
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### 8.1 Radix Sort
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DNA sequences are base-8 digit arrays. Radix sort on these arrays is:
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- **O(n · k)** where n = number of sequences, k = sequence length
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- For constant k, this is **O(n)** — linear time
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- Each digit is 3 bits, perfectly suited for GPU parallel processing
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### 8.2 Zero Copy
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CPU writes DNA sequences to GPU-accessible unified memory. GPU sorts in-place. CPU reads result. No memcpy.
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```
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CPU → [unified memory] → GPU (radix sort) → [unified memory] → CPU
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```
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### 8.3 Braid Sort Kernel
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The GPU compute shader performs braid crossings:
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```
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braid_cross(a, b):
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if a > b: return (b, a) // triangle rotation
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else: return (a, b) // eigensolid (converged)
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```
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Each workgroup processes a chunk of the array. After log₂(n) passes, the array is sorted.
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---
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## 9. Surface Rendering
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### 9.1 8×8 Hachimoji Surface
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A QUBO solution is rendered as an 8×8 pixel grid:
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- Each pixel = one variable
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- x[i] = 0 → dark (A-state, RGB: 13,13,13)
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- x[i] = 1 → bright (G-state, RGB: 26,204,77)
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- Variables laid out in row-major order
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### 9.2 Color Map
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| Base | Color | RGB | Meaning |
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|---|---|---|---|
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| A | Near black | (13, 13, 13) | x = 0 |
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| B | Deep purple | (51, 26, 77) | synthetic |
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| C | Ocean blue | (26, 77, 128) | synthetic |
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| G | Hachimoji green | (26, 204, 77) | x = 1 |
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| P | Plasma orange | (230, 102, 26) | synthetic |
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| S | Spectral violet | (153, 51, 204) | synthetic |
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| T | Teal | (26, 179, 179) | synthetic |
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| Z | Near white | (242, 242, 242) | synthetic |
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### 9.3 Eigenvalue Fingerprint
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The 8×8 surface is the **eigenvalue fingerprint** of the QUBO solution. Different QUBOs produce different surfaces. The surface IS the answer.
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---
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## 10. Invariants
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The following properties must hold for any valid Hachimoji DNA encoding:
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1. **Alphabet consistency.** All sequences use only bases from `{A, B, C, G, P, S, T, Z}`.
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2. **Ordering consistency.** ASCII order = index order = lexicographic rank.
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3. **Monotonicity.** In a monotone LUT, `sort(sequence) = sort(energy)`.
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4. **Roundtrip.** `decode(encode(data)) = data` for all valid inputs.
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5. **Uniqueness.** Each solution maps to exactly one DNA sequence.
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6. **Minimality.** The optimal (lowest-energy) solution maps to `AAA...A`.
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7. **Completeness.** Every entry in the LUT has a valid solution and energy.
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8. **Correlation.** Rank correlation between sequence index and energy = 1.0.
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---
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## 11. Anti-Patterns
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The following are **forbidden**:
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1. **Non-ASCII bases.** Sequences must use only the 8 canonical bases.
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2. **Variable-length symbols within a LUT.** All sequences in a LUT must have the same length.
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3. **Non-monotone assignment.** If `encoding = "monotone"`, the LUT must satisfy the monotonicity axiom.
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4. **Lossy encoding.** Roundtrip must be exact. No approximation.
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5. **Mutable base ordering.** The base ordering `A < B < C < G < P < S < T < Z` is fixed forever.
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---
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## 12. Extensions
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Future extensions (not yet specified):
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- **Multi-pass radix sort** for sequences longer than 8 bases
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- **Hierarchical LUTs** for problems with structure (banded, sparse, block-diagonal)
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- **Streaming encode/decode** for large files
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- **WebGPU compute shader** for GPU-accelerated sorting
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- **Finsler metric integration** for manifold-aware encoding
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- **Eigenvalue surface** for visual comparison of solutions
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---
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## Appendix A: Reference Implementation
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| Component | File | Language |
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|---|---|---|
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| LUT builder | `python/dna_lut.py` | Python |
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| File encoder | `python/dna_encode_file.py` | Python |
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| Radix sort | `python/dna_radix_gpu.py` | Python + NumPy |
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| GPU kernel | `python/dna_braid.wgsl` | WGSL |
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| WebGPU host | `python/dna_webgpu.js` | JavaScript |
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| Surface render | `python/dna_surface.html` | HTML + Canvas |
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## Appendix B: Proof of Monotonicity
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**Theorem:** The monotone encoding satisfies the lexicographic ordering axiom.
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**Proof:**
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1. Let `S = {s₀, s₁, ..., s_{n-1}}` be solutions sorted by energy: `E(s₀) ≤ E(s₁) ≤ ... ≤ E(s_{n-1})`.
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2. Assign `seq_i = int_to_dna(i, k)` where `k = ⌈log₈(n)⌉`.
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3. By construction, `i < j ⟹ seq_i < seq_j` (lexicographic), because `int_to_dna` preserves ordering (§2.4).
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4. Therefore, `seq_i < seq_j ⟹ E(s_i) ≤ E(s_j)`.
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5. The LUT is monotone. ∎
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## Appendix C: Worked Example
|
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|
||||
**Problem:** 3-variable diagonal QUBO, Q = diag(3, 2, 1).
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|
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| Rank | DNA | Solution | Energy |
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||||
|---|---|---|---|
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| 0 | AAA | [0,0,0] | 0.0 |
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| 1 | AAB | [0,0,1] | 1.0 |
|
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| 2 | AAC | [0,1,0] | 2.0 |
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| 3 | AAG | [0,1,1] | 3.0 |
|
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| 4 | AAP | [1,0,0] | 3.0 |
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| 5 | AAS | [1,0,1] | 4.0 |
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| 6 | AAT | [1,1,0] | 5.0 |
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| 7 | AAZ | [1,1,1] | 6.0 |
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**Verification:**
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||||
- Lexicographic sort: AAA < AAB < AAC < AAG < AAP < AAS < AAT < AAZ
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- Energy sort: 0.0 ≤ 1.0 ≤ 2.0 ≤ 3.0 ≤ 3.0 ≤ 4.0 ≤ 5.0 ≤ 6.0
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- Monotone: ✓
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||||
- Optimal: AAA → E=0.0
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||||
- Worst: AAZ → E=6.0
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||||
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Reference in a new issue