A LiveNakoudo protocol for real-estate matching.

HudosanyaLN is a quantum-inspired and optics-informed matching engine that combines ordinary property conditions, persona vectors, controlled exploration, earthquake safety correction, and proof-oriented records.

HudosanyaLN は、条件検索・ペルソナベクトル・ゆらぎ探索・地震安心補正・証跡確認を統合する、不動産マッチングのプロトコルエンジンです。

Read the protocol Claim boundary

Classical software today. Designed with a clean adapter path for hybrid quantum optimization and post-quantum security later.

HudosanyaLN pipeline v0.1 concept page

State preparation |user⟩ + |intent⟩ + |place⟩

budget, commute, room taste, persona, safety priority

α0–0.30exploration / yuragi

ψvector fieldpersona + property

TGLproof layerrecords + validation

One-sentence definition

What HudosanyaLN is

HudosanyaLN is the protocol engine behind hudosanya.com お部屋なこうど: a real-estate matching system that treats user preference as a living state, searches property data as a weighted possibility field, and corrects final recommendations with safety and evidence.

一般ユーザーには「お部屋なこうど」として見せ、技術者・研究者・メディアには HudosanyaLN として説明します。

Brand separation

Consumer brand: hudosanya.com お部屋なこうど
Technical name: HudosanyaLN / LiveNakoudo Protocol Engine
Primary launch field: Rental matching for singles, students, and new-life search
Expansion field: Buy/sell matching, agent matching, partner matching, entrepreneur matching

Important claim boundary

Quantum-inspired today, quantum-ready by design.

HudosanyaLN does not claim that production recommendations currently run on a quantum computer. The current implementation direction is classical software: databases, vector search, ranking, correction, explainability, and proof-oriented logs.

The “quantum” part is the mathematical design language: state spaces, amplitudes, perturbation, interference, optimization objectives, correction, and measurement. The system is structured so future quantum or hybrid quantum-classical solvers can be attached to selected optimization steps when they become practically useful.

Quantum mechanics + optics as product architecture

The physics model behind HudosanyaLN

Room search is not only filtering. A person’s “best room” is often a probability distribution across price, commute, comfort, risk, and future lifestyle. HudosanyaLN models that distribution before making a recommendation.

Preference as a state vector

The user is represented as a changing state: budget, commute, room taste, life pattern, safety priority, and exploration appetite.

∑

Candidates as superposed possibilities

Candidate rooms remain in a weighted possibility field until hard constraints, persona fit, and safety corrections collapse the final ranking.

∆

Yuragi perturbation

The user controls the search spread: 0% for “exact fit,” around 10% for “slightly wider,” and up to 30% for “adventure.”

◎

Optical lensing

Criteria act like lenses, filters, prisms, apertures, and correction plates: they amplify, suppress, split, and correct candidate signals.

⊗

Interference of signals

Distance, price, persona, building quality, and safety can amplify each other or cancel each other. The score is not a simple keyword match.

✓

Measurement + explanation

When the system recommends a room, it also explains why: fit, tradeoff, exploration reason, and safety correction reason.

System architecture

Six layers of the protocol engine

Intent Layer

Rent, buy, sell, consult, student move, company relocation, earthquake-safe search.

Constraint Layer

Budget, commute, map distance, pets, parking, room size, structure, floor, availability.

Persona Vector Layer

Books, stories, room photos, lunch habits, food, media use, lifestyle signals, taste clusters.

Yuragi Search Layer

Controlled perturbation changes the search width without losing user intent.

Safety Correction Layer

Earthquake risk, building structure, ground/shake characteristics, Namazu score, confidence.

Proof + Explain Layer

TGL / blockchain-oriented evidence records, validation, provenance, and recommendation reasons.

For engineers

Protocol I/O shape

The page intentionally avoids exposing the production schema. This illustrative JSON shows the technical shape of a HudosanyaLN request and response.

{
  "intent": "rent",
  "hard_constraints": {
    "max_rent": 85000,
    "commute_to": "university_or_company",
    "pet_ok": false,
    "parking": "optional"
  },
  "persona_vector": {
    "room_taste": ["quiet", "bookshelf", "morning_light"],
    "media_habit": "youtube_and_books",
    "lifestyle": "student_or_single_worker"
  },
  "yuragi_alpha": 0.10,
  "safety_priority": "high",
  "output": [
    "ranked_candidates",
    "score_decomposition",
    "explanation",
    "proof_reference"
  ]
}

Quantum-computer integration path

Preparing for quantum integration in parallel

HudosanyaLN is designed so that near-term classical matching remains useful now, while selected optimization steps can later be exported to hybrid quantum or quantum-inspired solvers.

Phase 1

Classical LN

Rental DB + bukken DB indexing, vector search, ranking, safety correction, and explanation.

Phase 2

Optimization exports

Convert selected matching problems into objective functions and constraint graphs for benchmarking.

Phase 3

Hybrid adapters

Add adapters for quantum-inspired solvers, QAOA-style workflows, annealing-style experiments, and classical fallback.

Phase 4

Quantum/PQC readiness

Prepare post-quantum security migration and test quantum hardware integration only where it is technically justified.

Why this timing matters

Quantum hardware is progressing, especially in error correction and fault-tolerant roadmaps, but ordinary production business systems should still be careful about overclaiming. HudosanyaLN uses a practical classical implementation now while keeping a future adapter path open.

Google Quantum AI reported Willow error-correction progress in 2024.
IBM’s public quantum roadmap points toward fault-tolerant milestones around 2029–2030.
NIST finalized the first post-quantum cryptography standards in 2024.

Trust rule

AI nakoudo without human peeping.

Persona answers are for the user’s private recommendation experience. The protocol should not expose sensitive persona details to landlords or use lifestyle signals to reject applicants. Human-readable output should focus on helpful explanation, not hidden discrimination.

ペルソナ情報は、ユーザー本人への提案のために使う。貸主側の選別や、人間がのぞき見る仕組みにはしない。

Private recommendation No landlord-side persona disclosure Safety correction with explanation Proof-oriented records

Builder profile

Hayato Kameta / 亀田勇人

Founder and CEO of ZWEISPACE JAPAN. Physics background from Kyoto University, with work spanning quantum optics, internet/telecom strategy, fintech, real estate, blockchain, AI, earthquake-risk systems, and property data platforms.

For public release, this profile should be checked against the final official company biography before publication.

Physics originQuantum mechanics / optics thinking applied to matching architecture.

Real-estate dataHudosanya rental and buy/sell systems provide practical property data roots.

Safety systemsNamazu / earthquake-risk work informs the safety-correction layer.

Proof systemsReal-estate blockchain and TGL work inform the evidence/proof layer.

For engineers and press

Reference description

HudosanyaLN is a LiveNakoudo protocol engine for real estate. It combines vector search, persona modeling, controlled exploration, earthquake safety correction, and proof-oriented records into one matching architecture. The consumer experience is branded as hudosanya.com お部屋なこうど.

Suggested short quote

“HudosanyaLN treats room search as a living matching problem, not a static filter. It starts with human preference, explores controlled possibilities, and corrects the final result with safety and evidence.”

External technical context

These references are not endorsements. They explain why HudosanyaLN is careful to say “quantum-inspired today” and “quantum-ready by design.”