ugrad.ai | Quantum-Centric Communication Cycle Research

Executive Summary

This paper defines a practical architecture for cross-app intelligence where one communication cycle controls authoring, execution, verification, and reuse across browser PWAs and quantum backends.

|ψ_out⟩ = (E ∘ R ∘ D ∘ T)(|ψ_in⟩), T=Detect, D=Decode, R=Render, E=Enhance

Pipeline State Evolution · Use chart lane only when stage timing/fidelity deltas change; otherwise equation-only review is sufficient.

What A Reviewer Sees Fast

Core thesis: macro orchestration (Μ) emerges from coordinated micro operators (μ) across a repeatable Detect → Decode → Render → Enhance cycle.
Production path: overclocked local source → prefix/gutter + steno + `.qbit` encode → preflight constraints → QPU dispatch → validation → memory reinjection.
Robustness claim: round-trip fidelity and formatter-safe strip/reinject provide durable artifact integrity and reproducible handoffs.
Operational value: faster iteration, clearer traceability, and lower deployment ambiguity for research-to-runtime workflows.

Communication Cycle Diagram

100%

flowchart LR
A[Authoring / Intent] --> B[Iron Line L0-L7]
B --> C[DAC Tracks + Stripes]
C --> D[Quantum Gutter + Prefixes]
D --> E[.qbit Robust Encode]
E --> F[Retrieval + Memory Reinjection]
F --> A

100%

Authoring / Intent

Input: Prompt + corpus + Contrails cues
Target: <100ms intent normalization
Output: Structured run payload

Iron Line L0-L7

Input: Run payload + layer map
Target: 24ms cortical budget routing
Output: Deterministic stage ownership

DAC Tracks + Stripes

Input: Source lines + language profile
Target: Stable structural telemetry + sorting keys
Output: Track depth, stripe tokens, complexity hotspots

DAC sorting schema: `sym`, `gate`, `depth`, `category`, `track`, `tokenType`, `lineIndex`, `contextScore`.

Steno Transport

Input: DAC signal + code stream
Target: Invisible metadata retention
Output: `strip` / `pajamas` / `noBLINK` payload

Quantum Gutter + Prefixes

Input: Normalized source + DAC context
Target: 11-symbol per-line semantic coverage
Output: Prefix coordinates + gate-mapped lines

Core fields: `prefixSym`, `cls`, `gate`, `lineCoverage`, `confidence`, `language`.

.qbit Robust Encode

Input: Prefixed + steno stream
Target: Round-trip-safe artifact
Output: Portable `.qbit` package

Preflight Constraints

Input: Candidate circuit / runtime job
Target: Sub-3% submit failure risk
Output: GO / WARN / ABORT verdict

QPU Dispatch

Input: Preflight-approved payload
Target: Queue-safe execution
Output: Job ID + backend handle

Result Validation

Input: Raw execution results
Target: Trustworthy post-run quality
Output: Validated result packet

Retrieval + Memory Reinjection

Input: Validated packet + corpus memory
Target: Higher retrieval precision
Output: Updated memory state

Next Iteration

Input: Updated memory + operator intent
Target: Lower edit-to-dispatch time
Output: Iteration n+1 start state

Capture + Route

01 Intent Capture

IN: Prompt + context corpus
TG: <100ms intent normalize
OUT: Structured run payload

02 Layer Routing

IN: Run payload + stage map
TG: Deterministic layer route
OUT: Stage ownership envelope

Structure Core

03 DAC Tracks + Stripes (Core)

IN: Source lines + language
TG: Sortable structural telemetry
OUT: Track/stripe annotations
KEY: `sym` `gate` `depth` `category` `track` `tokenType` `lineIndex` `contextScore`

04 Steno Transport

IN: DAC signal + code stream
TG: Invisible metadata retention
OUT: Steno-encoded payload

05 Quantum Gutter + Prefixes (Core)

IN: Normalized source + DAC context
TG: 11-symbol per-line coverage
OUT: Line-level semantic coordinates
KEY: `prefixSym` `cls` `gate` `lineCoverage` `confidence` `language`

Artifact + Runtime

06 `.qbit` Encode

IN: Prefixed + steno stream
TG: Round-trip fidelity near 1.0
OUT: Portable `.qbit` artifact

07 Preflight

IN: Candidate circuit / job
TG: Sub-3% submit failure
OUT: GO / WARN / ABORT verdict

08 QPU Dispatch

IN: Preflight-approved payload
TG: Queue-safe execution
OUT: Job ID + backend handle

09 Validation

IN: Raw execution results
TG: Post-run quality integrity
OUT: Validated result packet

Memory + Iterate

10 Memory Reinjection

IN: Validated packet + corpus
TG: Higher retrieval precision
OUT: Updated memory state

11 Iteration n+1

IN: Updated memory + intent
TG: Lower edit-to-dispatch time
OUT: Iteration n+1 start state

Model Trace-Through Audit

A1 Runtime Classification + Routing

TASK: Prefix classify + stage routing per line/event
MODEL: T5-native prefix engine (Rust) + DAC heuristics
WHY: Deterministic low-latency path for L0-L4 loops
RISK: Confidence misses on ambiguous mixed-language lines
GATE: TensorHead reroute + Qw threshold + line coverage check

A2 Authoring + Build Support

TASK: Draft/rewrite specs, explain flows, generate code deltas
MODEL: GPT-5.3 Codex coding agent
WHY: Reliable multi-file edits with strict scope control
RISK: Scope drift or descriptor-equation mismatch
GATE: DOM-path checkpoints + row equation audit + lint pass

A3 Retrieval + Memory Reinjection

TASK: Rank context and reinject state into next iteration
MODEL: BM25 + vector cosine + rerank + time weighting
WHY: Balances lexical precision with semantic continuity
RISK: Stale memory overweighting or recency bias under stress
GATE: Score breakdown inspection + freshness weight + validation packet check

The core contribution is not a standalone feature set, but an integrated control plane: Iron Line L0-L7 for deterministic stage ownership, DAC tracks and stripes for structural observability, and a steno transport layer (`strip`, `pajamas`, `noBLINK`) that preserves timing-critical metadata while keeping developer ergonomics intact. On top of this foundation, the quantum gutter and 11-symbol prefix coordinates provide line-level semantic traceability, while the new `.qbit` codec contributes robustness as a fault-tolerant interchange layer with round-trip-safe encoding, formatter-aware strip/reinject handling, and reproducible decode paths for long-horizon archives and multi-client sync. The ugrad build path operationalizes these abstractions into a measurable pipeline from overclocked local base code to QPU-ready artifacts through preflight checks, backend-constrained dispatch, result validation, and memory reinjection. A second innovation axis is the Contrails keyboard advancement, which turns symbolic concept lanes into an accessible human input protocol for faster intent capture, lower operator cognitive load, and cleaner alignment between keystroke semantics, prefix coordinates, and downstream quantum/runtime transformations. In grant terms, the novelty is the closed-loop linkage of symbolic reasoning, retrieval-memory continuity, resilient codec transport, and production deployment in one reproducible surface, with clear translational value for research velocity, verification rigor, and cross-platform reliability across ugrad.ai and qbitos.ai.

|ψ_out⟩ = Μ(∏_i μ_i) |0⟩^⊗n, μ_i ≡ U(P_i, d_i, c_i, s_i) |psi_out> = M(prod_i mu_i)|0>^otimes n, mu_i = U(P_i,d_i,c_i,s_i)

Canonical Macro/Micro State Equation

Canonical macro micro state circuit chart

Question: Do local μ operations combine into a stable global Μ state?
Metric definition: coherence=.87, operator stability=.82, aggregate output confidence=.89 (0–1 scale).
Decision gate: if coherence < .80, hold dispatch and rerun operator weighting before cycle advance.

Operational Walkthrough: Canonical Macro/Micro State Equation

Macro state (Μ): orchestrates global routing, memory continuity, and policy gates across Iron Line layers.
Micro operators (μ_i): per-line transforms carry prefix, depth, context, and semantic signal into execution.
Runtime mapping: `U(P_i, d_i, c_i, s_i)` maps directly to DAC + Prefixes + Quantum Gutter fields.
Product effect: one equation tracks authoring intent through `.qbit` transport into reproducible runtime behavior.

1. Core Model (Μμ)

Uppercase Mu (Μ) represents macro manifold behavior: global orchestration, routing, memory continuity, and multimodal scoring. Lowercase mu (μ) represents local operators: per-line transforms, confidence reroutes, transport decisions, and cue alignment. This split makes system-level reasoning and implementation-level behavior simultaneously auditable.

System = Μ(Σ_i μ_i(line_i, event_i)) System = M(sum_i mu_i(line_i,event_i))

System Aggregation (Μ over Σμ)

Question: Is system aggregation fast and correct under load?
Metric definition: event-fusion latency=34ms, stage assignment accuracy=97.2%.
Decision gate: if latency > 50ms or accuracy < 95%, fallback to conservative routing profile.

Operational Walkthrough: System Aggregation (Μ over Σμ)

Aggregation rule: each line/event micro-operation contributes to a single auditable macro system state.
Field stitching: line index + event payload + DAC track context fold into shared run identity and trace.
Control use: drives stage ownership, dispatch order, and memory update priority under load.
Product effect: keeps terminal-core behavior interpretable when parallel actions increase project stress.

2. Derived Equation Stack - Canonical Set

|ψ_out⟩ = (E ∘ R ∘ D ∘ T)(|ψ_in⟩) |psi_out> = (E o R o D o T)(|psi_in>), T=detect, D=decode, R=render, E=enhance

Pipeline State Evolution

Question: Where does cycle time concentrate in T→D→R→E?
Metric definition: T=42ms, D=31ms, R=18ms, E=24ms (total 115ms).
Decision gate: if total > 140ms, optimize top-two contributors first (currently T and D).

Operational Walkthrough: Pipeline State Evolution (Detect → Decode → Render → Enhance)

Detect (T): capture source intent and runtime context from `uterm`, `quantum-notepad`, and commander input channels.
Decode (D): classify/parse lines into structural coordinates via prefix + DAC paths before any export or dispatch.
Render (R): materialize UI/state outputs (cards, traces, charts, terminal output) for operator review.
Enhance (E): reinject retrieval-memory and confidence feedback to improve the next operator cycle.

ŷ_i = argmax_k p(k∣x_i), if max p < τ → TensorHead(x_i, 𝒩_i) y_hat_i = argmax_k p(k|x_i), if max p < tau -> TensorHead(x_i,N_i)

Adaptive Confidence with Reclassification

Adaptive confidence and error spread chart

Question: Does rerouting low-confidence lines improve correctness?
Metric definition: reroute rate=7.8%, precision lift=+4.6pp, false-route=2.1%.
Decision gate: if false-route exceeds 3%, raise τ sensitivity and retune confidence thresholds.

Operational Walkthrough: Adaptive Confidence with Reclassification

Primary pass: fast classifier resolves line/operator intent at low latency for terminal-grade responsiveness.
Confidence gate: when probability falls below τ, payload escalates to tensor context (`TensorHead`) instead of forcing bad labels.
Outcome: low-confidence noise is isolated and corrected without slowing high-confidence paths.
Product effect: fewer silent misroutes in automation and safer autonomous patch generation.

Qw(i) = P_i^α · D_i^β · C_i^γ · S_i^δ Qw(i) = P_i^alpha * D_i^beta * C_i^gamma * S_i^delta

Prefix-Depth-Semantic Composite

Question: Are Qw weights balanced for current workload type?
Metric definition: α=.35 β=.22 γ=.27 δ=.16; median Qw=.79.
Decision gate: if median Qw < .72, rebalance α/γ for semantic-heavy sessions.

Operational Walkthrough: Prefix-Depth-Semantic Composite (Qw)

P (prefix): structural symbol confidence from the 11-symbol gutter system.
D (depth): nesting/scope pressure from DAC track context to detect risky structural transitions.
C/S (context/semantic): local and cross-line meaning weight for retrieval + runtime ranking.
Tuning: α/β/γ/δ can be configured per app/layer to balance speed vs precision in terminal flows.

𝓕_rt = 1 − d(x, x̃) / ∥x∥ F_rt = 1 - d(x, x_tilde)/||x||, x_tilde = decode(encode(x))

Round-Trip Fidelity

Question: Is round-trip integrity good enough for production handoff?
Metric definition: F_rt mean=.983, p95=.976, release floor=.960, breaches=1.7%.
Decision gate: if breaches exceed 3%, block export and run strip/reinject diagnostics.

Operational Walkthrough: Round-Trip Fidelity for Handoff Artifacts

Encode/decode loop: every `.qbit` artifact can be checked against source payload before handoff.
Metric gate: reject or warn if 𝓕_rt drops below release threshold for production workflows.
Formatter-safe path: strip/reinject is validated to keep semantic payload stable after code formatting.
Product effect: stable archival and reproducible transport across machines/clients.

m_t+1 = λm_t + (1−λ)φ(e_t) m_(t+1) = lambda*m_t + (1-lambda)*phi(e_t)

Cross-App Memory Update

Question: Is memory improving signal without instability?
Metric definition: λ=.82, memory gain=+6.2%, variance=±3.4%.
Decision gate: if variance exceeds ±5%, lower reinjection weight and tighten freshness bias.

Operational Walkthrough: Cross-App Memory Update Rule

State blend: previous memory and new event evidence combine using λ to avoid thrash.
Persistence: synced through local app state + `BroadcastChannel` updates for shared context continuity.
Usage: drives better defaults for next command, prompt expansion, and retrieval scoring.
Stress behavior: memory smoothing reduces regression when project pressure increases.

v_t+1 = ηv_t + a_t, Δidx = sign(v) · 𝟙(|v| > v_min) v_(t+1) = eta*v_t + a_t, Delta_idx = sign(v) * 1(|v| > v_min)

Transport/Scrub Dynamics

Question: Are scrub interactions fast without accidental jumps?
Metric definition: v_min=.35, overshoot=0.8%, settle time=112ms median.
Decision gate: if overshoot > 2%, raise v_min or increase damping η.

Operational Walkthrough: Transport / Scrub Dynamics (Inertial Navigation)

Velocity model: inertia + acceleration captures swipe/scroll intent in queue or timeline surfaces.
Threshold gate: `Δidx` only steps when |v| exceeds `v_min`, preventing accidental jumps.
UI targets: coverflow queues, run history stepping, and timeline inspection tracks.
Product effect: smoother control at high speed without losing deterministic indexing.

j* = argmin_j |t_media − t_j| j_star = argmin_j |t_media - t_j|

Transcript-to-Media Alignment

Question: Does transcript highlighting stay time-locked to media?
Metric definition: cue delta median=42ms, p95=96ms.
Decision gate: if p95 exceeds 140ms, re-anchor cue matching cadence.

Operational Walkthrough: Transcript-to-Media Alignment

Nearest cue: active caption/index block is selected by minimal time delta against playback clock.
Runtime behavior: keeps transcript highlight and media state synchronized during seek/scrub.
Targets: VTT attachment paths, narrated walkthroughs, and review timelines.
Product effect: fast evidence lookup with less operator confusion during debug/demo sessions.

p′ = R_z(ψ) · R_y(θ) · R_x(φ) · p p_prime = Rz(psi) * Ry(theta) * Rx(phi) * p

XR/Ocular Pose Projection

Question: Is spatial projection accurate enough for XR navigation?
Metric definition: yaw RMSE=1.9°, pitch=2.3°, roll=1.6°.
Decision gate: if any axis RMSE exceeds 3.0°, apply recalibration pass before publish.

Operational Walkthrough: XR / Ocular Pose Projection

Rotation chain: yaw/pitch/roll transforms apply in ordered sequence for stable orientation math.
Visual targets: point clouds, voxel fields, and sequence viewers across quantum and research views.
Interaction: pointer/camera deltas map into pose updates for interpretable motion.
Product effect: spatial views stay coherent while navigating dense technical state.

Score(d∣q) = w_b·BM25 + w_v·cos(e_q,e_d) + w_r·rerank(q,d) + w_t·time(d) Score(d|q) = w_b*BM25 + w_v*cos(e_q,e_d) + w_r*rerank(q,d) + w_t*time(d)

Multimodal Ranking

Question: Which scoring component drives ranking quality right now?
Metric definition: w_b=.28, w_v=.34, w_r=.24, w_t=.14; MRR=.78.
Decision gate: if MRR drops below .74, rebalance w_r/w_v and rerun relevance calibration.

Operational Walkthrough: Multimodal Ranking for Search + History

Lexical signal: BM25 captures exact keyword/operator surface relevance.
Semantic signal: embedding cosine resolves concept-level similarity beyond token overlap.
Rerank + time: intent-aware reranker and recency weighting improve practical retrieval precision.
Product effect: conversation context stays aligned to “work in progress” instead of stale matches.

𝒞_t+1 = Π(𝒞_t, Qw, 𝓕_rt, M_t, 𝓡_mm) C_(t+1) = Pi(C_t,Qw,F_rt,M_t,R_mm)

Unified Communication Cycle Operator

Question: Does Π composition improve next-cycle performance?
Metric definition: cycle score=.88, next-iteration latency improvement=13%.
Decision gate: if improvement falls below 8%, review component weights (Qw/F_rt/M_t/R_mm).

Operational Walkthrough: Unified Communication Cycle Operator

Composition layer: Π ties classification weight (Qw), artifact integrity (𝓕_rt), memory (M_t), and retrieval (𝓡_mm) into one decision surface.
Operational loop: each iteration updates the next run state instead of treating outputs as terminal.
Terminal-core relevance: converts math into actionable control logic for deployment and autonomous iteration.
Scale behavior: as project stress increases, this keeps the system auditable and less error-prone.

2.1 Evidence Graphing + Benchmark Expansion

To expound the equation stack with contemporary quantum-centric evidence, this section maps each equation family to chart and graph forms used in current IBM quantum research/announcement narratives and related benchmark reporting. The intent is to make each symbol block auditable via a concrete visual method, not prose alone.

Closed-Loop Orchestration Graphs

For |ψ_out⟩ = (E ∘ R ∘ D ∘ T)(|ψ_in⟩) and 𝒞_t+1 = Π(...), use linked workflow/network charts that show quantum/classical handoff, queue utilization, and loop latency across runtime nodes.

Error/Fidelity Progression Curves

For 𝓕_rt, Qw(i), and confidence-reroute equations, use line/scatter/interval plots with uncertainty markers to show quality, variance, and threshold gate behavior.

Multimodal Ranking Diagnostics

For Score(d∣q), visualize contribution decomposition (BM25, vector, rerank, time) and retrieval drift over iterations to validate memory reinjection effects.

Transport + Runtime Scaling

For v_t+1 and queue-step dynamics, use throughput-vs-latency charts and dimension scaling plots to expose bottlenecks under boardroom-scale workloads.

Chart Method: Candlestick-Style Bars for Equation Benchmarks

Bar body: central estimate for each equation metric lane (e.g., F_rt, rerank gain, dispatch latency).
Upper/lower wick: confidence interval or run-to-run spread to prevent overclaiming single-point wins.
Side-by-side lanes: baseline vs current vs target so product/readiness deltas remain visible to reviewers.

Equation Metrics Candlestick Panel (Baseline → Current)

F_rt

Score

Memory

Dispatch

Cycle Π

IBM-Linked Evidence Trail for Equation Sections

QCSC closed-loop anchor: RIKEN + IBM Fugaku/Heron orchestration references the exact type of closed-loop control this paper formalizes.
Networked scale-out anchor: IBM’s QNU/networked quantum roadmap aligns with the control-plane + server/MCP orchestration lane in the diagram.
Research corpus anchor: IBM research/publication streams provide optimization, Hamiltonian, PDE, and ML graph forms for equation-to-chart traceability.
Update lane: maintain this section as a living benchmark board tied to blog/research updates and release checkpoints.

Data provenance rule: this page treats first-party uvspeed/uterm runs as claim-bearing evidence; external company figures are context comparators only, not endorsements or performance claims.

RIKEN + IBM QCSC at scale · Networked quantum computers roadmap · IBM Quantum Blog · IBM Quantum Research

Live Equation → Figure Asset Matrix (Local Assets)

Equation Traceability Panel

Each tile binds one equation lane to a local figure asset and a validation role. Swap assets per run while preserving equation mapping IDs for audit continuity.

Link detect probing...

E01/E10 — Pipeline + Memory LoopPublished

Control-plane routing / closed-loop orchestration map

E02 — Confidence + ReclassifyValidated

Error/energy spread with interval behavior

E03 — Qw Composite WeightValidated

Dimension scaling and objective landscape

E04 — F_rt Round-Trip FidelityDraft

Field stability/noise/restore panel progression

E08 — Multimodal Score(d|q)Draft

Comparative ranking surfaces and contrast lane

E11 — Unified Cycle Operator ΠValidated

Composed operator blocks and stage dependencies

Chart Pack ControlsLive

Switch all matrix cards between Default, Bloch, 3D Cube, and Circuit views.

E03 Extension LaneValidated

Qw composite lane expansion for side-by-side dimension checks below the primary matrix cards. alpha=.35 · beta=.22 · gamma=.27 · delta=.16 · median Qw=.79

uTerm → Quantum Integration Readiness Gate

Gate A (signal fidelity): `uterm` command stream exports deterministic prefix + steno traces with `F_rt` above release threshold.
Gate B (dispatch safety): `.qbit` artifacts pass preflight GO/WARN policy and backend compatibility checks before QPU queueing.
Gate C (closed-loop memory): memory reinjection and retrieval score deltas are measured per iteration and attached to the equation matrix row IDs.
Gate D (boardroom evidence): each milestone review includes updated local figure assets, candlestick metric bars, and run metadata for reproducibility.

3. Deployment Pattern for .qhtml

The landing is intentionally structured as a publication-grade PWA document: semantic sections, dense typography, justified narrative blocks, and a sticky expansion rail that keeps technical context available while reading. This supports desktop and mobile reading, and mirrors cleanly into launch announcements without layout drift.

3.1 Quantum Race Dynamics

The visual transport layer follows a race-wave interpretation of the communication cycle: Bloch-field coherence, torn-mesh constraints, and dot-flow sampling evolve together under one shared waveform. This mirrors the system objective where macro state (Μ) is not isolated from local operators (μ); instead, all lanes update from the same cycle clock, making phase drift, queue transitions, and retrieval-memory reinjection observable as one coupled dynamic rather than separate effects.

4. Publication + Distribution Targets

For final output, treat publication as a two-track release: (A) peer-reviewed research visibility, and (B) production-grade software distribution with explicit licensing and citation metadata.

Nature Computational Science — Submission Guidelines
Target checklist for manuscript scope fit, initial formatting, editorial process, ORCID linkage, and production readiness.

arXiv (quant-ph / cs.AI / cs.SE)
Primary preprint layer for fast disclosure, timestamping, and community feedback.

Quantum Journal
Peer-reviewed venue for quantum algorithms, systems framing, and reproducible methods.

PRX Quantum
Top-tier quantum publication channel for higher-impact manuscript versions.

npj Quantum Information
High-visibility journal track when framing cross-disciplinary technical contribution.

AVS Quantum Science (AIP)
Applied quantum engineering and implementation-focused publication positioning.

IBM Quantum Research
Industry-aligned benchmark and partnership context for practical utility narratives.

IBM: RIKEN + Fugaku QCSC Closed Loop
Closed-loop quantum/HPC orchestration milestone used here as a benchmark narrative for cycle equations.

IBM: Networked Quantum Computers
QNU and quantum-network scale strategy reference for control-plane/server orchestration lanes.

IBM Quantum Blog (Live Updates)
Rolling announcement feed for graphing comparators, advantage claims, and benchmark updates.

npm
JavaScript package distribution target for browser/Node modules and SDK surface.

PyPI
Python package distribution target for research tooling, automation, and CLI workflows.

crates.io
Rust package distribution target for high-performance engine and systems components.

Zenodo (DOI archival)
Immutable DOI for code/data snapshots tied to publication submissions and revisions.

SPDX License Registry
Canonical license naming for Apache-2.0/MIT/etc. and unambiguous downstream reuse.

Quanta Magazine
Narrative benchmark for broad-audience explainability after technical release.

Coverage: Config + Plugin + Rules + Artifacts + Codec

Config: publish versioned runtime/build config with reproducible defaults (`pyproject.toml`, lockfiles, model/provider matrix).
Plugin + MCP: release plugin manifest, tool schema inventory, and capability guardrails with compatibility notes.
Rulesets: include machine-readable policy/rules docs for safety, formatting scope, and deployment constraints.
Artifacts: ship signed release artifacts with checksums, changelog, and DOI snapshot mapping.
Codec specifics: document `.qbit` encode/decode mode, strip/reinject behavior, round-trip tests, and schema versioning.

Journal + Product Readiness Metrics (Live)

Current state: page/readability + contribution framing ≈ 85%; manuscript package readiness ≈ 60–70%.
Gap to submission: convert this launch paper into manuscript form (Abstract, Intro, Related Work, Methods, Experiments, Results, Limitations).
Evidence gates: benchmark protocol, datasets/traces, hardware table, statistical reporting, claim→figure/table map.
Policy gates: ORCID-linked authors, disclosures, cover letter, and venue-specific formatting checks.

Rough Timeline (Boardroom Demo → Journal Submission)

Weeks 0–2: lock baseline metrics and reproducibility harness (`F_rt`, latency, failure-rate, memory precision).
Weeks 2–6: run structured experiments across L0–L7 flows and finalize figures/tables for executive + reviewer audiences.
Weeks 6–10: draft manuscript + supplement package, complete metadata/policy checks, prepare preprint and journal submission files.
Weeks 10–12: submit preprint, start journal submission cycle, and align boardroom narrative to measured deltas.

Industry QPU Comparison Board (Continuous Update Lane)

Comparator set: IBM, Quantinuum, IonQ, Rigetti, D-Wave, Xanadu, QuEra, Pasqal, IQM, PsiQuantum, Atom Computing, OQC, Alice & Bob, Haiqu-linked claims.
Track per company: hardware modality, public qubit scale, error/fidelity disclosures, runtime claims, SDK maturity, and publication cadence.
Scoring model: normalize to common gates: utility evidence, reproducibility quality, deployment readiness, and partner validation.
IBM lane anchor: use IBM Quantum research/publications and platform benchmark context as baseline reference for external parity checks.

Open qbitos main Open universal viewer Open μgrad runtime

Prepared as a launch-grade research landing surface for immediate deployment on ugrad.ai and mirrored “launching soon” distribution on qbitos.ai.