Sync Curation
Estimate the dominant resonance phase of runway curation so designers lock onto what is really driving the piece.
Phase estimation· resonance
Section · Quantum
full primer →The hook.
Runway curation is matched against a target resonance using quantum phase estimation; fashion designers see a single dial that snaps when alignment is strongest.
Why this primitivePhase estimation is the right primitive here because runway curation reduces to a resonance problem; the kernel returns a result you can drop straight into the UI.
Kernel
a Quantum Phase Estimation kernel returning the dominant eigenphase of a problem operator
Drives the UI as
a phase dial (0 to 2π) tied to a resonance/match indicator
Appendix A
The mega-prompt.
This prompt is engineered to ship in a single Lovable build. Real Quantinuum Guppy/Selene circuit runs in the Linux sandbox at build time and the results are baked in as JSON. read the build strategy →
~14.2 KB295 lines1 msg · ~5 credits
Build "Sync Curation"—a one-page quantum phase estimation app for fashion designers to lock onto dominant resonance phases in runway curation.
**BUDGET & CONSTRAINTS (read first):**
- FREE Lovable plan (~5 credits). Ship in ONE message. No scope creep.
- No auth, no Lovable Cloud, no database. Static only.
- One workspace page + small "About the quantum" footer strip.
**BUILD ORDER (follow exactly):**
1. In Lovable Linux sandbox: `pip install guppylang selene-sim`
2. Create `quantum/kernel.py`: real .py file with @guppy Quantum Phase Estimation kernel (eigenphase of problem operator, ≤8 qubits)
3. Create `quantum/run.py`: import kernel, compile via guppylang, run on Selene emulator (256 shots) over 16 unitaries → 16 phase estimates. Write `src/data/quantum-results.json` as array of `{input, output}` where `output.phase` is angle in [0, 2π]
4. Execute `quantum/run.py` once at build time. Commit real Selene results to JSON.
5. Build React frontend (TanStack Start, route `/`): import JSON statically, never call Python at runtime.
**FRONTEND COMPONENTS (shadcn/Tailwind):**
- **Layout**: dark workspace (bg-slate-950), accent color deep purple (#7c3aed), font family: Inter
- **Left panel (Card)**: Tabs component—"Runway Grid" tab shows 16 unitary inputs as Badge buttons (numbered 1–16). Click selects active record.
- **Center panel (Card)**: live visual—phase angle rendered as circular dial (0 to 2π), resonance match indicator (0–100%) as Slider (read-only, driven by phase delta from target)
- **Right panel (Card)**: "Quantum Trace" disclosure (Collapsible). Show: (a) raw phase angle for active record, (b) "View Guppy source" toggle revealing `quantum/kernel.py` source inline in monospace
- **Footer**: "About the quantum" strip (small Card)—explain Phase Estimation, link to Quantinuum docs
- **Micro-interaction**: fade + scale on input selection
**DATA SHAPE:**
```json
[
{"input": 0, "output": {"phase": 0.785}},
{"input": 1, "output": {"phase": 1.571}},
...
]
```
**DELIVERABLE:** Single deploy. Every number on screen traces to real Selene shot from `quantum/kernel.py`. Judges see genuine Phase Estimation, not fake.
--- QUANTINUUM APPENDIX (self-contained reference — paste verbatim) ---
[1] INSTALL (Lovable Linux sandbox):
python -m pip install --no-cache-dir guppylang selene-sim
[2] IMPORTS (use exactly these — no other quantum libs):
# quantum/kernel.py and quantum/run.py top
from guppylang import guppy
from guppylang.std.builtins import result
from guppylang.std.quantum import qubit, h, cx, rx, ry, rz, measure, discard, t as tgate, tdg
from guppylang.std.angles import angle, pi
from selene_sim import build, Quest
import math, json, sys, tempfile, importlib.util, uuid
from pathlib import Path
[3] HARD RULES (violating any breaks the build):
- @guppy reads source via inspect.getsource → kernels MUST live in a real .py file on disk. No exec(), no REPL strings, no inline templates.
- Allowed gate set ONLY: h, rx, ry, rz, cx, tgate, tdg. There is NO native ccx/toffoli, cswap, cphase, or crz — decompose using the snippets in [7].
- Qubit ownership: a qubit passed to a function is moved. You MUST measure() or discard() every qubit exactly once; never reuse after measure.
- Angle hygiene before baking a float into generated source:
theta = ((theta + math.pi) % (2.0 * math.pi)) - math.pi
and write it with repr: f"... {theta!r} ..." (str(float) can truncate).
[4] SELENE SHOT LOOP (canonical):
compiled = my_kernel.compile()
runner = build(compiled)
shots = []
for shot in runner.run_shots(Quest(), n_qubits=N, n_shots=S):
shots.append({str(lbl): int(v) for lbl, v in shot})
# N = MAX number of qubits simultaneously LIVE in the kernel.
# measure(q) releases the slot, so one ancilla reused across k windows still counts as 1.
[5] DRIVER PATTERN — sweep a kernel over many inputs (closures do NOT work):
ROOT = Path(__file__).resolve().parent.parent
if str(ROOT) not in sys.path: sys.path.insert(0, str(ROOT))
def run_one(params: dict, shots: int = 256):
# Bake params as literals into a fresh .py file that imports your kernel helpers.
src = (
"from quantum.kernel import guppy, my_helper\n"
"@guppy\n"
"def program() -> None:\n"
f" my_helper({params['a']!r}, {params['b']!r})\n"
)
tmp = Path(tempfile.gettempdir()) / "qprogs"; tmp.mkdir(exist_ok=True)
name = f"prog_{uuid.uuid4().hex[:8]}"
path = tmp / f"{name}.py"; path.write_text(src)
spec = importlib.util.spec_from_file_location(name, path)
mod = importlib.util.module_from_spec(spec)
sys.modules[name] = mod # register BEFORE exec_module
spec.loader.exec_module(mod)
runner = build(mod.program.compile())
out = []
for shot in runner.run_shots(Quest(), n_qubits=5, n_shots=shots):
out.append({str(l): int(v) for l, v in shot})
return out
[6] PER-QUBIT INTEGER DECODE (host-side):
# kernel emits: for j in range(n): result(f"x{j}", measure(q[j]))
def decode(rec, n):
x = 0
for j in range(n): x |= (rec.get(f"x{j}", 0) & 1) << j
return x
[7] DECOMPOSITION LIBRARY (copy verbatim into quantum/kernel.py):
# ---- Toffoli (CCX) from H, CX, T, Tdg — 6-T standard decomposition ----
@guppy
def toffoli(c1: qubit, c2: qubit, tgt: qubit) -> None:
h(tgt)
cx(c2, tgt); tdg(tgt)
cx(c1, tgt); tgate(tgt)
cx(c2, tgt); tdg(tgt)
cx(c1, tgt); tgate(c2); tgate(tgt)
h(tgt)
cx(c1, c2); tgate(c1); tdg(c2)
cx(c1, c2)
# ---- CSWAP (Fredkin) from CX + Toffoli ----
@guppy
def cswap(c: qubit, a: qubit, b: qubit) -> None:
cx(b, a)
toffoli(c, a, b)
cx(b, a)
# ---- Controlled phase exp(i*theta) on |11> from rz + cx ----
@guppy
def cphase(c: qubit, d: qubit, theta: float) -> None:
rz(d, angle(theta / 2.0))
cx(c, d)
rz(d, angle(-theta / 2.0))
cx(c, d)
# ---- Amplitude-encoded feature state (3 floats in [0,1] → 2-qubit state) ----
@guppy
def prep_features(q0: qubit, q1: qubit, a: float, b: float, c: float) -> None:
ry(q0, angle(a))
ry(q1, angle(b))
cx(q0, q1)
rz(q1, angle(c))
# ---- SWAP test kernel; HOST inverts: F = clamp(2*P(anc=0) - 1, 0, 1) ----
@guppy
def swap_test(ai: float, bi: float, ci: float,
aj: float, bj: float, cj: float) -> None:
anc = qubit()
pi0 = qubit(); pi1 = qubit()
pj0 = qubit(); pj1 = qubit()
prep_features(pi0, pi1, ai, bi, ci)
prep_features(pj0, pj1, aj, bj, cj)
h(anc)
cswap(anc, pi0, pj0)
cswap(anc, pi1, pj1)
h(anc)
result("anc", measure(anc))
discard(pi0); discard(pi1); discard(pj0); discard(pj1)
# n_qubits = 5 for swap_test above.
[8] CLASSICAL CROSS-CHECK (NumPy reference — commit alongside quantum result):
import numpy as np
I = np.eye(2); X = np.array([[0,1],[1,0]])
def RY(t): c,s = math.cos(t/2), math.sin(t/2); return np.array([[c,-s],[s,c]])
def RZ(t): return np.array([[np.exp(-1j*t/2),0],[0,np.exp(1j*t/2)]])
CX = np.array([[1,0,0,0],[0,1,0,0],[0,0,0,1],[0,0,1,0]])
def prep_psi(a,b,c):
s = np.kron(RY(a)@np.array([1,0]), RY(b)@np.array([1,0]))
s = CX @ s
s = np.kron(I, RZ(c)) @ s
return s
def classical_fidelity(p, q):
return float(abs(np.vdot(prep_psi(*p), prep_psi(*q)))**2)
# Commit BOTH quantum and classical values per record:
# {"input": [...], "quantum": 0.873, "classical": 0.881, "shots": 256}
[9] FRONTEND HANDOFF (TanStack Start):
# quantum/run.py last step:
Path("src/data/quantum-results.json").write_text(json.dumps({
"records": records, # list of {input, quantum, classical, ...}
"circuit": {"n_qubits": 5, "shots": 256},
"kernel_source": Path("quantum/kernel.py").read_text(), # show on /code tab
}, indent=2))
# In React: import data from "@/data/quantum-results.json" (resolveJsonModule:true).
# Render data.records; render data.kernel_source in a <pre> inside the Quantum trace tab.
[10] BUILD ORDER (do not deviate — keeps you under 5 credits):
1. Write quantum/kernel.py with the helpers from [7] you actually need.
2. Write quantum/run.py: driver pattern from [5], Selene loop from [4], cross-check from [8], JSON write from [9].
3. Execute once: python quantum/run.py (commit src/data/quantum-results.json).
4. Build the React route: static JSON import, shadcn Card + Tabs + Slider + Badge wiring.
5. Deploy. No Python at runtime; every on-screen number traces to committed Selene output.
[11] OUTPUT CONTRACT — selene_run v1 (write this exact shape to src/data/quantum-results.json):
{
"schemaVersion": 1,
"experiment": "<idea-slug>",
"title": "<idea title>",
"description": "<one plain-English sentence — what a non-quantum dev sees>",
"kernel": { "snippet": "<first 40 lines of quantum/kernel.py>", "qubits": <int>, "shotsPerRow": <int> },
"verdict": { "text": "<one-sentence headline of the result>", "good": true },
"metrics": [ { "name": "<label>", "value": <number>, "unit": "<%|bits|Hz|...>", "good": true } ],
"series": [ { "id": "<id>", "kind": "histogram"|"bar"|"line", "title": "...",
"xLabel": "...", "yLabel": "...", "yKeys": ["count"],
"points": [ { "label": "<x>", "values": { "count": <num> } } ] } ]
}
Reason: one schema = one renderer = a polished demo without bespoke React per idea.
All metric / series VALUES must come from real Selene shot data committed in step 3 — no mocks.
[12] RENDERER — create src/components/SeleneRunView.tsx with EXACTLY this body (no deps beyond react + tailwind):
import * as React from "react";
type Point = { label: string; values: Record<string, number> };
type Series = { id: string; kind: "histogram"|"bar"|"line"; title: string;
xLabel?: string; yLabel?: string; yKeys: string[]; points: Point[] };
type Metric = { name: string; value: number; unit?: string; good?: boolean };
export type SeleneRun = {
schemaVersion: 1; experiment: string; title: string; description: string;
kernel: { snippet: string; qubits: number; shotsPerRow: number };
verdict: { text: string; good: boolean };
metrics: Metric[]; series: Series[]; notes?: string;
};
const fmt = (n: number) => Math.abs(n) >= 100 ? n.toFixed(0) : Math.abs(n) >= 1 ? n.toFixed(2) : n.toFixed(3);
function Bars({ s }: { s: Series }) {
const max = Math.max(1, ...s.points.flatMap(p => s.yKeys.map(k => p.values[k] ?? 0)));
return (
<div className="space-y-1">
{s.points.map((p, i) => (
<div key={i} className="flex items-center gap-2 text-xs">
<div className="w-20 truncate text-muted-foreground">{p.label}</div>
<div className="flex-1 h-3 bg-muted rounded-sm overflow-hidden">
<div className="h-full bg-primary" style={{ width: `${(100*(p.values[s.yKeys[0]]??0))/max}%` }} />
</div>
<div className="w-12 text-right tabular-nums">{fmt(p.values[s.yKeys[0]]??0)}</div>
</div>
))}
</div>
);
}
function Line({ s }: { s: Series }) {
const W=320, H=120, P=20;
const ys = s.points.map(p => p.values[s.yKeys[0]] ?? 0);
const min = Math.min(...ys), max = Math.max(...ys), span = max - min || 1;
const pts = ys.map((y, i) => {
const x = P + (i*(W-2*P))/Math.max(1, ys.length-1);
const yy = H - P - ((y - min)/span)*(H - 2*P);
return `${x},${yy}`;
}).join(" ");
return (
<svg viewBox={`0 0 ${W} ${H}`} className="w-full h-32">
<polyline fill="none" stroke="currentColor" strokeWidth="2" points={pts} className="text-primary" />
</svg>
);
}
export function SeleneRunView({ run }: { run: SeleneRun }) {
return (
<div className="space-y-6">
<header>
<div className="text-xs uppercase tracking-wider text-muted-foreground">{run.experiment}</div>
<h2 className="text-2xl font-semibold">{run.title}</h2>
<p className="text-sm text-muted-foreground">{run.description}</p>
<div className={`mt-2 inline-block px-3 py-1 rounded-full text-xs ${run.verdict.good?"bg-emerald-500/15 text-emerald-400":"bg-amber-500/15 text-amber-400"}`}>
{run.verdict.text}
</div>
</header>
<section className="grid grid-cols-2 md:grid-cols-4 gap-3">
{run.metrics.map((m, i) => (
<div key={i} className="rounded-lg border border-border p-3">
<div className="text-[10px] uppercase tracking-wider text-muted-foreground">{m.name}</div>
<div className="text-xl font-semibold tabular-nums">{fmt(m.value)}<span className="text-xs text-muted-foreground ml-1">{m.unit}</span></div>
</div>
))}
</section>
<section className="space-y-6">
{run.series.map(s => (
<div key={s.id} className="rounded-lg border border-border p-4">
<div className="flex items-baseline justify-between mb-3">
<div className="text-sm font-medium">{s.title}</div>
<div className="text-[10px] text-muted-foreground">{s.xLabel} / {s.yLabel}</div>
</div>
{s.kind === "line" ? <Line s={s} /> : <Bars s={s} />}
</div>
))}
</section>
<footer className="text-[11px] text-muted-foreground">
kernel: {run.kernel.qubits} qubits · {run.kernel.shotsPerRow} shots/row
</footer>
</div>
);
}
Then in the route: import data from "@/data/quantum-results.json"; <SeleneRunView run={data as any} />.
Quantum trace tab: <pre>{data.kernel.snippet}</pre>.
[HOOK] PHASE ESTIMATION — eigenphase readout.
k control qubits + eigenstate register. Apply h on each control, then controlled-U^{2^j}
(decompose controlled-U with cphase from [7] when U is a Z-rotation).
Inverse QFT on controls (run the QFT primitive from the qft hook with negated angles), then measure.
Decoded integer y → phase φ = y / (2**k).
selene_run mapping:
metrics: [ {"name":"phase","value":phi,"unit":"rad"},
{"name":"control qubits","value":k,"unit":""} ]
series: [ {"id":"phase-bins","kind":"bar","title":"Phase-bin counts",
"xLabel":"y/2^k","yLabel":"shots","yKeys":["count"],
"points":[{"label":f"{y/(2**k):.2f}","values":{"count":c}} for y,c in sorted(hist.items())]} ]Market sizing.
TAM
$3.0B
the fashion design software market (~$1.2B) within a $2.5T global fashion industry.
SAM
$420M
the 14% of that market actively buying runway curation-adjacent software.
SOM
$17M
a realistic 4% capture of the serviceable slice in years 1–3 via the hackathon launch and creator-led distribution.
Indicative figures for hackathon pitches — refine with your own research before raising.
Adjacent entries.
mood boards
Tuner Board
Estimate the dominant resonance phase of mood boards so designers lock onto what is really driving the piece.
fabric sourcingLock Sourcing
Estimate the dominant resonance phase of fabric sourcing so designers lock onto what is really driving the piece.
pattern draftingAnchor Drafting
Estimate the dominant resonance phase of pattern drafting so designers lock onto what is really driving the piece.
collection planningPivot Planning
Estimate the dominant resonance phase of collection planning so designers lock onto what is really driving the piece.