Lattice Mastering
Co-create mastering with a partner via entangled measurements that keep two contributions correlated in real time.
Entanglement· correlated co-creation
Section · Quantum
full primer →The hook.
Two musicians share an entangled session: when one makes a move on the mastering, the other's side updates in correlated lockstep.
Why this primitiveEntanglement is the right primitive here because mastering reduces to a correlated co-creation problem; the kernel returns a result you can drop straight into the UI.
Kernel
a Bell-pair / GHZ entanglement kernel that returns correlated measurement outcomes for two players
Drives the UI as
a two-pane co-creation surface where one side's reveal updates the other in correlated steps
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 →
~14.0 KB293 lines1 msg · ~5 credits
Build "Lattice Mastering"—a one-page quantum music co-creation app for Lovable FREE tier (~5 credits, ONE build message).
CONSTRAINTS:
- No auth, database, Lovable Cloud. Static only.
- One workspace page + "About the quantum" footer strip.
- Ship in ONE message.
REAL QUANTUM (build-time, Guppy + Selene):
1. pip install guppylang selene-sim in sandbox.
2. Create quantum/kernel.py: a real .py file with a @guppy Bell-pair/GHZ entanglement kernel returning correlated measurement outcomes for two players (8–20 inputs, ≤8 qubits).
3. Create quantum/run.py: import kernel, compile via Selene, run 256 shots per input, write src/data/quantum-results.json as [{input, output}, ...] with raw kernel output.
4. Execute quantum/run.py once at build; commit results. NO Python at runtime.
FRONTEND (TanStack Start, /route):
- Import src/data/quantum-results.json statically.
- Workspace: shadcn/Tailwind Card layout: left Tabs for input grid picker (8–20 options); center Chart showing correlated measurement pairs; right two-pane Slider UI where left pane's adjustment updates right pane in entangled steps (driven by JSON).
- "Quantum trace" Disclosure: show raw kernel output + toggle to inline quantum/kernel.py source (judges verify real Selene, not fake).
- Dark theme, one accent color (deep purple or cyan for sound), one typeface, tactile Badge/Badge-variant micro-interactions on record pick.
BUILD ORDER (prevent scope creep):
1. Write quantum/kernel.py (Bell-pair, 8–20 inputs).
2. Write quantum/run.py, test locally, commit quantum-results.json.
3. Create src/components: Card, Tabs, Chart, Slider, Disclosure, Badge.
4. Build /page.tsx: import JSON, render workspace + trace strip.
5. Style dark + accent; ship.
Deliverable: every number traces to real Selene shot from quantum/kernel.py.
--- 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] ENTANGLEMENT — Bell / GHZ + witness.
Bell: h(q0); cx(q0, q1).
GHZ_n: h(q0); for j in 1..n-1: cx(q0, q[j]).
Measure all qubits twice — once in Z basis, once after h() on each → X basis.
Host: <ZZ…Z> = E[(-1)**parity_Z], <XX…X> = E[(-1)**parity_X].
GHZ witness: W = 0.5 - 0.5*(<ZZ…Z> + <XX…X>). W < 0 certifies genuine multipartite entanglement.
selene_run mapping:
metrics: [ {"name":"witness W","value":W,"unit":"","good":W<0},
{"name":"⟨ZZ…Z⟩","value":zz,"unit":""},
{"name":"⟨XX…X⟩","value":xx,"unit":""} ]
series: [ {"id":"z-parity","kind":"bar","title":"Z-basis parity counts",
"xLabel":"parity","yLabel":"shots","yKeys":["count"],
"points":[{"label":"even","values":{"count":z_even}},
{"label":"odd","values":{"count":z_odd}}]} ]Market sizing.
TAM
$25.0B
the music software market (~$11B and music creators (~50M)).
SAM
$5.8B
the 23% of that market actively buying mastering-adjacent software.
SOM
$58M
a realistic 1% 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.
beat-making
Pact Beat-making
Co-create beat-making with a partner via entangled measurements that keep two contributions correlated in real time.
melody sketchingBridge Sketching
Co-create melody sketching with a partner via entangled measurements that keep two contributions correlated in real time.
masteringMirror Mastering
Compare mastering candidates by quantum fidelity so musicians pick the closest match in one tap.
masteringSilhouette Mastering
Reveal the topological shape (clusters, loops, voids) hiding inside mastering so musicians read structure at a glance.