[\![160,\,28,\,3]\!] factory

As gates: \mathrm{T}_{0}\cdot \mathrm{T}_{1}\cdot \mathrm{T}_{2}\cdot \mathrm{T}_{3}\cdot \mathrm{T}_{4}\cdot \mathrm{T}_{5}\cdot \mathrm{T}_{6}\cdot \mathrm{T}_{7}\cdot \mathrm{T}_{8}\cdot \mathrm{T}_{9}\cdot \mathrm{T}_{10}\cdot \mathrm{T}_{11}\cdot \mathrm{T}_{12}\cdot \mathrm{T}_{13}\cdot \mathrm{T}_{14}\cdot \mathrm{T}_{15}\cdot \mathrm{T}_{16}\cdot \mathrm{T}_{17}\cdot \mathrm{T}_{18}\cdot \mathrm{T}_{19}\cdot \mathrm{T}_{20}\cdot \mathrm{T}_{21}\cdot \mathrm{T}_{22}\cdot \mathrm{T}_{23}\cdot \mathrm{T}_{24}\cdot \mathrm{T}_{25}\cdot \mathrm{T}_{26}\cdot \mathrm{T}_{27}

As monomials: 0+1+2+3+4+5+6+7+8+9+10+11+12+13+14+15+16+17+18+19+20+21+22+23+24+25+26+27 (output wires per term, separated by +)

This row stands for every gate that a CNOT circuit and S, Z and CZ gates on the outputs can reach from this one. They all prepare the same magic state. The wires are shown in the S_k-canonical frame.

With every rotation a \mathrm{T}=\mathrm{diag}(1,e^{i\pi/4}), the rotations deposit the gate above times a diagonal Clifford. Undo it after the rotations and before the checks are measured, with 34 single-wire gates and 19 \mathrm{CZ} gates: \mathrm{S} on output 0, \mathrm{S} on output 1, \mathrm{S}^\dagger on output 2, \mathrm{S} on output 3, \mathrm{S}^\dagger on output 4, \mathrm{S} on output 5, \mathrm{S}^\dagger on output 6, \mathrm{S}^\dagger on output 7, \mathrm{S} on output 8, \mathrm{S} on output 9, \mathrm{S}^\dagger on output 10, \mathrm{S}^\dagger on output 11, \mathrm{S}^\dagger on output 12, \mathrm{S}^\dagger on output 13, \mathrm{S}^\dagger on output 14, \mathrm{S}^\dagger on output 15, \mathrm{S}^\dagger on output 16, \mathrm{S} on output 17, \mathrm{S}^\dagger on output 18, \mathrm{S} on output 19, \mathrm{S} on output 20, \mathrm{S}^\dagger on output 21, \mathrm{S} on output 22, \mathrm{S} on output 23 and 29 more (listed below). The circuit's action is then exactly the gate above, and an error-free run passes every check with certainty.

Or no Clifford gate at all: run \mathrm{T}^3 on rotations 1, 3, 10, 24, 26, 29, 38, 40, 45, 46, 48, 51, 55, 57, 60, 62, 63, 65, 67, 68, 69, 81, 82, 84 and 33 more (listed below); \mathrm{T}^5 on rotations 2, 4, 8, 9, 17, 25, 41, 42, 70, 80, 83; \mathrm{T}^\dagger on rotations 0, 6, 16, 20, 21, 27, 30, 43, 105, and \mathrm{T} on the rest. A power of \mathrm{T} costs one magic state, like \mathrm{T} itself. For a code this is a transversal \mathrm{T} gate, \mathrm{T}^j on each qubit.

Rotations are numbered by matrix column, from 0. The correction comes from the columns by the weight expansion of Bravyi and Haah (2012). verify_catalog.py re-derives it, checks the corrected circuit's phase input by input against the gate, and on small circuits simulates it gate by gate.

A CNOT circuit on the outputs, followed by S, Z and CZ gates, turns this factory's gate into other gates that prepare the same magic state. Type a target gate to check whether it is reachable. If it is, you get the circuit, checked on every basis state.

import json, urllib.request

NAMES = {1: "T", 2: "CS", 3: "CCZ"}


def equivalent_gates(k, terms, limit=1_000_000):
    """Every gate a CNOT circuit and S, Z, CZ gates reach from this one.

    A gate is its set of terms mod Clifford: {a} is T, {a,b} is CS and
    {a,b,c} is CCZ on those outputs. CNOT c->t sends x_t to
    x_c + x_t - 2 x_c x_t, so a term on t but not c adds the term with t
    replaced by c, and, below CCZ, the term with c added.
    """
    start = frozenset(frozenset(m) for m in terms)
    seen, queue = {start}, [start]
    for gate in queue:
        for c in range(k):
            for t in range(k):
                if c == t:
                    continue
                new = set(gate)
                for m in gate:
                    if t in m and c not in m:
                        new ^= {(m - {t}) | {c}}
                        if len(m) < 3:
                            new ^= {m | {c}}
                new = frozenset(new)
                if new not in seen:
                    seen.add(new)
                    queue.append(new)
                    if len(seen) > limit:
                        raise RuntimeError(f"more than {limit} equivalent gates")
    return seen


def name(gate, k):
    sep = "," if k > 10 else ""
    terms = sorted((sorted(m) for m in gate), key=lambda m: (len(m), m))
    return "·".join(NAMES[len(m)] + sep.join(map(str, m)) for m in terms) or "I"


url = "https://shubhamj810.github.io/magic-state-factory-repo/data/factories/160.28.3.a.json"
record = json.load(urllib.request.urlopen(url))
k = record["parameters"]["k"]
gates = equivalent_gates(k, record["gate"]["monomials"])
lines = sorted(name(g, k) for g in gates)
print(len(lines), "equivalent gates")
with open("equivalent-gates-160.28.3.a.txt", "w") as out:
    out.write("\n".join(lines) + "\n")

d=3, exact. No fault of weight below 3 is both undetectable and damaging. One of weight 3 is.

Exactly 318 faults of weight 3 are undetectable and damaging, so A_{3}=318. If each rotation fails independently with probability p, an accepted run has a damaged output with probability A_{3}\,p^{3}+O(p^{4}).

A damaging fault of weight 3 sits on columns [0,34,39].

A fault is a set of faulty columns. It is undetectable when their check parts cancel, and damaging when their output parts do not.

S. P. Jain, A. Wills, and S. Singh, "Symmetry and AI-assisted discovery of magic-state factories," arXiv:2610.06535 (2026). arxiv.org/abs/2610.06535

a paired-column contraction of an existing triorthogonal code, in the pure-T distillation-exponent release (gamma = log(n/k)/log d, n < 1000); a verified witness, not a maximum -- only the release's own frontier points are the lowest gamma it knows

Found by: AI search (gamma frontier): contraction of an existing code

Discovery: AI search

[
 {
  "regime": "AI search (gamma frontier): contraction of an existing code",
  "file": "gamma_frontier_release/protocols/paired_column_contraction_162_28_3_660e895a67eb.json",
  "label": "paired_column_contraction_162_28_3_660e895a67eb",
  "N": 40,
  "d": 3,
  "d_is_exact": true,
  "origin": "Existing triorthogonal code with physical paired-column contraction",
  "provenance": "gamma frontier release record paired_column_contraction_162_28_3_660e895a67eb (source catalogue id paired_column_contraction_162_28_3_660e895a67eb); construction: Existing triorthogonal code with physical paired-column contraction; matrix 660e895a67ebfa48...; the release states d=3 (exact), gamma=1.5978264972671201 (exact); the release matrix has 162 columns of which 2 are a repeated pair -- equal columns cancel to a diagonal Clifford and deposit nothing, so they are deleted here and this circuit is the reduced one, with 160 injections for the same gate and distance",
  "notes": "matrix gamma_frontier_release/matrices/660e895a67ebfa48df361cbb72f17e3b5f29381d6fe99fda59aa271e27d101fa.matrix"
 }
]

Load this factory and re-derive every number from its circuit with the catalogue's own verifier.

# git clone https://github.com/shubhamj810/magic-state-factory-repo
# cd magic-state-factory-repo/master_catalog
import json, urllib.request
import verify_catalog as VC

url = "https://shubhamj810.github.io/magic-state-factory-repo/data/factories/160.28.3.a.json"
record = json.load(urllib.request.urlopen(url))
columns = record["circuit"]["columns"]
k, N = record["parameters"]["k"], record["parameters"]["N"]

facts, problems = VC.derive(columns, k, N)    # gate, T-count, degree, ...
distance = VC.measure_distance(columns, k, N)  # exhaustive fault search
print(facts["gate_human"], distance["d_exact"] or distance["d_at_least"],
      problems or "verified")

Source: master_catalog.json, factories[786].