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# Methods
Cost model for all benchmarks: `score = Q² × T` (`Q` = peak qubits,
`T` = Toffoli count), computed by the official verifiers.
---
## I. Inverse square root (`submission/invsqrt.kmb`)
**Score 684,969.09** (89 qubits × 1,670 Toffolis), baseline 2,076,774.23 → −67.0%.
Verifier: PASS, 0 value errors and 0 phase errors over all 65,536 inputs
(`verification/inv_verify_v2.json`, salt `u-ai-006`).
The reference circuit runs a 4-step Newton iteration. This circuit replaces
the last two Newton steps with a **finite-difference tail-polynomial evaluated
at a doubled Newton root**:
1. Two exact small-scale iterations (n_p = 6, 12) seed a doubled root.
2. The tail `T(y) ≈ (1/y)^(-1/2)` correction is evaluated as a
quantized polynomial (optimal shifts 0/4/12, scaled-e₂ recurrence,
4,234 nonzero coefficients), selected by exhaustive screening of
shift/recurrence variants against the exact 24-bit rounding boundary —
the shipped variant (`WC3`) is the only one of 27 shift triples that both
fits the qubit budget and passes all inputs.
3. Coefficients load through a select network into a fixed-point accumulator;
Newton correction uses one multiply by the doubled root.
Classical design validation (`builders/model_direct.py`) reproduces the exact
fixed-point semantics in numpy and confirms all 2¹⁶ inputs before any quantum
build. Regenerate with `builders/build_invsqrt2.py` (previous design retained
in `builders/build_invsqrt.py`).
---
## II. DQI — Reed–Solomon decoding (`submission/dqi.kmb`)
**Score 17,673,729,048,937.30** (1,884 qubits × 4,179,942 Toffolis),
baseline 24,439,463,583,672.93 → −27.7%.
Verifier: PASS with 0 value / 0 phase errors at 102,400 shots
(`verification/dqi_verify_v6.json`, salt `dqi-v6-full`), confirmed on a second
salt at 10,240 shots (`verification/dqi_verify_v6_salt2.json`), on adversarial
maximum-weight full-size inputs, and on small decoders (m=63, n=10) and
(m=255, n=20) with exhaustive error patterns (`builders/test_dqi.py`).
Problem: prepare the DQI state for a [4095, 70] Reed–Solomon-like code —
evaluate error-locator polynomials, run the extended Euclidean algorithm
(EEA) over GF(2¹²), and apply Chien-search rotations, all reversibly.
The decoder structure follows the reference; the win comes from replacing the
field arithmetic:
- **Composite-tower GF(2¹²) inverse (79 Toffolis, was 315 built-in).**
GF(2¹²) = GF(2⁶)[y]/(y²+y+ν), ν = 32 of trace 1; the GF(2⁶) sub-inverse is
itself a GF((2³)²) tower (`builders/gf_inv12.py`: `init_inv6`/`del_inv6`).
Basis changes are applied **in place** with Clifford CNOT/SWAP networks
(`_apply_linear_inplace`), so no separate transform registers are paid for.
- **Generated bilinear multiplies** (`builders/gf_mul12.py`): GF(2¹²) multiply
in 54 products, GF(2⁶) in 18, GF(2³) in 6 (Chudnovsky evaluation form),
each exhaustively verified over all input pairs
(`builders/test_gf_primitives.py`). Controlled multiply: 66 Toffolis
(was 75). Erasure is Toffoli-free (`del_gf2_mul`, measurement-style cleanup).
- **Narrowed EEA degree counters** (`m.bit_length()` bits) and quotient
accumulation directly into the output register — peak qubits 1,914 → 1,884
with no correctness change.
- **Chien σ=0 test**: an 11-Toffoli AND tree erased measurement-style,
replacing two 12-Toffoli `flip_if_equal` passes.
Explored and rejected (details in `DESIGN_NOTES.md`): reversible
Berlekamp–Massey (a clean reversible core needs a ~840-qubit journal whose
Q² penalty exceeds the Toffoli saving; journal-free recovery formulas are
singular-prone), EEA support-based swap/shift pruning (packed simulation
shows nearly all slots live), and reducing EEA iterations/window (provably
near-tight: n=20 fails at 69/72 iterations; window m=n+3 fails outright).
---
## III. DFTHC-SOS (`submission/dfthcsos/`)
**Score 2,309,230,123.93**, baseline 3,355,599,588.32 → −31.2%.
Verifier: PASS (`verification/dfthc_verify.json`).
Double-factorized tensor hypercontraction with a sum-of-squares (SOS)
factorization of the FeMoCo Hamiltonian:
1. The two-electron integrals are factorized by a symmetry-verified SOS
decomposition; spectral certificates (eigenvalue bounds, residual norms)
are checked classically before any circuit is built
(`dfthc_source/certificate.json`, `dfthc_source/numerical_invariants.json`).
2. Each Givens rotation word is compiled to Clifford-only circuits from
permutation/phase tables (`angle_k_0.kmb` … `angle_k_6.kmb`), with
`submission.npz` holding the factorization data.
3. The merged `C28_b10_30` candidate was selected over alternative
factorizations by full verifier runs; per-circuit qubit/Toffoli costs are
in `dfthc_source/circuit_costs.json`, rationale in `dfthc_source/BEST.md`.
---
## Cross-cutting techniques
- **Measurement-style cleanup**: ancillas whose values are classically known
functions of surviving registers are erased with Clifford-only networks
instead of Toffoli uncomputation.
- **Shared-product uncomputation**: bilinear forms share monomial products
between the forward map and its inverse, so erasure reuses computed terms.
- **In-place linear algebra**: invertible GF(2)-linear maps (basis changes,
syndrome transforms) are compiled as CNOT/SWAP networks acting on existing
registers rather than allocating image registers.

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