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# Copyright (C) 2026 SnapKitty Collective
# SPDX-License-Identifier: AGPL-3.0-or-later
"""Classical state-vector simulator for the tensor_roll kernel family.
This is NOT a quantum computer. Every invocation report from this module
carries backend="qsim-classical" and device="cpu (statevector simulator)".
It implements genuine quantum-circuit linear algebra (unitary evolution +
Born-rule measurement) so the encode -> transform -> measure -> reconstruct
pipeline is empirically measurable, including its reconstruction error.
The real CUDA-Q kernels live in cudaq/tensor_roll_kernels.py and the real
Q# operations in qsharp/TensorRoll.qs. Neither can execute on this machine
(no QPU, no CUDA-Q toolchain, no Q# toolchain); see docs/BOUNDARIES.md.
"""
from __future__ import annotations
import time
from typing import Dict, Optional, Tuple
import numpy as np
from .core import QSIM_BACKEND, new_report
MAX_QUBITS = 10 # 2**10 amplitudes; hard cap for honesty about cost
# ---------------------------------------------------------------------------
# Low-level statevector machinery (real linear algebra)
# ---------------------------------------------------------------------------
def _n_qubits_for(size: int) -> int:
n = max(1, int(np.ceil(np.log2(size))))
if n > MAX_QUBITS:
raise ValueError(
f"block of {size} elements needs {n} qubits > cap {MAX_QUBITS}; "
"refusing rather than silently approximating")
return n
def _apply_1q(sv: np.ndarray, U: np.ndarray, qubit: int, n: int) -> np.ndarray:
t = sv.reshape([2] * n)
ax = n - 1 - qubit
t = np.tensordot(U, t, axes=([1], [ax]))
t = np.moveaxis(t, 0, ax)
return t.reshape(2 ** n)
def _apply_cnot(sv: np.ndarray, control: int, target: int, n: int) -> np.ndarray:
idx = np.arange(2 ** n)
mask = ((idx >> control) & 1).astype(bool)
flipped = idx ^ (1 << target)
out = sv.copy()
out[mask] = sv[flipped[mask]]
return out
def _rx(theta: float) -> np.ndarray:
c, s = np.cos(theta / 2), np.sin(theta / 2)
return np.array([[c, -1j * s], [-1j * s, c]], dtype=np.complex128)
def _ry(theta: float) -> np.ndarray:
c, s = np.cos(theta / 2), np.sin(theta / 2)
return np.array([[c, -s], [s, c]], dtype=np.complex128)
def _rz(theta: float) -> np.ndarray:
return np.array([[np.exp(-0.5j * theta), 0], [0, np.exp(0.5j * theta)]],
dtype=np.complex128)
def _measure_counts(sv: np.ndarray, shots: int, seed: int) -> Dict[str, int]:
probs = np.abs(sv) ** 2
probs /= probs.sum()
rng = np.random.default_rng(seed)
draws = rng.choice(len(sv), size=shots, p=probs)
counts: Dict[str, int] = {}
n = int(np.log2(len(sv)))
for d in draws:
key = format(int(d), f"0{n}b")
counts[key] = counts.get(key, 0) + 1
return counts
def _dist_stats(counts: Dict[str, int], shots: int) -> Dict:
p = np.array(sorted(counts.values()), dtype=np.float64) / shots
ent = float(-(p * np.log2(p + 1e-300)).sum())
return {
"n_outcomes": len(counts),
"top_p": float(p.max()) if p.size else 0.0,
"dist_entropy_bits": ent,
}
# ---------------------------------------------------------------------------
# tensor_roll kernel family (simulated)
# ---------------------------------------------------------------------------
def tensor_roll_encode(block: np.ndarray, *, encoding: str = "amplitude",
seed: int = 0) -> Tuple[np.ndarray, Dict]:
"""Encode a tensor block into a simulated n-qubit register.
amplitude: |psi> = sum_i (x_i/||x||) |i>; signs are stored classically
(documented hybrid classical/quantum encoding — amplitude
encoding cannot carry sign in measurement probabilities).
angle: one qubit per element, RY(2*atan(x_i)) rotations.
"""
t0 = time.perf_counter()
W = np.asarray(block, dtype=np.float64)
flat = W.ravel()
mem = float(W.nbytes)
shots = 0
stats = None
if encoding == "amplitude":
n = _n_qubits_for(flat.size)
padded = np.zeros(2 ** n)
padded[:flat.size] = flat
norm = float(np.linalg.norm(padded))
signs = np.sign(padded)
sv = (padded / norm).astype(np.complex128) if norm > 0 else np.zeros(2 ** n, dtype=np.complex128)
if norm == 0:
sv[0] = 1.0
payload = {"sv": sv, "n": n, "norm": norm, "signs": signs,
"shape": W.shape, "encoding": encoding}
mem += float(sv.nbytes)
elif encoding == "angle":
if flat.size > MAX_QUBITS:
raise ValueError(f"angle encoding needs 1 qubit/element: {flat.size} > {MAX_QUBITS}")
n = flat.size
sv = np.ones(2 ** n, dtype=np.complex128) / np.sqrt(2 ** n)
for q, x in enumerate(flat):
sv = _apply_1q(sv, _ry(2 * np.arctan(x)), q, n)
payload = {"sv": sv, "n": n, "shape": W.shape, "encoding": encoding}
mem += float(sv.nbytes)
else:
raise ValueError(f"unknown encoding {encoding!r}")
dt = time.perf_counter() - t0
rep = new_report(backend=QSIM_BACKEND, device="cpu (statevector simulator)",
kernel="tensor_roll_encode",
tensor_dims=[W.shape, (2 ** payload["n"],)], precision="complex128",
exec_time_s=dt, memory_bytes=mem, recursion_depth=0,
shots=shots, measurement_stats=stats,
reconstruction_error=None,
extra={"encoding": encoding, "n_qubits": payload["n"]})
return payload, rep
def tensor_roll_rotate(payload: Dict, *, angles, seed: int = 0) -> Tuple[Dict, Dict]:
"""Apply RY rotations (one angle per qubit) to the register."""
t0 = time.perf_counter()
sv, n = payload["sv"].copy(), payload["n"]
angles = np.asarray(angles, dtype=float)
if angles.size != n:
raise ValueError(f"need {n} angles, got {angles.size}")
for q in range(n):
sv = _apply_1q(sv, _ry(float(angles[q])), q, n)
payload = dict(payload, sv=sv)
dt = time.perf_counter() - t0
rep = new_report(backend=QSIM_BACKEND, device="cpu (statevector simulator)",
kernel="tensor_roll_rotate",
tensor_dims=[(2 ** n,)], precision="complex128",
exec_time_s=dt, memory_bytes=float(sv.nbytes),
recursion_depth=0, reconstruction_error=None)
return payload, rep
def tensor_roll_entangle(payload: Dict, *, seed: int = 0) -> Tuple[Dict, Dict]:
"""CNOT ladder across the register (nearest-neighbour entanglement)."""
t0 = time.perf_counter()
sv, n = payload["sv"].copy(), payload["n"]
for q in range(n - 1):
sv = _apply_cnot(sv, q, q + 1, n)
payload = dict(payload, sv=sv)
dt = time.perf_counter() - t0
rep = new_report(backend=QSIM_BACKEND, device="cpu (statevector simulator)",
kernel="tensor_roll_entangle",
tensor_dims=[(2 ** n,)], precision="complex128",
exec_time_s=dt, memory_bytes=float(sv.nbytes),
recursion_depth=0, reconstruction_error=None)
return payload, rep
def tensor_roll_measure(payload: Dict, *, shots: int = 1024,
seed: int = 0) -> Tuple[Dict, Dict]:
"""Born-rule measurement of the register -> counts."""
t0 = time.perf_counter()
sv, n = payload["sv"], payload["n"]
counts = _measure_counts(sv, shots, seed)
stats = _dist_stats(counts, shots)
payload = dict(payload, counts=counts, shots=shots)
dt = time.perf_counter() - t0
rep = new_report(backend=QSIM_BACKEND, device="cpu (statevector simulator)",
kernel="tensor_roll_measure",
tensor_dims=[(2 ** n,)], precision="complex128",
exec_time_s=dt, memory_bytes=float(sv.nbytes),
recursion_depth=0, shots=shots,
measurement_stats=stats, reconstruction_error=None)
return payload, rep
def tensor_roll_reconstruct(payload: Dict) -> Tuple[np.ndarray, Dict]:
"""Reconstruct the classical block from measurement counts.
Amplitude encoding: x_i_hat = sign_i * sqrt(count_i/shots) * ||x||.
This is a statistical estimator — its error is measured and reported,
never hidden.
"""
t0 = time.perf_counter()
if payload.get("encoding") != "amplitude" or "counts" not in payload:
raise ValueError("reconstruct needs amplitude encoding + measured counts")
n, shots = payload["n"], payload["shots"]
norm, signs, shape = payload["norm"], payload["signs"], payload["shape"]
probs = np.zeros(2 ** n)
for bitstr, c in payload["counts"].items():
probs[int(bitstr, 2)] = c / shots
flat_hat = signs[:np.prod(shape)] * np.sqrt(probs[:int(np.prod(shape))]) * norm
What = flat_hat.reshape(shape)
dt = time.perf_counter() - t0
rep = new_report(backend=QSIM_BACKEND, device="cpu (statevector simulator)",
kernel="tensor_roll_reconstruct",
tensor_dims=[(2 ** n,), tuple(shape)], precision="float64",
exec_time_s=dt, memory_bytes=float(What.nbytes),
recursion_depth=0, shots=shots,
measurement_stats=_dist_stats(payload["counts"], shots),
reconstruction_error=None)
return What, rep
def tensor_roll_recursive(block: np.ndarray, *, depth: int = 2,
shots: int = 1024, seed: int = 0,
encoding: str = "amplitude") -> Tuple[np.ndarray, Dict]:
"""Recursive kernel: at each level, rotate by block-derived angles,
entangle, measure a shrinking qubit subset, record per-level stats."""
t0 = time.perf_counter()
payload, reps = tensor_roll_encode(block, encoding=encoding, seed=seed)
reps_list = [reps]
n = payload["n"]
levels = []
for lvl in range(depth):
lt0 = time.perf_counter()
k = max(1, n - lvl) # shrinking active subset
angles = np.linspace(0.1, 0.9, k) * float(np.mean(np.abs(block)) + 1e-6)
sub = dict(payload)
sv = payload["sv"].copy()
for q in range(k):
sv = _apply_1q(sv, _ry(float(angles[q])), q, n)
for q in range(k - 1):
sv = _apply_cnot(sv, q, q + 1, n)
sub["sv"] = sv
counts = _measure_counts(sv, shots, seed + lvl)
levels.append({"level": lvl, "active_qubits": k,
"stats": _dist_stats(counts, shots)})
reps_list.append(new_report(
backend=QSIM_BACKEND, device="cpu (statevector simulator)",
kernel="tensor_roll_recursive", tensor_dims=[(2 ** n,)],
precision="complex128", exec_time_s=time.perf_counter() - lt0,
memory_bytes=float(sv.nbytes), recursion_depth=lvl + 1,
shots=shots, measurement_stats=levels[-1]["stats"],
reconstruction_error=None))
payload, rep_m = tensor_roll_measure(payload, shots=shots, seed=seed)
What, rep_r = tensor_roll_reconstruct(payload)
dt = time.perf_counter() - t0
from .core import relative_error
err = relative_error(np.asarray(block, dtype=np.float64), What)
summary = new_report(
backend=QSIM_BACKEND, device="cpu (statevector simulator)",
kernel="tensor_roll_recursive[summary]",
tensor_dims=[tuple(np.asarray(block).shape), tuple(What.shape)],
precision="float64", exec_time_s=dt,
memory_bytes=float(np.asarray(block).nbytes + What.nbytes),
recursion_depth=depth, shots=shots,
measurement_stats={"levels": levels},
reconstruction_error=err)
# final measurement counts + qubit count: payload of the real
# process/IR boundary artifact (see tensor_roll/boundary.py)
summary["counts"] = {k: int(v) for k, v in payload["counts"].items()}
summary["n_qubits"] = n
return What, summary
def qsim_encode_for_roll(W: np.ndarray, *, shots: int = 2048,
seed: int = 0):
"""Adapter used by core.evaluate_options: full qsim pipeline on a block,
returning (reconstructed_block, summary_report)."""
return tensor_roll_recursive(np.asarray(W, dtype=np.float64),
depth=1, shots=shots, seed=seed)
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