qibotn/tools/vidal_mpi_contest_runner.py

from __future__ import annotations

import argparse
import math
import time

import numpy as np
from mpi4py import MPI
from qibo import Circuit, gates, hamiltonians
from qibo.symbols import X, Y, Z

from qibotn.backends.vidal import VidalBackend


def optional_int(text):
    if isinstance(text, str) and text.lower() in {"none", "null", "inf", "unlimited"}:
        return None
    return int(text)


def optional_float(text):
    if isinstance(text, str) and text.lower() in {"none", "null", "inf", "unlimited"}:
        return None
    return float(text)


def format_optional(value, fmt="g"):
    return "None" if value is None else format(value, fmt)


def set_torch_threads(nthreads):
    try:
        import torch

        torch.set_num_threads(nthreads)
    except Exception:
        pass


def build_circuit(kind, nqubits, nlayers, seed):
    rng = np.random.default_rng(seed)
    circuit = Circuit(nqubits)

    for layer in range(nlayers):
        for q in range(nqubits):
            circuit.add(gates.RY(q, theta=rng.uniform(-math.pi, math.pi)))
            circuit.add(gates.RZ(q, theta=rng.uniform(-math.pi, math.pi)))
            if kind in ("rxx_rzz", "scramble"):
                circuit.add(gates.RX(q, theta=rng.uniform(-math.pi, math.pi)))

        if kind == "reversed_cnot":
            for q in range(0, nqubits - 1, 2):
                circuit.add(gates.CNOT(q + 1, q) if layer % 2 else gates.CNOT(q, q + 1))
            for q in range(1, nqubits - 1, 2):
                circuit.add(gates.CNOT(q + 1, q) if layer % 2 == 0 else gates.CNOT(q, q + 1))
        elif kind == "rxx_rzz":
            for q in range(layer % 2, nqubits - 1, 2):
                circuit.add(gates.RXX(q, q + 1, theta=rng.uniform(-0.9, 0.9)))
                circuit.add(gates.RZZ(q, q + 1, theta=rng.uniform(-0.9, 0.9)))
        elif kind == "scramble":
            for q in range(layer % 2, nqubits - 1, 2):
                circuit.add(gates.RXX(q, q + 1, theta=rng.uniform(-0.8, 0.8)))
                circuit.add(gates.RZZ(q, q + 1, theta=rng.uniform(-0.8, 0.8)))
                if layer % 5 == 4:
                    circuit.add(gates.SWAP(q, q + 1))
        else:
            raise ValueError(f"Unknown circuit kind {kind!r}.")

    return circuit


def ring_xz(nqubits):
    form = 0
    for q in range(nqubits):
        form += 0.5 * X(q) * Z((q + 1) % nqubits)
    return hamiltonians.SymbolicHamiltonian(form=form)


def open_zz(nqubits):
    form = 0
    for q in range(nqubits - 1):
        form += (1.0 / (nqubits - 1)) * Z(q) * Z(q + 1)
    return hamiltonians.SymbolicHamiltonian(form=form)


def range2_xx(nqubits):
    form = 0
    for q in range(nqubits - 2):
        form += (1.0 / (nqubits - 2)) * X(q) * X(q + 2)
    return hamiltonians.SymbolicHamiltonian(form=form)


def dense_observable(nqubits, qubits, seed, dim):
    rng = np.random.default_rng(seed)
    raw = rng.normal(size=(dim, dim)) + 1j * rng.normal(size=(dim, dim))
    matrix = (raw + raw.conj().T) / 2.0
    matrix = matrix / np.linalg.norm(matrix)
    return {"matrix": matrix, "qubits": list(qubits)}


def observables_for_case(nqubits, seed):
    q1 = nqubits // 4
    q2 = nqubits // 2
    q3 = (3 * nqubits) // 4
    last = nqubits - 1

    return [
        ("boundary_ZZ_q1", hamiltonians.SymbolicHamiltonian(form=Z(q1 - 1) * Z(q1))),
        ("boundary_ZZ_q2", hamiltonians.SymbolicHamiltonian(form=Z(q2 - 1) * Z(q2))),
        ("boundary_ZZ_q3", hamiltonians.SymbolicHamiltonian(form=Z(q3 - 1) * Z(q3))),
        (
            "long_Z_5_sites",
            hamiltonians.SymbolicHamiltonian(form=Z(0) * Z(q1) * Z(q2) * Z(q3) * Z(last)),
        ),
        (
            "mixed_XZYZX",
            hamiltonians.SymbolicHamiltonian(form=X(0) * Z(q1) * Y(q2) * Z(q3) * X(last)),
        ),
        ("ring_xz", ring_xz(nqubits)),
        ("open_zz", open_zz(nqubits)),
        ("range2_xx", range2_xx(nqubits)),
        ("complex_iZ0", hamiltonians.SymbolicHamiltonian(form=1.0j * Z(0))),
        ("dense2_mid", dense_observable(nqubits, (q2 - 1, q2), seed + 101, 4)),
        ("dense3_spread", dense_observable(nqubits, (q1, q2, q3), seed + 202, 8)),
    ]


def run_case(args):
    set_torch_threads(args.torch_threads)
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    size = comm.Get_size()

    circuit = build_circuit(args.kind, args.nqubits, args.nlayers, args.seed)
    observables = observables_for_case(args.nqubits, args.seed)
    if args.obs_filter:
        wanted = set(args.obs_filter.split(","))
        observables = [(name, obs) for name, obs in observables if name in wanted]
        if not observables:
            raise ValueError(f"OBS_FILTER matched no observables: {args.obs_filter!r}")

    if rank == 0:
        print("=" * 88, flush=True)
        print(
            "case "
            f"label={args.label} kind={args.kind} ranks={size} "
            f"nqubits={args.nqubits} nlayers={args.nlayers} gates={len(circuit.queue)} "
            f"bond={format_optional(args.bond)} "
            f"cut_ratio={format_optional(args.cut_ratio)} "
            f"torch_threads={args.torch_threads} seed={args.seed} "
            f"obs_filter={args.obs_filter or 'all'}",
            flush=True,
        )
        print(
            "observable value seconds trunc_sum trunc_max status",
            flush=True,
        )

    for obs_name, observable in observables:
        backend = VidalBackend()
        backend.configure_tn_simulation(
            max_bond_dimension=args.bond,
            cut_ratio=args.cut_ratio,
            tensor_module="torch",
            mpi_approach="CT",
            mpi_num_procs=size,
            fallback=False,
        )

        comm.Barrier()
        start = time.perf_counter()
        try:
            value = backend.expectation(
                circuit,
                observable,
                preprocess=True,
                compile_circuit=False,
            )
            status = "ok"
        except Exception as exc:  # pragma: no cover - printed for manual runs
            value = np.nan
            status = type(exc).__name__ + ":" + str(exc).split("\n", 1)[0]
        seconds = time.perf_counter() - start

        if rank == 0:
            print(
                f"{obs_name} {value!r} {seconds:.3f} "
                f"{backend.last_truncation_error:.6e} "
                f"{backend.last_max_truncation_error:.6e} {status}",
                flush=True,
            )


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--label", required=True)
    parser.add_argument("--kind", choices=("reversed_cnot", "rxx_rzz", "scramble"), required=True)
    parser.add_argument("--nqubits", type=int, required=True)
    parser.add_argument("--nlayers", type=int, required=True)
    parser.add_argument("--bond", type=optional_int, required=True)
    parser.add_argument("--cut-ratio", type=optional_float, required=True)
    parser.add_argument("--seed", type=int, required=True)
    parser.add_argument("--torch-threads", type=int, required=True)
    parser.add_argument("--obs-filter", default="")
    run_case(parser.parse_args())


if __name__ == "__main__":
    main()