jaunatisblue
a96b71a8bc
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57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import time
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import pickle
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import numpy as np
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import quimb.tensor as qtn
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import cotengra as ctg
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def build_qft(n_qubits):
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circ = qtn.Circuit(n_qubits, dtype=np.complex128)
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for i in range(n_qubits):
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circ.apply_gate('H', i)
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for j in range(i + 1, n_qubits):
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circ.apply_gate('CPHASE', np.pi / 2 ** (j - i), i, j)
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return circ
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def run(n_qubits, output="tree.pkl"):
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print(f"Circuit: QFT {n_qubits} qubits")
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circ = build_qft(n_qubits)
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psi = circ.psi
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Z = np.array([[1, 0], [0, -1]], dtype=np.complex128)
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bra = psi.conj().reindex({f'k{i}': f'b{i}' for i in range(n_qubits)})
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obs = qtn.Tensor(Z, inds=(f'k0', f'b0'))
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net = psi & obs & bra
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t0 = time.perf_counter()
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opt = ctg.HyperOptimizer(
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methods=['kahypar'],
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max_repeats=32,
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minimize='combo',
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parallel=8,
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)
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tree = net.contraction_tree(optimize=opt)
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t1 = time.perf_counter()
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print(f"Path search: {t1 - t0:.4f} s")
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print(tree)
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sliced_tree = tree.slice(target_size=2**28)
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print(f"Total slices: {sliced_tree.nslices}")
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arrays = [t.data for t in net.tensors]
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with open(output, "wb") as f:
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pickle.dump({"sliced_tree": sliced_tree, "arrays": arrays}, f)
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print(f"Saved to {output}")
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--n_qubits", type=int, default=18)
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parser.add_argument("--output", type=str, default="tree.pkl")
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args = parser.parse_args()
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run(args.n_qubits, args.output)
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