jaunatisblue
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57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
"""Expectation value benchmark with timing breakdown."""
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import time
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import numpy as np
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import torch
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import qibo
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from qibo import Circuit, gates, hamiltonians
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from qibo.symbols import X, Z
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import quimb as qu
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from qibotn.parallel import parallel_path_search
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# Config
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NQUBITS = 25
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NLAYERS = 10
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WORKERS = 96
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# Build circuit
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np.random.seed(42)
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circuit = Circuit(NQUBITS)
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for _ in range(NLAYERS):
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for q in range(NQUBITS):
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circuit.add(gates.RY(q, theta=np.random.uniform(0, 2*np.pi)))
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circuit.add(gates.RZ(q, theta=np.random.uniform(0, 2*np.pi)))
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for q in range(NQUBITS):
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circuit.add(gates.CNOT(q % NQUBITS, (q + 1) % NQUBITS))
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# Setup backend
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qibo.set_backend("qibotn", platform="quimb")
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backend = qibo.get_backend()
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backend.configure_tn_simulation(ansatz="tn")
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torch.set_num_threads(WORKERS)
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# Prepare TN
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t0 = time.time()
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qc = backend._qibo_circuit_to_quimb(circuit, backend.circuit_ansatz)
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op = qu.pauli('x') & qu.pauli('z')
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tn = qc.local_expectation(op, (0, 1), rehearse='tn')
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t_prepare = time.time() - t0
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# Search path
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t0 = time.time()
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tree = parallel_path_search(tn, tn.outer_inds(), 'processpool', 1024, 300, WORKERS)
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t_search = time.time() - t0
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# Contract
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t0 = time.time()
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for tensor in tn.tensors:
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tensor._data = torch.from_numpy(np.asarray(tensor._data)).to(torch.complex128)
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val = tn.contract(all, output_inds=(), optimize=tree)
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t_contract = time.time() - t0
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# Results
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print(f"Prepare: {t_prepare:.4f}s")
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print(f"Search: {t_search:.4f}s")
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print(f"Contract: {t_contract:.4f}s")
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print(f"Total: {t_prepare+t_search+t_contract:.4f}s")
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print(f"Expectation: {0.5 * complex(val).real:.10f}")
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