Tenpy & Quimb & Tensornetwork transformation

examples/tenpy_sz_convention.py

@@ -0,0 +1,163 @@
+"""
+Demonstrates the different internal basis conventions between
+TeNPy's TFIChain and XXZChain models and showcases a robust
+method for handling these inconsistencies when converting to TensorCircuit.
+
+1.  TFIChain: Shows a direct conversion works perfectly.
+2.  XXZChain:
+    a. Runs DMRG to obtain a non-trivial ground state.
+    b. Shows that direct conversion leads to incorrect expectation values for correlation functions.
+    c. Demonstrates that applying a layer of X-gates in TensorCircuit
+3.  Tensor Dissection: Provides definitive proof of the differing internal basis conventions between the two models.
+"""
+
+import numpy as np
+from tenpy.networks.mps import MPS
+from tenpy.models.tf_ising import TFIChain
+from tenpy.models.xxz_chain import XXZChain
+from tenpy.algorithms import dmrg
+
+import tensorcircuit as tc
+
+print("Scenario 1: Antiferromagnetic State (TFIChain)")
+L = 10
+afm_model_params = {"L": L, "bc_MPS": "finite", "J": 1.0, "g": 0.0, "conserve": None}
+afm_M = TFIChain(afm_model_params)
+afm_state = ["up", "down"] * (L // 2)
+print(f"Testing with a simple product state: {afm_state}")
+
+psi_afm = MPS.from_product_state(afm_M.lat.mps_sites(), afm_state, bc=afm_M.lat.bc_MPS)
+tc_afm_state = tc.quantum.tenpy2qop(psi_afm)
+circuit_afm = tc.MPSCircuit(L, wavefunction=tc_afm_state)
+
+mag_z_afm_tenpy = psi_afm.expectation_value("Sz")
+mag_z_afm_tc = np.array(
+    [
+        tc.backend.numpy(tc.backend.real(circuit_afm.expectation((tc.gates.z(), [i]))))
+        / 2.0
+        for i in range(L)
+    ]
+)
+print("\nAntiferromagnetic state site-by-site magnetization comparison:")
+print("TeNPy:", np.round(mag_z_afm_tenpy, 8))
+print("TC:   ", np.round(mag_z_afm_tc, 8))
+np.testing.assert_allclose(mag_z_afm_tenpy, mag_z_afm_tc, atol=1e-5)
+print(
+    "\n[SUCCESS] TFI-based Antiferromagnetic state matches perfectly with the pure converter."
+)
+
+
+print("Scenario 2: XXZChain Model")
+xxz_model_params = {"L": L, "bc_MPS": "finite", "Jxx": 1.0, "Jz": 0.5, "hz": 0.1}
+xxz_M = XXZChain(xxz_model_params)
+example_state = ["up", "down", "up", "up", "down", "down", "up", "down", "down", "up"]
+print(f"Testing with a random product state: {example_state}")
+psi_rand_xxz = MPS.from_product_state(
+    xxz_M.lat.mps_sites(), example_state, bc=xxz_M.lat.bc_MPS
+)
+tc_rand_xxz_state = tc.quantum.tenpy2qop(psi_rand_xxz)
+circuit_rand_xxz = tc.MPSCircuit(L, wavefunction=tc_rand_xxz_state)
+mag_z_rand_xxz_tenpy = psi_rand_xxz.expectation_value("Sz")
+mag_z_rand_xxz_tc = np.array(
+    [
+        tc.backend.numpy(
+            tc.backend.real(circuit_rand_xxz.expectation((tc.gates.z(), [i])))
+        )
+        / 2.0
+        for i in range(L)
+    ]
+)
+print("\nXXZ-based random state site-by-site magnetization comparison:")
+print("TeNPy:", np.round(mag_z_rand_xxz_tenpy, 8))
+print("TC:   ", np.round(mag_z_rand_xxz_tc, 8))
+try:
+    np.testing.assert_allclose(mag_z_rand_xxz_tenpy, mag_z_rand_xxz_tc, atol=1e-5)
+except AssertionError as e:
+    print("\n[SUCCESS] As expected, the direct comparison fails for XXZChain.")
+    print(
+        "This is because the pure converter does not handle its inverted basis convention."
+    )
+    print("\nVerifying that the values match after correcting the sign:")
+    np.testing.assert_allclose(mag_z_rand_xxz_tenpy, -mag_z_rand_xxz_tc, atol=1e-5)
+    print(
+        "[SUCCESS] Test passes after applying the sign correction for the XXZChain model."
+    )
+
+
+print("Scenario 3: Tensor Dissection for Both Models")
+simple_L = 2
+simple_labels = ["up", "down"]
+print("\nDissecting TFIChain-based Tensors")
+sites_tfi = afm_M.lat.mps_sites()[:simple_L]
+psi_simple_tfi = MPS.from_product_state(sites_tfi, simple_labels, bc="finite")
+for i, label in enumerate(simple_labels):
+    B_tensor = psi_simple_tfi.get_B(i).to_ndarray()
+    print(
+        f"For '{label}', TFIChain internal tensor has non-zero at physical index {np.where(B_tensor[0,:,0] != 0)[0][0]}"
+    )
+print("\nDissecting XXZChain-based Tensors")
+sites_xxz = xxz_M.lat.mps_sites()[:simple_L]
+psi_simple_xxz = MPS.from_product_state(sites_xxz, simple_labels, bc="finite")
+for i, label in enumerate(simple_labels):
+    B_tensor = psi_simple_xxz.get_B(i).to_ndarray()
+    print(
+        f"For '{label}', XXZChain internal tensor has non-zero at physical index {np.where(B_tensor[0,:,0] != 0)[0][0]}"
+    )
+print("\n Conclusion")
+print("The dissection above shows the root cause of the different behaviors:")
+print(
+    "  - TFIChain's 'up' maps to index 0, 'down' to index 1. This matches TC's standard."
+)
+print(
+    "  - XXZChain's 'up' maps to index 1, 'down' to index 0. This is INVERTED from TC's standard."
+)
+print("\nTherefore, a single, universal converter is not feasible without context.")
+print(
+    "The robust solution is to use a pure converter and apply corrections on a case-by-case basis,"
+)
+print("or to create model-specific converters.")
+
+
+print("--- Scenario 3: Correcting XXZChain DMRG state with X-gates ---")
+
+L = 10
+xxz_model_params = {"L": L, "bc_MPS": "finite", "Jxx": 1.0, "Jz": 1.0, "conserve": None}
+xxz_M = XXZChain(xxz_model_params)
+psi0_xxz = MPS.from_product_state(
+    xxz_M.lat.mps_sites(), ["up", "down"] * (L // 2), bc=xxz_M.lat.bc_MPS
+)
+dmrg_params = {"max_sweeps": 10, "trunc_params": {"chi_max": 64}}
+eng = dmrg.TwoSiteDMRGEngine(psi0_xxz, xxz_M, dmrg_params)
+E, psi_gs_xxz = eng.run()
+print(f"XXZ DMRG finished. Ground state energy: {E:.10f}")
+
+state_raw_quvector = tc.quantum.tenpy2qop(psi_gs_xxz)
+
+i, j = L // 2 - 1, L // 2
+corr_tenpy = psi_gs_xxz.correlation_function("Sz", "Sz", sites1=[i], sites2=[j])[0, 0]
+print("\nApplying X-gate to each qubit to correct the basis convention...")
+circuit_to_be_corrected = tc.MPSCircuit(L, wavefunction=state_raw_quvector)
+
+for k in range(L):
+    circuit_to_be_corrected.x(k)
+
+corr_tc_corrected = (
+    tc.backend.real(
+        circuit_to_be_corrected.expectation((tc.gates.z(), [i]), (tc.gates.z(), [j]))
+    )
+    / 4.0
+)
+
+print(f"\nComparing <Sz_{i}Sz_{j}> correlation function for the DMRG ground state:")
+print(f"TeNPy (Ground Truth):         {corr_tenpy:.8f}")
+print(f"TC (after X-gate correction): {corr_tc_corrected:.8f}")
+np.testing.assert_allclose(corr_tenpy, corr_tc_corrected, atol=1e-5)
+print(
+    "\n[SUCCESS] The correlation functions match perfectly after applying the X-gate correction."
+)
+print(
+    "This demonstrates the recommended physical approach to handle the XXZChain's inverted basis convention."
+)
+
+
+print("\n\nWorkflow demonstration and analysis complete!")

requirements/requirements-extra.txt

@@ -5,6 +5,8 @@ qiskit-nature
 mitiq
 cirq
 torch==2.2.2
+physics-tenpy==1.0.6
+quimb==1.11.1
 # jupyter
 mthree==1.1.0
 openfermion