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updaged examples

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This commit is contained in:
Mattia Robbiano
2025-09-23 00:37:11 +02:00
parent 271a44576d
commit 386891ee1a
2 changed files with 106 additions and 163 deletions
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217
examples/quimb_intro/quimb_introduction.ipynb
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@@ -36,7 +36,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"id": "b0a1da82",
"metadata": {},
"outputs": [],
@@ -53,7 +53,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 23,
"id": "64162116-1555-4a68-811c-01593739d622",
"metadata": {},
"outputs": [],
@@ -69,7 +69,7 @@
"\n",
"quimb_backend.setup_backend_specifics(\n",
" qimb_backend=\"jax\", \n",
" optimizer='auto-hq'\n",
" contractions_optimizer=ctg_opt\n",
" )"
]
},
@@ -83,7 +83,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 6,
"id": "4a22a172-f50d-411d-afa3-fa61937c7b3a",
"metadata": {},
"outputs": [],
@@ -102,7 +102,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 7,
"id": "76f23c57-6d08-496b-9a27-52fb63bbfcb1",
"metadata": {},
"outputs": [
@@ -124,7 +124,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 8,
"id": "07b2c097-cea2-42ec-8f1d-b4bbb5b71d98",
"metadata": {},
"outputs": [],
@@ -147,7 +147,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 9,
"id": "2ee03e94-d794-4a51-9e76-01e8d8a259ba",
"metadata": {},
"outputs": [],
@@ -175,7 +175,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 10,
"id": "35a244c3-adba-4b8b-b28c-0ab592b0f7cf",
"metadata": {},
"outputs": [
@@ -196,33 +196,33 @@
"text/plain": [
"{'nqubits': 4,\n",
" 'backend': qibotn (quimb),\n",
" 'measures': Counter({'0011': 8,\n",
" 'measures': Counter({'1000': 14,\n",
" '1011': 9,\n",
" '0101': 5,\n",
" '0010': 12,\n",
" '0111': 4,\n",
" '1011': 7,\n",
" '0000': 8,\n",
" '1110': 14,\n",
" '0101': 4,\n",
" '1010': 4,\n",
" '1000': 14,\n",
" '1111': 8,\n",
" '0100': 6,\n",
" '1101': 8,\n",
" '1100': 1,\n",
" '0110': 2}),\n",
" 'measured_probabilities': {'1110': np.float64(0.07174919872959985),\n",
" '1101': 19,\n",
" '0011': 6,\n",
" '0100': 3,\n",
" '1111': 9,\n",
" '0111': 4,\n",
" '0110': 4,\n",
" '1110': 2,\n",
" '1100': 1}),\n",
" 'measured_probabilities': {'1101': np.float64(0.12331159869893256),\n",
" '1000': np.float64(0.11330883548333587),\n",
" '0010': np.float64(0.09466860481989385),\n",
" '0011': np.float64(0.07571277233522114),\n",
" '0000': np.float64(0.08390937969317269),\n",
" '1111': np.float64(0.10184806171791962),\n",
" '1101': np.float64(0.12331159869893256),\n",
" '1011': np.float64(0.053499396925872744),\n",
" '0100': np.float64(0.07142939529687138),\n",
" '0111': np.float64(0.04029185074729259),\n",
" '1111': np.float64(0.10184806171791962),\n",
" '0000': np.float64(0.08390937969317269),\n",
" '0011': np.float64(0.07571277233522114),\n",
" '0101': np.float64(0.05622305772698622),\n",
" '1010': np.float64(0.03872758515126756),\n",
" '0111': np.float64(0.04029185074729259),\n",
" '0110': np.float64(0.05146064807369214),\n",
" '0100': np.float64(0.07142939529687138),\n",
" '1110': np.float64(0.07174919872959985),\n",
" '1100': np.float64(0.013605984872668404)},\n",
" 'prob_type': 'default',\n",
" 'statevector': Array([[ 0.08809624-0.27594998j],\n",
@@ -243,7 +243,7 @@
" [-0.01729754-0.31866732j]], dtype=complex64)}"
]
},
"execution_count": 8,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -268,7 +268,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 11,
"id": "c0443efc-21ef-4ed5-9cf4-785d204a1881",
"metadata": {},
"outputs": [
@@ -277,7 +277,7 @@
"output_type": "stream",
"text": [
"Probabilities:\n",
" {'1110': np.float64(0.07174919872959985), '1000': np.float64(0.11330883548333587), '0010': np.float64(0.09466860481989385), '0011': np.float64(0.07571277233522114), '0000': np.float64(0.08390937969317269), '1111': np.float64(0.10184806171791962), '1101': np.float64(0.12331159869893256), '1011': np.float64(0.053499396925872744), '0100': np.float64(0.07142939529687138), '0111': np.float64(0.04029185074729259), '0101': np.float64(0.05622305772698622), '1010': np.float64(0.03872758515126756), '0110': np.float64(0.05146064807369214), '1100': np.float64(0.013605984872668404)}\n",
" {'1101': np.float64(0.12331159869893256), '1000': np.float64(0.11330883548333587), '0010': np.float64(0.09466860481989385), '1011': np.float64(0.053499396925872744), '1111': np.float64(0.10184806171791962), '0000': np.float64(0.08390937969317269), '0011': np.float64(0.07571277233522114), '0101': np.float64(0.05622305772698622), '1010': np.float64(0.03872758515126756), '0111': np.float64(0.04029185074729259), '0110': np.float64(0.05146064807369214), '0100': np.float64(0.07142939529687138), '1110': np.float64(0.07174919872959985), '1100': np.float64(0.013605984872668404)}\n",
"\n",
"State:\n",
" [[ 0.08809624-0.27594998j]\n",
@@ -321,7 +321,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 12,
"id": "37385485-e8a3-4ab0-ad44-bcc4e9da24ca",
"metadata": {},
"outputs": [
@@ -348,17 +348,61 @@
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ddecc910-7804-4199-8577-a7db38a16db8",
"execution_count": 20,
"id": "9712783d",
"metadata": {},
"outputs": [],
"source": [
"# define Hamiltonian\n",
"operators = [\"zz\", \"xz\", \"z\"]\n",
"qubits = [\"01\", \"02\", \"3\"]\n",
"coefficients = [\"0.5\", \"-1.5\", \"1\"]\n",
"hamiltonian = (operators, qubits, coefficients)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "163b70a3-814a-4a62-a98a-2ffca933a544",
"metadata": {},
"outputs": [
{
"name": "stderr",
"name": "stdout",
"output_type": "stream",
"text": [
"[Qibo 0.2.20|INFO|2025-09-20 16:43:42]: Using qibojit (numba) backend on /CPU:0\n"
"Expectation value: 0.7143570184707642\n",
"Elapsed time: 0.1498 seconds\n"
]
},
}
],
"source": [
"start = time.time()\n",
"expval = quimb_backend.expectation(\n",
" circuit=circuit,\n",
" operators_list=hamiltonian[0],\n",
" sites_list=hamiltonian[1],\n",
" coeffs_list=hamiltonian[2]\n",
" )\n",
"\n",
"elapsed = time.time() - start\n",
"print(f\"Expectation value: {expval}\")\n",
"print(f\"Elapsed time: {elapsed:.4f} seconds\")"
]
},
{
"cell_type": "markdown",
"id": "90663e28",
"metadata": {},
"source": [
"Try with Qibo (which is by default using the Qibojit backend)\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c8760074",
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
@@ -368,7 +412,7 @@
"-1.5*X0*Z2 + 0.5*Z0*Z1 + Z3"
]
},
"execution_count": 11,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -385,41 +429,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"id": "163b70a3-814a-4a62-a98a-2ffca933a544",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Expectation value: 0.7143489122390747\n",
"Elapsed time: 12.4550 seconds\n"
]
}
],
"source": [
"start = time.time()\n",
"expval = quimb_backend.expectation(\n",
" circuit=circuit,\n",
" observable=hamiltonian,\n",
")\n",
"elapsed = time.time() - start\n",
"print(f\"Expectation value: {expval}\")\n",
"print(f\"Elapsed time: {elapsed:.4f} seconds\")"
]
},
{
"cell_type": "markdown",
"id": "90663e28",
"metadata": {},
"source": [
"Try with Qibo (which is by default using the Qibojit backend)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 19,
"id": "e2d05707",
"metadata": {},
"outputs": [
@@ -428,7 +438,7 @@
"output_type": "stream",
"text": [
"Expectation value: 0.7143570920618565\n",
"Elapsed time: 0.5871 seconds\n"
"Elapsed time: 0.5597 seconds\n"
]
}
],
@@ -458,7 +468,7 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": null,
"id": "8df55c5f",
"metadata": {},
"outputs": [
@@ -474,9 +484,7 @@
}
],
"source": [
"# grad of this circuit returning nan for some reason...\n",
"\n",
"def build_circuit(nqubits, nlayers):\n",
"def build_circuit_A(nqubits, nlayers):\n",
" \"\"\"Construct a parametric quantum circuit.\"\"\"\n",
" circ = Circuit(nqubits)\n",
" for _ in range(nlayers):\n",
@@ -509,7 +517,7 @@
}
],
"source": [
"def build_circuit(nqubits, nlayers):\n",
"def build_circuit_B(nqubits, nlayers):\n",
" circ = Circuit(nqubits)\n",
" for _ in range(nlayers):\n",
" for q in range(nqubits):\n",
@@ -528,31 +536,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b803250f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Array(1.4999985, dtype=float32)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quimb_backend.expectation(\n",
" circuit=circuit, \n",
" observable=hamiltonian,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": null,
"id": "0943482e",
"metadata": {},
"outputs": [
@@ -560,27 +544,38 @@
"name": "stdout",
"output_type": "stream",
"text": [
"(Array(0.4465402, dtype=float32), Array([-1.5755819e-01, 9.7801067e-02, -1.2350259e-01, 1.3670625e-01,\n",
" 3.6954228e-03, -1.7437905e-02, 2.7746204e-01, -1.0357879e-01,\n",
" 1.1504190e-01, -4.5175910e-02, -4.8447326e-02, 1.4743687e-01,\n",
" -3.0708680e-01, 2.0652822e-01, 1.9298886e-01, 5.1306009e-02,\n",
" -3.3362946e-01, -7.5548244e-01, -3.0034758e-02, -5.2868712e-01,\n",
" 4.8458660e-01, -2.9802322e-08, 8.0767423e-02, 0.0000000e+00], dtype=float32))\n"
"[ 2.57090405e-02 -2.24703997e-02 -2.82387018e-01 -1.02654770e-01\n",
" 1.03672206e-01 1.03572123e-01 -9.93297935e-01 3.26367974e-01\n",
" -8.58561993e-01 -3.14284384e-01 -1.22645356e-01 -2.13570029e-01\n",
" -4.55642402e-01 1.11669600e-02 -2.92290837e-01 -1.91316485e-01\n",
" 2.78813928e-01 8.80600572e-01 5.07975101e-01 -1.97107181e-01\n",
" -4.69740361e-01 -8.50831568e-02 4.45045829e-01 3.42172906e-02\n",
" -8.43066633e-01 1.86891228e-01 4.52477366e-01 7.36747682e-03\n",
" -6.28291368e-01 -9.38566178e-02 5.43581992e-02 3.57441790e-02\n",
" 5.15162945e-04 2.55566716e-01 -3.20922613e-01 4.96513635e-01]\n",
"Elapsed time: 11.3724 seconds\n"
]
}
],
"source": [
"import jax\n",
"import time\n",
"\n",
"def f(params):\n",
"def f(circuit, hamiltonian, params):\n",
" circuit.set_parameters(params)\n",
" return quimb_backend.expectation(\n",
" circuit=circuit,\n",
" observable=hamiltonian,\n",
" operators_list=hamiltonian[0],\n",
" sites_list=hamiltonian[1],\n",
" coeffs_list=hamiltonian[2]\n",
" )\n",
"\n",
"parameters = np.random.uniform(-np.pi, np.pi, size=len(circuit.get_parameters()))\n",
"print(jax.value_and_grad(f)(parameters))\n"
"start = time.time()\n",
"grad = jax.grad(f, argnums=2)(circuit, hamiltonian, parameters)\n",
"elapsed = time.time() - start\n",
"print(grad)\n",
"print(f\"Elapsed time: {elapsed:.4f} seconds\")"
]
}
],

52
examples/quimb_intro/test.py
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@@ -1,52 +0,0 @@
import numpy as np
import jax
from qibo.backends import construct_backend
from qibo import Circuit, gates, hamiltonians
from qibo.symbols import Z, X, Y
# construct qibotn backend
quimb_backend = construct_backend(backend="qibotn", platform="quimb")
quimb_backend.setup_backend_specifics(
qimb_backend="jax",
optimizer='auto-hq'
)
quimb_backend.configure_tn_simulation(
max_bond_dimension=10
)
# define Hamiltonian
form = 0.5 * Z(0) * Z(1) +- 1.5 * X(0) * Z(2) + Z(3)
hamiltonian = hamiltonians.SymbolicHamiltonian(form)
# define circuit
def build_circuit(nqubits, nlayers):
"""Construct a more complex Qibo parametric quantum circuit without CNOT gates."""
circ = Circuit(nqubits)
for layer in range(nlayers):
for q in range(nqubits):
circ.add(gates.RY(q=q, theta=0.))
circ.add(gates.RZ(q=q, theta=0.))
circ.add(gates.RX(q=q, theta=0.))
# Add controlled rotations and SWAPs for entanglement
for q in range(nqubits - 1):
circ.add(gates.CNOT(q, q + 1))
circ.add(gates.SWAP(q, q + 1))
circ.add(gates.M(*range(nqubits)))
return circ
nqubits = 6
circuit = build_circuit(nqubits=nqubits, nlayers=3)
def f(params):
circuit.set_parameters(params)
return quimb_backend.expectation(
circuit=circuit,
observable=hamiltonian,
)
parameters = np.random.uniform(-np.pi, np.pi, size=len(circuit.get_parameters()))
print(jax.value_and_grad(f)(parameters))