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-qibo = "^0.2.17" +qibo = { git="https://github.com/qiboteam/qibo", branch="expectation"} quimb = { version = "^1.10.0", extras = ["tensor"] } cupy-cuda11x = { version = "^13.1.0", optional = true } cuquantum-python-cu11 = { version = "^24.1.0", optional = true } diff --git a/src/qibotn/backends/quimb.py b/src/qibotn/backends/quimb.py index 8a11bb9..6e4629b 100644 --- a/src/qibotn/backends/quimb.py +++ b/src/qibotn/backends/quimb.py @@ -1,63 +1,55 @@ from collections import Counter import numpy as np - -from qibo.models import Circuit -from qibo.backends import NumpyBackend -from qibo.config import raise_error -from qibotn.backends.abstract import QibotnBackend -from qibotn.result import TensorNetworkResult -from qibo.gates.abstract import ParametrizedGate - import quimb as qu import quimb.tensor as qtn +from qibo.backends import NumpyBackend +from qibo.config import raise_error +from qibo.gates.abstract import ParametrizedGate +from qibo.models import Circuit +from qibotn.backends.abstract import QibotnBackend +from qibotn.result import TensorNetworkResult GATE_MAP = { - "h": "H", - "x": "X", - "y": "Y", - "z": "Z", - "s": "S", - "t": "T", + "h": "H", + "x": "X", + "y": "Y", + "z": "Z", + "s": "S", + "t": "T", + "rx": "RX", + "ry": "RY", + "rz": "RZ", + "u3": "U3", # TODO: check + "cx": "CX", + "cnot": "CNOT", + "cy": "CY", + "cz": "CZ", + "iswap": "ISWAP", + "swap": "SWAP", + "ccx": "CCX", + "ccy": "CCY", + "ccz": "CCZ", + "toffoli": "TOFFOLI", + "cswap": "CSWAP", + "fredkin": "FREDKIN", + "fsim": "fsim", + "measure": "measure", +} - "rx": "RX", - "ry": "RY", - "rz": "RZ", - - "u3": "U3", # TODO: check - - "cx": "CX", - "cnot": "CNOT", - "cy": "CY", - "cz": "CZ", - - "iswap": "ISWAP", - "swap": "SWAP", - - "ccx": "CCX", - "ccy": "CCY", - "ccz": "CCZ", - - "toffoli": "TOFFOLI", - "cswap": "CSWAP", - "fredkin": "FREDKIN", - - "fsim": "fsim", - - "measure": "measure" - } class QuimbBackend(QibotnBackend, NumpyBackend): - def __init__(self): + def __init__(self, engine="numpy"): super().__init__() self.name = "qibotn" self.platform = "quimb" + self.engine = engine self.configure_tn_simulation() - self.setup_backend_specifics() + self.setup_backend_specifics(quimb_backend=engine) def configure_tn_simulation( self, @@ -71,7 +63,7 @@ class QuimbBackend(QibotnBackend, NumpyBackend): Args: ansatz : str, optional The tensor network ansatz to use. Default is `None` and, in this case, a - generic Circuit Quimb class is used. + generic Circuit Quimb class is used. max_bond_dimension : int, optional The maximum bond dimension for the MPS ansatz. Default is 10. @@ -83,7 +75,9 @@ class QuimbBackend(QibotnBackend, NumpyBackend): self.max_bond_dimension = max_bond_dimension self.n_most_frequent_states = n_most_frequent_states - def setup_backend_specifics(self, qimb_backend="numpy", contractions_optimizer="auto-hq"): + def setup_backend_specifics( + self, quimb_backend="numpy", contractions_optimizer="auto-hq" + ): """Setup backend specifics. Args: qimb_backend: str @@ -91,19 +85,22 @@ class QuimbBackend(QibotnBackend, NumpyBackend): contractions_optimizer: str, optional The contractions_optimizer to use for the quimb tensor network simulation. """ - if qimb_backend == "jax": + if quimb_backend == "jax": import jax.numpy as jnp + self.np = jnp - elif qimb_backend == "numpy": + elif quimb_backend == "numpy": import numpy as np + self.np = np - elif qimb_backend == "torch": + elif quimb_backend == "torch": import torch + self.np = torch else: - raise_error(ValueError, f"Unsupported quimb backend: {qimb_backend}") + raise_error(ValueError, f"Unsupported quimb backend: {quimb_backend}") - self.backend = qimb_backend + self.backend = quimb_backend self.contractions_optimizer = contractions_optimizer def execute_circuit( @@ -180,7 +177,9 @@ class QuimbBackend(QibotnBackend, NumpyBackend): measured_probabilities = None statevector = ( - circ_quimb.to_dense(backend=self.backend, optimize=self.contractions_optimizer) + circ_quimb.to_dense( + backend=self.backend, optimize=self.contractions_optimizer + ) if return_array else None ) @@ -193,14 +192,16 @@ class QuimbBackend(QibotnBackend, NumpyBackend): statevector=statevector, ) - def expectation(self, circuit, operators_list, sites_list, coeffs_list): + def expectation_observable_symbolic_from_state( + self, circuit, operators_list, sites_list, coeffs_list, nqubits + ): """ Compute the expectation value of a symbolic Hamiltonian on a quantum circuit using tensor network contraction. This method takes a Qibo circuit, converts it to a Quimb tensor network circuit, and evaluates the expectation value of a Hamiltonian specified by three lists of strings: operators, sites, and coefficients. The expectation value is computed by summing the contributions from each term in the Hamiltonian, where each term's expectation is calculated using Quimb's `local_expectation` function. - + Parameters ---------- circuit : qibo.models.Circuit @@ -216,27 +217,30 @@ class QuimbBackend(QibotnBackend, NumpyBackend): float The real part of the expectation value of the Hamiltonian on the given circuit state. """ - quimb_circuit = self._qibo_circuit_to_quimb(circuit, quimb_circuit_type=qtn.Circuit) - + quimb_circuit = self._qibo_circuit_to_quimb( + circuit, quimb_circuit_type=qtn.Circuit + ) + expectation_value = 0.0 - for opstr, sitesstr, coeffstr in zip(operators_list, sites_list, coeffs_list): - + for opstr, sites, coeff in zip(operators_list, sites_list, coeffs_list): + ops = self._string_to_quimb_operator(opstr) - coeff = self._parse_coefficient(coeffstr) - sites = tuple(int(q) for q in sitesstr) - + coeff = coeff.real + exp_values = quimb_circuit.local_expectation( ops, where=sites, backend=self.backend, - optimize=self.contractions_optimizer + optimize=self.contractions_optimizer, ) expectation_value = expectation_value + coeff * exp_values - - return np.real(expectation_value) - def _qibo_circuit_to_quimb(self, qibo_circ, quimb_circuit_type=qtn.Circuit, **circuit_kwargs): + return self.np.real(expectation_value) + + def _qibo_circuit_to_quimb( + self, qibo_circ, quimb_circuit_type=qtn.Circuit, **circuit_kwargs + ): """ Convert a Qibo Circuit to a Quimb Circuit. Measurement gates are ignored. If are given gates not supported by Quimb, an error is raised. @@ -267,18 +271,12 @@ class QuimbBackend(QibotnBackend, NumpyBackend): params = getattr(gate, "parameters", ()) qubits = getattr(gate, "qubits", ()) - - is_parametrized = ( - isinstance(gate, ParametrizedGate) - and getattr(gate, "trainable", True) - ) + + is_parametrized = isinstance(gate, ParametrizedGate) and getattr( + gate, "trainable", True + ) if is_parametrized: - circ.apply_gate( - qname, - *params, - *qubits, - parametrized=is_parametrized - ) + circ.apply_gate(qname, *params, *qubits, parametrized=is_parametrized) else: circ.apply_gate( qname, @@ -287,28 +285,6 @@ class QuimbBackend(QibotnBackend, NumpyBackend): ) return circ - def _parse_coefficient(self, s): - """Parse a coefficient from string to float, int, or complex. - Args: - s: str - The string representation of the coefficient. - Returns: - The coefficient as float, int, or complex. - """ - try: - return float(s) - except ValueError: - pass - if s == "j": - return 1j - elif s == "-j": - return -1j - else: - try: - return complex(s) - except ValueError: - raise ValueError(f"Cannot parse coefficient: {s}") - def _string_to_quimb_operator(self, op_str): """ Convert a Pauli string (e.g. 'xzy') to a Quimb operator using '&' chaining.