refactor: rewrite the expectation method using Qibo built in features
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MatteoRobbiati
@@ -198,7 +198,7 @@ class QMatchaTeaBackend(QibotnBackend, NumpyBackend):
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operators = qmatchatea.QCOperators()
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observables = qtealeaves.observables.TNObservables()
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# Add custom observable
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observables += self._qiboobs_to_qmatchaobs(hamiltonian_form=observable)
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observables += self._qiboobs_to_qmatchaobs(hamiltonian=observable)
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results = qmatchatea.run_simulation(
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circ=circuit,
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@@ -225,92 +225,68 @@ class QMatchaTeaBackend(QibotnBackend, NumpyBackend):
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)
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return qiskit_circuit
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def _qiboobs_to_qmatchaobs(
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self, hamiltonian_form, observable_name="custom_hamiltonian"
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):
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"""Convert a Qibo-style symbolic expression (e.g. '2.0*Y2*Z0 + Z0*Z2')
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into a qmatchatea ``TNObsWeightedSum`` observable.
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def _qiboobs_to_qmatchaobs(self, hamiltonian, observable_name="custom_hamiltonian"):
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"""
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Convert a Qibo SymbolicHamiltonian into a qmatchatea TNObsWeightedSum observable.
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The parsing logic here assumes:
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- Each term may have an optional leading coefficient (defaults to 1.0).
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- Each operator is a single-letter from [XYZI] plus a qubit index (e.g., 'X2' means X on qubit 2).
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- Terms are separated by '+' (and optionally '-') signs. If negative, we parse it as a negative coefficient.
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The SymbolicHamiltonian is expected to have a collection of terms, where each term has:
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- `coefficient`: A numeric value.
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- `factors`: A list of factors, each a string such as "X2" or "Z0", representing an operator
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and the qubit it acts on.
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Args:
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hamiltonian_form: e.g. 'Y2*Z0 + 2.5*Z0*Z2'
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observable_name (str): A name for the resulting ``TNObsWeightedSum``.
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hamiltonian (qibo.SymbolicHamiltonian): The symbolic Hamiltonian containing the terms.
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observable_name (str): The name for the resulting TNObsWeightedSum observable.
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Returns:
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TNObsWeightedSum: An observable suitable for qmatchatea.
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TNObsWeightedSum: An observable suitable for use with qmatchatea.
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"""
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hamiltonian_form = str(hamiltonian_form)
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# Collect all the simple terms in the string and preserve the sign
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# whenever a coefficient is negative
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hamiltonian_form = hamiltonian_form.replace("-", "+-")
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raw_terms = [t.strip() for t in hamiltonian_form.split("+") if t.strip()]
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coeff_list = []
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tensor_product_obs = None
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# Regex for leading coefficient: e.g. "2.5*" or "-0.3*"
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# group(1) will capture the numeric part, group(0) includes the sign if present
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leading_coeff_pattern = re.compile(r"^([+-]?\d+(\.\d+)?)\*")
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# Regex to split an operator factor (e.g., "X2" -> operator "X", qubit 2)
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factor_pattern = re.compile(r"([^\d]+)(\d+)")
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for i, hamiltonian_term in enumerate(raw_terms):
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# Set default coefficient to 1.0
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coeff = 1.0
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# Look for a leading numeric coefficient
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match = leading_coeff_pattern.search(hamiltonian_term)
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# Iterate over each term in the symbolic Hamiltonian
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for i, term in enumerate(hamiltonian.terms):
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# Store the term's coefficient
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coeff_list.append(term.coefficient)
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if match:
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# Parse that coefficient
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coeff = float(match.group(1))
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operator_names = []
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acting_on_qubits = []
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# Remove that portion from the term string so only operators remain
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hamiltonian_term = leading_coeff_pattern.sub(
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"", hamiltonian_term, count=1
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)
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# Now isolate the single terms in the product (if there are more than 1)
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operators_qubits = hamiltonian_term.split("*")
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# Prepare lists for qmatchatea
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operator_names, acting_on_qubits = [], []
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# Each sub-term is e.g. "Y2", so operator = "Y", qubit = 2
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# We assume the operator is the single letter, the rest is the qubit index
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for operator in operators_qubits:
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operator = operator.strip()
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# Use a regex to split the operator and the qubit index
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match = re.match(r"([^\d]+)(\d+)", operator)
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# Process each factor in the term
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for factor in term.factors:
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# Assume each factor is a string like "Y2" or "Z0"
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match = factor_pattern.match(str(factor))
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if match:
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operator_name = match.group(
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1
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) # All characters before the number (e.g., 'XYZ')
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qubit_index = int(match.group(2)) # The number part (e.g., 2)
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operator_name = match.group(1)
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qubit_index = int(match.group(2))
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operator_names.append(operator_name)
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acting_on_qubits.append([qubit_index])
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else:
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raise ValueError(
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f"Factor '{str(factor)}' does not match the expected format."
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)
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operator_names.append(operator_name)
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acting_on_qubits.append([qubit_index])
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# Create a TNObsTensorProduct for this term.
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term_tensor_prod = qtealeaves.observables.TNObsTensorProduct(
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name=f"term_{i}",
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operators=operator_names,
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sites=acting_on_qubits,
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)
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# Build collection of tensor product operators (tpo)
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if i == 0:
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tpo = qtealeaves.observables.TNObsTensorProduct(
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name=f"{hamiltonian_term}",
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operators=operator_names,
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sites=acting_on_qubits,
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)
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# Combine tensor products from each term
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if tensor_product_obs is None:
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tensor_product_obs = term_tensor_prod
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else:
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tpo += qtealeaves.observables.TNObsTensorProduct(
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name=f"{hamiltonian_term}",
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operators=operator_names,
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sites=acting_on_qubits,
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)
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# And also keep track of coefficients
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coeff_list.append(coeff)
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tensor_product_obs += term_tensor_prod
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# Combine everything into a WeightedSum
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# Combine all terms into a weighted sum observable
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obs_sum = qtealeaves.observables.TNObsWeightedSum(
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name=observable_name, tp_operators=tpo, coeffs=coeff_list, use_itpo=False
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name=observable_name,
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tp_operators=tensor_product_obs,
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coeffs=coeff_list,
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use_itpo=False,
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)
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return obs_sum
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