Fix bug in SymbolicHamiltonian.__mul__

[renatomello]

Checklist:

• Reviewers confirm new code works as expected.
• Tests are passing.
• Coverage does not decrease.
• Documentation is updated.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 99.72%. Comparing base (05b57e9) to head (64f6e2e).
Report is 5 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff           @@
##           master    #1517   +/-   ##
=======================================
  Coverage   99.72%   99.72%           
=======================================
  Files          80       80           
  Lines       11781    11782    +1     
=======================================
+ Hits        11749    11750    +1     
  Misses         32       32           

Flag	Coverage Δ
unittests	99.72% <100.00%> (+<0.01%)	⬆️

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[marekgluza]

Thanks @renatomello

• I confirm that the code now runs
• Can you/I push to this branch a test that will cover this case? I suggest to modify this

qibo/tests/test_hamiltonians_symbolic.py

Lines 162 to 202 in 05b57e9

	def test_symbolic_hamiltonian_operator_add_and_sub(
	backend, nqubits, calcterms, calcdense
	):
	"""Test addition and subtraction between Trotter Hamiltonians."""
	local_ham1 = hamiltonians.SymbolicHamiltonian(
	symbolic_tfim(nqubits, h=1.0), backend=backend
	)
	local_ham2 = hamiltonians.SymbolicHamiltonian(
	symbolic_tfim(nqubits, h=0.5), backend=backend
	)
	if calcterms:
	_ = local_ham1.terms
	_ = local_ham2.terms
	if calcdense:
	_ = local_ham1.dense
	_ = local_ham2.dense
	local_ham = local_ham1 + local_ham2
	target_ham = hamiltonians.TFIM(nqubits, h=1.0, backend=backend) + hamiltonians.TFIM(
	nqubits, h=0.5, backend=backend
	)
	dense = local_ham.dense
	backend.assert_allclose(dense.matrix, target_ham.matrix)
	local_ham1 = hamiltonians.SymbolicHamiltonian(
	symbolic_tfim(nqubits, h=1.0), backend=backend
	)
	local_ham2 = hamiltonians.SymbolicHamiltonian(
	symbolic_tfim(nqubits, h=0.5), backend=backend
	)
	if calcterms:
	_ = local_ham1.terms
	_ = local_ham2.terms
	if calcdense:
	_ = local_ham1.dense
	_ = local_ham2.dense
	local_ham = local_ham1 - local_ham2
	target_ham = hamiltonians.TFIM(nqubits, h=1.0, backend=backend) - hamiltonians.TFIM(
	nqubits, h=0.5, backend=backend
	)
	dense = local_ham.dense
	backend.assert_allclose(dense.matrix, target_ham.matrix)

by multiplying some fiducial numbers - see 1.5 and 0.6 in the next to last lines

def test_symbolic_hamiltonian_operator_add_and_sub(
    backend, nqubits, calcterms, calcdense
):
    """Test addition and subtraction between Trotter Hamiltonians."""
    local_ham1 = hamiltonians.SymbolicHamiltonian(
        symbolic_tfim(nqubits, h=1.0), backend=backend
    )
    local_ham2 = hamiltonians.SymbolicHamiltonian(
        symbolic_tfim(nqubits, h=0.5), backend=backend
    )
    if calcterms:
        _ = local_ham1.terms
        _ = local_ham2.terms
    if calcdense:
        _ = local_ham1.dense
        _ = local_ham2.dense
    local_ham = local_ham1 + local_ham2
    target_ham = hamiltonians.TFIM(nqubits, h=1.0, backend=backend) + hamiltonians.TFIM(
        nqubits, h=0.5, backend=backend
    )
    dense = local_ham.dense
    backend.assert_allclose(dense.matrix, target_ham.matrix)

    local_ham1 = hamiltonians.SymbolicHamiltonian(
        symbolic_tfim(nqubits, h=1.0), backend=backend
    )
    local_ham2 = hamiltonians.SymbolicHamiltonian(
        symbolic_tfim(nqubits, h=0.5), backend=backend
    )
    if calcterms:
        _ = local_ham1.terms
        _ = local_ham2.terms
    if calcdense:
        _ = local_ham1.dense
        _ = local_ham2.dense
    local_ham =1.5 * local_ham1 - 0.6 * local_ham2
    target_ham = 1.5 *hamiltonians.TFIM(nqubits, h=1.0, backend=backend) - 0.6 * hamiltonians.TFIM(
        nqubits, h=0.5, backend=backend
    )
    dense = local_ham.dense
    backend.assert_allclose(dense.matrix, target_ham.matrix)

[renatomello]

@marekgluza I introduced a small example that covers this situation.

[marekgluza]

Nice! Worth also keeping in mind for tutorials even :)