refactor QuantumNetwork to support general quantum networks
#1244
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Canoming
added
the
quantum_info module
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
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The build of the |
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## master #1244 +/- ##
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Coverage 99.81% 99.82%
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Files 72 72
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Thanks @Canoming. All the methods seem to be implemented correctly.
Regarding the documentation, I have noticed that the new classes QuantumComb and QuantumChannel need to be added to qibo.srt so that they appear on the website. The file README.md is not included in any toctree. I think the best place to add it is in advancedexamples.rst.
I only have a few minor suggestions at the code level:
| """Quantum comb is a quantum network such that the systems follows a sequential order. | ||
| It is also called the *non-Markovian quantum process* in many literatures. | ||
| Specifically, a quantum comb is a quantum network of the form :math:`J[┍i1┑,┕o1┙,┍i2┑,┕o2┙, ...]`, | ||
| where the the process first take an input state from system :math:`i1`, then output a state to system :math:`o1`, and so on. |
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| where the the process first take an input state from system :math:`i1`, then output a state to system :math:`o1`, and so on. | |
| where the process first take an input state from system :math:`i1`, then output a state to system :math:`o1`, and so on. |
| tensors = [ | ||
| operand.full() if operand.is_pure() else operand._tensor for operand in operands | ||
| ] |
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Tests are failing with cupy and cuquantum due to this line. np.einsum_path() is not implemented in some backends, so I think the simplest workaround is to cast to numpy.
| tensors = [ | |
| operand.full() if operand.is_pure() else operand._tensor for operand in operands | |
| ] | |
| tensors = [ | |
| backend.to_numpy(operand.full()) if operand.is_pure() else backend.to_numpy(operand._tensor) for operand in operands | |
| ] |
| QuantumChannel(matrix, partition=(2, 2, 2), pure=True, backend=backend) | ||
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| with pytest.raises(TypeError): | ||
| link_product(1, quantum_comb) |
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| link_product(1, quantum_comb) | |
| link_product(1, quantum_comb, backend=backend) |
| unitary_channel2, | ||
| trace(2, backend=backend), | ||
| trace(2, backend=backend), | ||
| ) |
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| ) | |
| backend=backend, | |
| ) |
| atol=1e-8, | ||
| ) | ||
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| traced = link_product("ij,j", id, tr) |
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| traced = link_product("ij,j", id, tr) | |
| traced = link_product("ij,j", id, tr, backend=backend) |
| link_product("ii", quantum_channel) | ||
| link_product("ij, kj", network_state, quantum_channel) | ||
| link_product("ij, jj", network_state, quantum_channel) | ||
| link_product("ij, jj, jj", network_state, quantum_channel, quantum_channel) |
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| link_product("ii", quantum_channel) | |
| link_product("ij, kj", network_state, quantum_channel) | |
| link_product("ij, jj", network_state, quantum_channel) | |
| link_product("ij, jj, jj", network_state, quantum_channel, quantum_channel) | |
| link_product("ii", quantum_channel, backend=backend) | |
| link_product("ij, kj", network_state, quantum_channel, backend=backend) | |
| link_product("ij, jj", network_state, quantum_channel, backend=backend) | |
| link_product("ij, jj, jj", network_state, quantum_channel, quantum_channel, backend=backend) |
| backend=backend, | ||
| ) | ||
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| non_comb = link_product("ij kl, km on -> jl mn", non_channel, unitary_channel) |
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| non_comb = link_product("ij kl, km on -> jl mn", non_channel, unitary_channel) | |
| non_comb = link_product("ij kl, km on -> jl mn", non_channel, unitary_channel, backend=backend) |
| link_product(1, quantum_comb) | ||
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| with pytest.raises(TypeError): | ||
| link_product("ij, i", quantum_comb, matrix) |
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| link_product("ij, i", quantum_comb, matrix) | |
| link_product("ij, i", quantum_comb, matrix, backend=backend) |
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@Canoming I'm sorry for my absence. I can't review this PR this week but I can next week. |
Checklist:
I've refactored the
QuantumNetworksto support general quantum networks and make it more compatible with usual tensor contraction methods.To make things clear, I made a class diagram for the future development of the module