hybridq
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HybridQ is a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to simulate large scale quantum circuits on a variety of hardware. Hybr...
At the moment, QPU layouts are hard embedded in `hybridq.architecture`. To improve maintainability, use corresponding `API`s to retrieve the correct layout.
At the moment, most of the objects in **HybridQ** only implement `__str__`, while `__repr__` simply calls `__str__`. However, `__repr__` should return an unambiguous representation of the object while `__str__` should...
When `simulation.simulate(circuit=circuit, simplify=True, remove_id_gates=False)`, if the number of qubits changes (that is, some qubits in `circuit` are removed after the simplification), **HybridQ** returns an error. At the moment, `simulation.simulate(circuit=circuit, simplify=True,...
Once the Kraus sampling is more developed, we might want to come up with a neater way for the user to take multiple samples. Currently one would just call ``simulate``...
Example running generated qasm on QC. By: 1. Running circuit.to_qasm() 2. Using the generated qasm with the IBM quantum computer.
Building off of #30, consider QC-specific optimization
Create simulate backend to implement logic required to run a circuit on a specified QC. Builds off of examples: #26, #27, #29