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Merge branch 'master' of github.com:SexyCarrots/tensorcircuit-dev into pr34
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docs/source/tutorials/vqe_h2o.ipynb

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"id": "4009fbd4",
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"metadata": {},
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"source": [
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"# Variational Quantum Eigensolver (VQE) on molecules"
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"# Variational Quantum Eigensolver (VQE) on Molecules"
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]
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"id": "f9c6c84e",
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"metadata": {},
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"source": [
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"We can use binary code to save two further qubits, as the number of spin up and spin down filling is both 5 (5/odd electrons in 7 obitals)"
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"We can use binary code to save two further qubits, as the number of spin up and spin down filling is both 5 (5/odd electrons in 7 orbitals)"
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}
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],
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"source": [
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"print(b.terms[((0, \"Z\"),)]) # coefficient of Z_0 Paulistring"
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"print(b.terms[((0, \"Z\"),)]) # coefficient of Z_0 Pauli-string"
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]
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{
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"cell_type": "markdown",
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"id": "4ecd89c0",
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"metadata": {},
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"source": [
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"* Transform the qubit Hamiltoian in openfermion to the format in tensorcircuit"
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"* Transform the qubit Hamiltonian in openfermion to the format in TensorCircuit"
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"id": "7b43d5c1",
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"metadata": {},
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"source": [
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"The corrsponding Hartree Fock product state in these two types of Hamiltonians"
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"The corresponding Hartree Fock product state in these two types of Hamiltonian"
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"id": "994d944b",
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"metadata": {},
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"source": [
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"## VQE setup\n",
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"## VQE Setup\n",
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"\n",
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"We can in princinple evaluate each Pauli string of the Hamiltonian as an expectation measurment, but it costs lots of simulation time, instead we fuse them together as a Hamiltonian matrix as shown above to run the VQE."
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"We can in principle evaluate each Pauli string of the Hamiltonian as an expectation measurement, but it costs lots of simulation time, instead we fuse them as a Hamiltonian matrix as shown above to run the VQE."
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]
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{
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"id": "d8847fe3",
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"metadata": {},
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"source": [
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"A micro-benchmark between sparse matrix evaluation and dense matrix evaluation for expectation in terms of time, sparse always win in terms of space, of course."
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"A micro-benchmark between sparse matrix evaluation and dense matrix evaluation for expectation in terms of time, sparse always wins in terms of space, of course."
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]
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{
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"id": "3eafaa67",
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"metadata": {},
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"source": [
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"Therefore, sparse matrix evaluation also saves time apart from space, which is always recommeded."
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"Therefore, sparse matrix evaluation also saves time apart from space, which is always recommended."
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

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