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[Quantum Intelligent Virtual Machine (QIVM)]
From the establishment of the Xanadu battery simulation model in the next chapter, we can generalize the model and derive the requirements for a QIVM1.0 version. You can use Python to program the following functions: (1) Quantum simulation submachine. How to use QML's various quantum algorithms to speed up building battery simulation models. Including Topic 4: Quantum Machine Learning, (1.1) Subtopic 11: kernel methods in the Quantum Intelligence Association’s website teaching. (1.2) Subtopic13: coherent learning process (including quantum phase estimation); and (1.3) Subtopic14: quantum matrix inversion. (2) I/O submachine and knowledge/semantic submachine. When running battery simulation models, quantized GPT can be used to reduce the cost of running battery simulation models. (3) Other AIVM sub-machines, such as abstract sub-machine and basic sub-machine, are quantized.
(As for the future version of QIVM 2.0, it will also include (4) psychological and emotional models in the quantum brain, and (5) consciousness, wisdom, and creative models.)
1 QIVM quantum simulation submachine
2 QIVM GPT slave
According to the 2020 paper "word2ket: Space-efficient Word Embeddings Inspired by Quantum Entanglement" by Panahi et al., and its open source code is released at https://github.com/panaali/word2ket, we can analyze it based on this.
3 QIVM abstract submachine
QIVM abstract sub-machine contains various quantum mechanical theories (thermodynamics, quantum superposition, quantum entanglement) and is consistent with the system theory or principle of AIVM.
4 QIVM basic slave machine
The basic sub-machine of QIVM is the quantum supply chain theory (that is, the basic sub-machine of AIVM).
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