Quantum computing system model training

A system comprising:

• a quantum computing system comprising:
• a first quantum gate set ansatz configured to perform a first task;
• a second quantum gate set ansatz generated based on the first quantum gate set ansatz, the second quantum gate set ansatz is configured to perform a second task related to the first task;
• and
• quantum hardware, the quantum hardware configured to:
• train parameters of the second quantum gate set ansatz using a first dataset, the training including adjusting electromagnetic waves applied to qubits of quantum hardware according to the second quantum gate set ansatz and the first dataset, and
• train the parameters of the first quantum gate set ansatz using a second dataset related to the first dataset, the training including adjusting electromagnetic waves applied to qubits of quantum hardware according to the first quantum gate set ansatz and the second dataset, the parameters of the first quantum gate set ansatz are initialized based on the trained parameters of the second quantum gate set ansatz.

A non-transitory computer readable media configured to store instructions that when executed by a system perform operations comprising:

• obtaining a first quantum gate set ansatz configured to perform a first task;
• directing generation of a second quantum gate set ansatz using the first quantum gate set ansatz, the second quantum gate set ansatz configured to perform a second task related to the first task;
• directing training of parameters of the second quantum gate set ansatz using a first dataset, the training including adjusting electromagnetic waves applied to qubits of quantum hardware according to the second quantum gate set ansatz and the first dataset;
• directing initialization of parameters of the first quantum gate set ansatz based on the trained parameters of the second quantum gate set ansatz;
• and
• directing training of the parameters of the first quantum gate set ansatz using a second dataset related to the first dataset, the training including adjusting electromagnetic waves applied to qubits of quantum hardware according to the first quantum gate set ansatz and the second dataset, the trained parameters used to adjust qubits of quantum hardware used to generate an output.