Variational Quantum Circuits for Quantum State Tomography
This provides a scalable method for quantum state tomography on near-term quantum platforms, addressing a key bottleneck in quantum experiments, though it is incremental as it builds on existing variational approaches.
The paper tackles quantum state tomography by using variational quantum circuits to learn unknown quantum states through fidelity maximization, requiring only polynomial measurements even for entangled states, and demonstrates it via numerical simulations on a quantum spin chain ground state.
Quantum state tomography is a key process in most quantum experiments. In this work, we employ quantum machine learning for state tomography. Given an unknown quantum state, it can be learned by maximizing the fidelity between the output of a variational quantum circuit and this state. The number of parameters of the variational quantum circuit grows linearly with the number of qubits and the circuit depth, so that only polynomial measurements are required, even for highly-entangled states. After that, a subsequent classical circuit simulator is used to transform the information of the target quantum state from the variational quantum circuit into a familiar format. We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator. Our method is suitable for near-term quantum computing platforms, and could be used for relatively large-scale quantum state tomography for experimentally relevant quantum states.