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Register a new userGeneralized overlap quantum state tomography
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We propose and experimentally demonstrate a quantum state tomography protocol that generalizes the Wallentowitz-Vogel-Banaszek-Wodkiewicz point-by-point Wigner function reconstruction. The full density operator of an arbitrary quantum state is efficiently reconstructed in the Fock basis, using semidefinite programming, after interference with a small set of calibrated coherent states. This new protocol is resource- and computationally efficient, is robust against noise, does not rely on approximate state displacements, and ensures the physicality of results.
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Quantum states and the modes of the optical field they occupy are intrinsically connected. Here, we show that one can trade the knowledge of a quantum state to gain information about the underlying mode structure and, vice versa, the knowledge about the modal shape allows one to perform a complete tomography of the quantum state. Our scheme can be executed experimentally using the interference between the signal and probe states on an unbalanced beam splitter with a single on/off-type detector. By changing the temporal overlap between the signal and the probe, the imperfect interference is turned into a powerful tool to extract the information about the signal mode structure. A single on/off detector is already sufficient to collect the necessary measurement data for the reconstruction of the diagonal part of the density matrix of an arbitrary multi-mode signal. Moreover, we experimentally demonstrate the feasibility of our scheme with just one control parameter -- the time-delay of a coherent probe field.
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.
We develop a practical quantum tomography protocol and implement measurements of pure states of ququarts realized with polarization states of photon pairs (biphotons). The method is based on an optimal choice of the measuring schemes parameters that provides better quality of reconstruction for the fixed set of statistical data. A high accuracy of the state reconstruction (above 0.99) indicates that developed methodology is adequate.
The exact reconstruction of many-body quantum systems is one of the major challenges in modern physics, because it is impractical to overcome the exponential complexity problem brought by high-dimensional quantum many-body systems. Recently, machine learning techniques are well used to promote quantum information research and quantum state tomography has been also developed by neural network generative models. We propose a quantum state tomography method, which is based on Bidirectional Gated Recurrent Unit neural network (BiGRU), to learn and reconstruct both easy quantum states and hard quantum states in this paper. We are able to use fewer measurement samples in our method to reconstruct these quantum states and obtain high fidelity.
Full quantum state tomography is used to characterize the state of an ensemble based qubit implemented through two hyperfine levels in Pr3+ ions, doped into a Y2SiO5 crystal. We experimentally verify that single-qubit rotation errors due to inhomogeneities of the ensemble can be suppressed using the Roos-Moelmer dark state scheme. Fidelities above >90%, presumably limited by excited state decoherence, were achieved. Although not explicitly taken care of in the Roos-Moelmer scheme, it appears that also decoherence due to inhomogeneous broadening on the hyperfine transition is largely suppressed.
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