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Experimental single-setting quantum state tomography

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arxiv 2206.00019 v1 pith:D5QOBDVN submitted 2022-05-31 quant-ph

Experimental single-setting quantum state tomography

classification quant-ph
keywords quantumstatetomographyclassicalmeasurementsystemdemonstrateenables
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Quantum computers solve ever more complex tasks using steadily growing system sizes. Characterizing these quantum systems is vital, yet becoming increasingly challenging. The gold-standard is quantum state tomography (QST), capable of fully reconstructing a quantum state without prior knowledge. Measurement and classical computing costs, however, increase exponentially in the system size - a bottleneck given the scale of existing and near-term quantum devices. Here, we demonstrate a scalable and practical QST approach that uses a single measurement setting, namely symmetric informationally complete (SIC) positive operator-valued measures (POVM). We implement these nonorthogonal measurements on an ion trap device by utilizing more energy levels in each ion - without ancilla qubits. More precisely, we locally map the SIC POVM to orthogonal states embedded in a higher-dimensional system, which we read out using repeated in-sequence detections, providing full tomographic information in every shot. Combining this SIC tomography with the recently developed randomized measurement toolbox ("classical shadows") proves to be a powerful combination. SIC tomography alleviates the need for choosing measurement settings at random ("derandomization"), while classical shadows enable the estimation of arbitrary polynomial functions of the density matrix orders of magnitudes faster than standard methods. The latter enables in-depth entanglement studies, which we experimentally showcase on a 5-qubit absolutely maximally entangled (AME) state. Moreover, the fact that the full tomography information is available in every shot enables online QST in real time. We demonstrate this on an 8-qubit entangled state, as well as for fast state identification. All in all, these features single out SIC-based classical shadow estimation as a highly scalable and convenient tool for quantum state characterization.

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Cited by 3 Pith papers

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    Classical light training of photonic quantum reservoirs enables accurate model-free estimation of single-qubit observables and two-qubit entanglement witnesses on unseen quantum states.

  2. Quantum Machine Learning for State Tomography Using Classical Data

    quant-ph 2025-07 unverdicted novelty 6.0

    A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators a...

  3. Connecting Quantum Tomography and Quantum Retrodiction

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