Year: 2022
Type: article
Abstract: 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 method for this task is quantum state tomography (QST), capable of fully reconstructing a quantum state without prior knowledge.The measurement and classical computing costs, however, increase exponentially with the number of constituents (e.g., qubits)-a daunting bottleneck given the scale of existing and nearterm quantum devices.Here, we demonstrate a scalable and practical QST approach that only uses a single measurement setting, namely symmetric informationally complete (SIC) positive operator-valued measures (POVMs).We implement these nonorthogonal measurements on an ion trap quantum processor by utilizing additional energy levels within each ion-without requiring ancillary ions to assist in measurements.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, thereby 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 characterization, which we experimentally showcase on a five-qubit absolutely maximally entangled state.Moreover, the fact that the full tomography information is available in every shot enables online QST in real time (i.e., while the experiment is running).We demonstrate this on an eight-qubit entangled state (which has 2 8 • 2 8 -1 = 65 535 degrees of freedom), 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.
Source: PRX Quantum
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,450
Funders: Office of the Director of National Intelligence, European Commission, Austrian Science Fund, Österreichische Forschungsförderungsgesellschaft, Department of Industry, Innovation and Science, Australian Government, U.S. Air Force, Horizon 2020 Framework Programme, Intelligence Advanced Research Projects Activity, NIH Office of the Director, Air Force Office of Scientific Research, Army Research Office
Awards: G104788807, G1427311622, G1432373144, G1523888516, G256775066, Advanced quantum computing with trapped ions, G3065493051, G3105035837, G3165068977, G3216352777, G3392305090, G3478216141, G355744640, G434337789, G4516736450, G4713038487, G4934466994, G4937468798, G495187929, G657448715, G7090116110, G7452299184, G7463219521, G8225529543, G8278177200, Quantum Information Processing with Trapped Ion Qudits, G8722205058, G8823967694
OpenAlex ID: W4307054409