Year: 2023
Type: preprint
Abstract: Advancements in quantum system lifetimes and control have enabled the creation of increasingly complex quantum states, such as those on multiple bosonic cavity modes. When characterizing these states, traditional tomography scales exponentially in both computational and experimental measurement requirement, which becomes prohibitive as the state size increases. Here, we implement a state reconstruction method whose sampling requirement instead scales polynomially with subspace size, and thus mode number, for states that can be expressed within such a subspace. We demonstrate this improved scaling with Wigner tomography of multimode entangled W states of up to 4 modes on a 3D circuit quantum electrodynamics (cQED) system. This approach performs similarly in efficiency to existing matrix inversion methods for 2 modes, and demonstrates a noticeable improvement for 3 and 4 modes, with even greater theoretical gains at higher mode numbers.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, David and Lucile Packard Foundation, U.S. Department of Energy, University of Chicago, Samsung Advanced Institute of Technology, Samsung, Office of Science, Multidisciplinary University Research Initiative, Division of Materials Research, Division of Electrical, Communications and Cyber Systems, Air Force Office of Scientific Research, Army Research Office, Air Force Research Laboratory, NTT Research
Awards: Materials Research Science and Engineering Centers, G1276186940, G1404049065, G1432373144, G1523888516, G165179637, G1770223817, G209774301, G2598085392, G2867976162, G2882288540, G3068461594, G3162985271, G336680401, G3392305090, G3663418429, G3722242190, G431725244, G4516736450, QuIC - TaQS: Interconnects for a Superconducting-Atomic Hybrid Quantum Network, G4876042962, G5298762856, G5385673730, G5659690140, NNCI: Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource, G6167497367, G6518383159, G6526646931, G657448715, G7452299184, NSF Engineering Research Center for Quantum Networks (CQN), G7900882, G8183070802, G818377809, G8289759875, G837711380, Collaborative Research: EPiQC: Enabling Practical-Scale Quantum Computation, QII-TAQS: Quantum Metrological Platform for Single-Molecule Bio-Sensing
OpenAlex ID: W4386907233