Program
Full Program | Thursday | Friday | Saturday | All Sessions | Posters | Talks
SESSION 9b: Error correction and estimation (Pavilion I)Chair: Bryan Eastin (Northrop Grumman) | |
| 4:00 pm - 4:30 pm | Joshua Combes, (IQC, Waterloo and Perimeter) Logical randomized benchmarking | Abstract. Randomized Benchmarking is a characterization tool that is insensitive to state preparation and measurement errors. In many situations estimating the fidelity of a logical qubit from the physical fidelity can lead to over or under estimates. In this work we show how performing Randomized Benchmarking on a logical qubit can (i) help provide a more accurate assessment of the quality of the gates and physical channel, (ii) give estimates for the rates of correctable and uncorrectable errors. |
| 4:30 pm - 5:00 pm | Andrzej Veitia, van Enk group (Oregon) Detecting memory effects in QIP architectures | Abstract. We present a method to test for temporal correlations between quantum gates. Our protocol can also detect strong memory effects, i.e., non-Markovianity. Moreover, our method is insensitive to state preparation and measurement errors (SPAM). |
| 5:00 pm - 5:30 pm | Eleanor Rieffel, (NASA Ames) Non-commuting two-local Hamiltonians for quantum error suppression | Abstract. Physical constraints make it challenging to implement and control multi-body interactions. Designing quantum information processes with Hamiltonians consisting of only one- and two-local terms is a worthwhile challenge. A common approach to robust storage of quantum information is to encode in the ground subspace of a Hamiltonian. Even allowing particles with high Hilbert-space dimension, it is not possible to protect quantum information from single-site errors by encoding in the ground subspace of any Hamiltonian containing only commuting two-local terms [1]. We demonstrate how to get around this no-go result by encoding in the ground subspace of a Hamiltonian consisting of non-commuting two-local terms arising from the gauge operators of a subsystem code. Specifically, we show how to protect stored quantum information against single-qubit errors using a Hamiltonian consisting of sums of the gauge generators from Bacon-Shor codes [2] and generalized-Bacon-Shor code [3]. Thus, non-commuting two-local Hamiltonians have more error-suppressing power than commuting two-local Hamiltonians. Finally, we comment briefly on the robustness of the whole scheme. [1] I. Marvian and D. A. Lidar, PRL 113, 260504 (2014) [2] D. Bacon, PRA 73, 012340 (2006) [3] S. Bravyi, PRA 83, 012320 (2011) |
| 5:30 pm - 6:00 pm | Nathan Wiebe, QuArC (Microsoft) Efficient Bayesian phase estimation | Abstract. We provide a new efficient adaptive algorithm for performing phase estimation that does not require that the user infer the bits of the eigenphase in reverse order; rather it directly infers the phase and estimates the uncertainty in the phase directly from experimental data. Our method is highly flexible, recovers from failures, can be run in the presence of substantial decoherence and other experimental imperfections, can learn instantaneous eigenphases for time dependent systems and is as fast or faster than existing algorithms. Finally, we show a new method for performing phase and amplitude estimation in small quantum systems that makes these methods practical for characterizing small quantum systems using present day hardware. |
| 6:00 pm - 6:30 pm | Travis Scholten, Blume-Kohout group (Sandia) Towards a model selection rule for quantum state tomography | Abstract. Quantum tomography on continuous variable systems poses a challenge: the density matrix comprises infinitely many parameters, but only finite data is available. Allowing all those parameters to vary will incorporate excessive noise, producing a poor estimate. Fortunately, model selection techniques can be used to fix (or exclude) some parameters. Model selection has been used in tomography to determine the best rank for an estimate, characterize sources of entanglement, and detect drift in state preparation. But these methods rely implicitly or explicitly on the Wilks Theorem, which predicts the behavior of the loglikelihood ratio statistic (LLRS) used to choose between models. Until now, it was not known whether the Wilks Theorem is accurate for quantum state tomography. We investigated the behavior of the LLRS using Monte Carlo simulations, and found that Wilks' prediction fails dramatically. Instead, the distribution of the LLRS is heavily distorted by boundaries (in state space and between models). We construct a model for the behavior of the LLRS, derive an almost analytic prediction for its mean value, and compare it to numerical experiments. The new model improves on existing methods (e.g. the Wilks Theorem), but is still imperfect. We conclude that LLRS-based model selection techniques like Akaike’s AIC may not be reliable for quantum tomography. |
SQuInT Chief Organizer
Prof. Akimasa Miyake
amiyake@unm.edu
SQuInT Co-Organizer
Prof. Elohim Becerra
fbecerra@unm.edu
SQuInT Founder
Prof. Ivan Deutsch
ideutsch@unm.edu
SQuInT Administrator
Gloria Cordova
gjcordo1@unm.edu
505 277-1850
