47 research outputs found
Accelerated Randomized Benchmarking
Quantum information processing offers promising advances for a wide range of
fields and applications, provided that we can efficiently assess the
performance of the control applied in candidate systems. That is, we must be
able to determine whether we have implemented a desired gate, and refine
accordingly. Randomized benchmarking reduces the difficulty of this task by
exploiting symmetries in quantum operations.
Here, we bound the resources required for benchmarking and show that, with
prior information, we can achieve several orders of magnitude better accuracy
than in traditional approaches to benchmarking. Moreover, by building on
state-of-the-art classical algorithms, we reach these accuracies with
near-optimal resources. Our approach requires an order of magnitude less data
to achieve the same accuracies and to provide online estimates of the errors in
the reported fidelities. We also show that our approach is useful for physical
devices by comparing to simulations.
Our results thus enable the application of randomized benchmarking in new
regimes, and dramatically reduce the experimental effort required to assess
control fidelities in quantum systems. Finally, our work is based on
open-source scientific libraries, and can readily be applied in systems of
interest.Comment: 10 pages, full source code at
https://github.com/cgranade/accelerated-randomized-benchmarking #quantuminfo
#benchmarkin
Quantum Hamiltonian Learning Using Imperfect Quantum Resources
Identifying an accurate model for the dynamics of a quantum system is a
vexing problem that underlies a range of problems in experimental physics and
quantum information theory. Recently, a method called quantum Hamiltonian
learning has been proposed by the present authors that uses quantum simulation
as a resource for modeling an unknown quantum system. This approach can, under
certain circumstances, allow such models to be efficiently identified. A major
caveat of that work is the assumption of that all elements of the protocol are
noise-free. Here, we show that quantum Hamiltonian learning can tolerate
substantial amounts of depolarizing noise and show numerical evidence that it
can tolerate noise drawn from other realistic models. We further provide
evidence that the learning algorithm will find a model that is maximally close
to the true model in cases where the hypothetical model lacks terms present in
the true model. Finally, we also provide numerical evidence that the algorithm
works for non-commuting models. This work illustrates that quantum Hamiltonian
learning can be performed using realistic resources and suggests that even
imperfect quantum resources may be valuable for characterizing quantum systems.Comment: 16 pages 11 Figure