4 research outputs found
Learning the tensor network model of a quantum state using a few single-qubit measurements
The constantly increasing dimensionality of artificial quantum systems
demands for highly efficient methods for their characterization and
benchmarking. Conventional quantum tomography fails for larger systems due to
the exponential growth of the required number of measurements. The conceptual
solution for this dimensionality curse relies on a simple idea - a complete
description of a quantum state is excessive and can be discarded in favor of
experimentally accessible information about the system. The probably
approximately correct (PAC) learning theory has been recently successfully
applied to a problem of building accurate predictors for the measurement
outcomes using a dataset which scales only linearly with the number of qubits.
Here we present a constructive and numerically efficient protocol which learns
a tensor network model of an unknown quantum system. We discuss the limitations
and the scalability of the proposed method.Comment: 10 pages, 11 figure
Benchmarking a boson sampler with Hamming nets
Analyzing the properties of complex quantum systems is crucial for further
development of quantum devices, yet this task is typically challenging and
demanding with respect to required amount of measurements. A special attention
to this problem appears within the context of characterizing outcomes of noisy
intermediate-scale quantum devices, which produce quantum states with specific
properties so that it is expected to be hard to simulate such states using
classical resources. In this work, we address the problem of characterization
of a boson sampling device, which uses interference of input photons to produce
samples of non-trivial probability distributions that at certain condition are
hard to obtain classically. For realistic experimental conditions the problem
is to probe multi-photon interference with a limited number of the measurement
outcomes without collisions and repetitions. By constructing networks on the
measurements outcomes, we demonstrate a possibility to discriminate between
regimes of indistinguishable and distinguishable bosons by quantifying the
structures of the corresponding networks. Based on this we propose a
machine-learning-based protocol to benchmark a boson sampler with unknown
scattering matrix. Notably, the protocol works in the most challenging regimes
of having a very limited number of bitstrings without collisions and
repetitions. As we expect, our framework can be directly applied for
characterizing boson sampling devices that are currently available in
experiments.Comment: 14 page
Benchmarking quantum tomography completeness and fidelity with machine learning
We train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior information. In addition, we perform fidelity benchmarking based on this measurement set without explicitly carrying out state tomography. The networks are trained to recognize the fidelity and a reliable measure for informational completeness. By gradually accumulating measurements and data, these trained convolutional networks can efficiently establish a compressive quantum-state characterization scheme by accelerating runtime computation and greatly reducing systematic drifts in experiments. We confirm the potential of this machine-learning approach by presenting experimental results for both spatial-mode and multiphoton systems of large dimensions. These predictions are further shown to improve when the networks are trained with additional bootstrapped training sets from real experimental data. Using a realistic beam-profile displacement error model for Hermite-Gaussian sources, we further demonstrate numerically that the orders-of-magnitude reduction in certification time with trained networks greatly increases the computation yield of a large-scale quantum processor using these sources, before state fidelity deteriorates significantly