156 research outputs found
Fidelity estimation of quantum states on a silicon photonic chip
As a measure of the 'closeness' of two quantum states, fidelity plays a
fundamental role in quantum information theory. Fidelity estimation protocols
try to strike a balance between information gleaned from an experiment, and the
efficiency of its implementation, in terms of the number of states consumed by
the protocol. Here we adapt a previously reported optimal state verification
protocol (Phys. Rev. Lett. 120, 170502, 2018) for fidelity estimation of
two-qubit states. We demonstrate the protocol experimentally using a
fully-programmable silicon photonic two-qubit chip. Our protocol outputs
significantly smaller error bars of its point estimate in comparison with
another widely-used estimation protocol, showing a clear step forward in the
ability to estimate the fidelity of quantum states produced by a practical
device
Fidelity estimation of quantum states on a silicon photonic chip
As a measure of the 'closeness' of two quantum states, fidelity plays a fundamental role in quantum information theory. Fidelity estimation protocols try to strike a balance between information gleaned from an experiment, and the efficiency of its implementation, in terms of the number of states consumed by the protocol. Here we adapt a previously reported optimal state verification protocol (Phys. Rev. Lett. 120, 170502, 2018) for fidelity estimation of two-qubit states. We demonstrate the protocol experimentally using a fully-programmable silicon photonic two-qubit chip. Our protocol outputs significantly smaller error bars of its point estimate in comparison with another widely-used estimation protocol, showing a clear step forward in the ability to estimate the fidelity of quantum states produced by a practical device
Efficient quantum walk on a quantum processor
The random walk formalism is used across a wide range of applications, from modelling share prices to predicting population genetics. Likewise, quantum walks have shown much potential as a framework for developing new quantum algorithms. Here we present explicit efficient quantum circuits for implementing continuous-time quantum walks on the circulant class of graphs. These circuits allow us to sample from the output probability distributions of quantum walks on circulant graphs efficiently. We also show that solving the same sampling problem for arbitrary circulant quantum circuits is intractable for a classical computer, assuming conjectures from computational complexity theory. This is a new link between continuous-time quantum walks and computational complexity theory and it indicates a family of tasks that could ultimately demonstrate quantum supremacy over classical computers. As a proof of principle, we experimentally implement the proposed quantum circuit on an example circulant graph using a two-qubit photonics quantum processor
Plasma metabolomic signatures of breast cancer
BackgroundBreast cancer is a common malignant tumor. A large number of medical evidence shows that breast cancer screening can improve the early diagnosis rate and reduce the mortality rate of breast cancer. In the present study, a wide range of targeted metabolomics profiling was conducted to investigate the plasma signatures of breast cancer.MethodsA total of 86 patients with benign breast abnormalities (L group) and 143 patients with breast cancer (E group) were recruited. We collected their plasma samples and clinical information. Metabolomic analysis, based on the coverage of a wide range of targeted metabolomics was conducted with ultraperformance liquid chromatography- triple quadrupole-linear ion trap mass spectrometer (UPLC-QTRAP-MS).ResultsWe identified 716 metabolites through widely-targeted metabolomics. Serotonergic synapse was the main different metabolic pathway. The fold change of 14 metabolites was considered significantly different (fold change <0.67 or fold change >2; p < 0.05). By combining all the 14 metabolites, we achieved differentiation of L group vs. E group (AUC = 0.792, 95%Cl: 0.662–0.809).ConclusionThis study provided new insights into plasma biomarkers for differential diagnosis of benign abnormalities and breast cancer
FlowFormer: A Transformer Architecture and Its Masked Cost Volume Autoencoding for Optical Flow
This paper introduces a novel transformer-based network architecture,
FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for
pretraining it to tackle the problem of optical flow estimation. FlowFormer
tokenizes the 4D cost-volume built from the source-target image pair and
iteratively refines flow estimation with a cost-volume encoder-decoder
architecture. The cost-volume encoder derives a cost memory with
alternate-group transformer~(AGT) layers in a latent space and the decoder
recurrently decodes flow from the cost memory with dynamic positional cost
queries. On the Sintel benchmark, FlowFormer architecture achieves 1.16 and
2.09 average end-point-error~(AEPE) on the clean and final pass, a 16.5\% and
15.5\% error reduction from the GMA~(1.388 and 2.47). MCVA enhances FlowFormer
by pretraining the cost-volume encoder with a masked autoencoding scheme, which
further unleashes the capability of FlowFormer with unlabeled data. This is
especially critical in optical flow estimation because ground truth flows are
more expensive to acquire than labels in other vision tasks. MCVA improves
FlowFormer all-sided and FlowFormer+MCVA ranks 1st among all published methods
on both Sintel and KITTI-2015 benchmarks and achieves the best generalization
performance. Specifically, FlowFormer+MCVA achieves 1.07 and 1.94 AEPE on the
Sintel benchmark, leading to 7.76\% and 7.18\% error reductions from
FlowFormer.Comment: arXiv admin note: substantial text overlap with arXiv:2203.16194,
arXiv:2303.0123
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