text: 115,25,1288833900991729666,1284439222187892736,Enrico Fontana,"First paper out! With the wonderful people at @LosAlamosNatLab Quantum Computing Summer School we just released a paper on a new phenomenon that we called Noise Induced Barren Plateaus. This has implications for Quantum Neural Networks on NISQ devices. <LINK> Technical summary: for a model of Pauli noise, with sufficient noise both the cost function and its gradient in any direction decay exponentially in depth. This suggests that deep noisy QNNs are untrainable. Compared to noiseless BPs, this is not a statistical phenomenon! Understandable summary: under certain conditions, the noise of current quantum computer builds up extremely quickly as computations become more complicated.  This means that we must make better quantum computers or risk ending up with garbage results. Shoutout to the first author @samson_wang for the truly impressive work, and to the magic duo @kunal_phy and @MvsCerezo for the invaluable effort and guidance. Thanks to @SoneAkira @LCincio for the contributions and to the big boss @ColesQuantum for having made QCSS possible. Check out @MvsCerezo 's thread for an excellent memesplanation of the results! 👇 <LINK>",https://arxiv.org/abs/2007.14384,"Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise on NISQ devices places fundamental limitations on VQA performance. We rigorously prove a serious limitation for noisy VQAs, in that the noise causes the training landscape to have a barren plateau (i.e., vanishing gradient). Specifically, for the local Pauli noise considered, we prove that the gradient vanishes exponentially in the number of qubits $n$ if the depth of the ansatz grows linearly with $n$. These noise-induced barren plateaus (NIBPs) are conceptually different from noise-free barren plateaus, which are linked to random parameter initialization. Our result is formulated for a generic ansatz that includes as special cases the Quantum Alternating Operator Ansatz and the Unitary Coupled Cluster Ansatz, among others. For the former, our numerical heuristics demonstrate the NIBP phenomenon for a realistic hardware noise model. ",Noise-Induced Barren Plateaus in Variational Quantum Algorithms,5,"['First paper out!\n\nWith the wonderful people at @LosAlamosNatLab Quantum Computing Summer School we just released a paper on a new phenomenon that we called Noise Induced Barren Plateaus.\n\nThis has implications for Quantum Neural Networks on NISQ devices.\n\n<LINK>', 'Technical summary: for a model of Pauli noise, with sufficient noise both the cost function and its gradient in any direction decay exponentially in depth. This suggests that deep noisy QNNs are untrainable.\n\nCompared to noiseless BPs, this is not a statistical phenomenon!', 'Understandable summary: under certain conditions, the noise of current quantum computer builds up extremely quickly as computations become more complicated. \n\nThis means that we must make better quantum computers or risk ending up with garbage results.', 'Shoutout to the first author @samson_wang for the truly impressive work, and to the magic duo @kunal_phy and @MvsCerezo for the invaluable effort and guidance.\n\nThanks to @SoneAkira @LCincio for the contributions and to the big boss @ColesQuantum for having made QCSS possible.', ""Check out @MvsCerezo 's thread for an excellent memesplanation of the results! 👇\n\nhttps://t.co/zOzhTZy7Zw""]",20,07,1149