Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory

We utilize machine learning models which are based on recurrent neural networks to optimize dynamical decoupling (DD) sequences. DD is a relatively simple technique for suppressing the errors in quantum memory for certain noise models. In numerical simulations, we show that with minimum use of prior knowledge and starting from random sequences, the models are able to improve over time and eventually output DD-sequences with performance better than that of the well known DD-families. Furthermore, our algorithm is easy to implement in experiments to find solutions tailored to the specific hardware, as it treats the figure of merit as a black box.Comment: 18 pages, comments are welcom

Scalable Neural Network Decoders for Higher Dimensional Quantum Codes

Machine learning has the potential to become an important tool in quantum error correction as it allows the decoder to adapt to the error distribution of a quantum chip. An additional motivation for using neural networks is the fact that they can be evaluated by dedicated hardware which is very fast and consumes little power. Machine learning has been previously applied to decode the surface code. However, these approaches are not scalable as the training has to be redone for every system size which becomes increasingly difficult. In this work the existence of local decoders for higher dimensional codes leads us to use a low-depth convolutional neural network to locally assign a likelihood of error on each qubit. For noiseless syndrome measurements, numerical simulations show that the decoder has a threshold of around $7.1\%$ when applied to the 4D toric code. When the syndrome measurements are noisy, the decoder performs better for larger code sizes when the error probability is low. We also give theoretical and numerical analysis to show how a convolutional neural network is different from the 1-nearest neighbor algorithm, which is a baseline machine learning method

A Non-Commuting Stabilizer Formalism

We propose a non-commutative extension of the Pauli stabilizer formalism. The aim is to describe a class of many-body quantum states which is richer than the standard Pauli stabilizer states. In our framework, stabilizer operators are tensor products of single-qubit operators drawn from the group $\langle \alpha I, X,S\rangle$, where $\alpha=e^{i\pi/4}$ and $S=\operatorname{diag}(1,i)$. We provide techniques to efficiently compute various properties related to bipartite entanglement, expectation values of local observables, preparation by means of quantum circuits, parent Hamiltonians etc. We also highlight significant differences compared to the Pauli stabilizer formalism. In particular, we give examples of states in our formalism which cannot arise in the Pauli stabilizer formalism, such as topological models that support non-Abelian anyons.Comment: 52 page

Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees

Recently there is a line of research work proposing to employ Spectral Clustering (SC) to segment (group){Throughout the paper, we use segmentation, clustering, and grouping, and their verb forms, interchangeably.} high-dimensional structural data such as those (approximately) lying on subspaces {We follow {liu2010robust} and use the term "subspace" to denote both linear subspaces and affine subspaces. There is a trivial conversion between linear subspaces and affine subspaces as mentioned therein.} or low-dimensional manifolds. By learning the affinity matrix in the form of sparse reconstruction, techniques proposed in this vein often considerably boost the performance in subspace settings where traditional SC can fail. Despite the success, there are fundamental problems that have been left unsolved: the spectrum property of the learned affinity matrix cannot be gauged in advance, and there is often one ugly symmetrization step that post-processes the affinity for SC input. Hence we advocate to enforce the symmetric positive semidefinite constraint explicitly during learning (Low-Rank Representation with Positive SemiDefinite constraint, or LRR-PSD), and show that factually it can be solved in an exquisite scheme efficiently instead of general-purpose SDP solvers that usually scale up poorly. We provide rigorous mathematical derivations to show that, in its canonical form, LRR-PSD is equivalent to the recently proposed Low-Rank Representation (LRR) scheme {liu2010robust}, and hence offer theoretic and practical insights to both LRR-PSD and LRR, inviting future research. As per the computational cost, our proposal is at most comparable to that of LRR, if not less. We validate our theoretic analysis and optimization scheme by experiments on both synthetic and real data sets.Comment: 10 pages, 4 figures. Accepted by ICDM Workshop on Optimization Based Methods for Emerging Data Mining Problems (OEDM), 2010. Main proof simplified and typos corrected. Experimental data slightly adde

Commuting quantum circuits: efficient classical simulations versus hardness results

The study of quantum circuits composed of commuting gates is particularly useful to understand the delicate boundary between quantum and classical computation. Indeed, while being a restricted class, commuting circuits exhibit genuine quantum effects such as entanglement. In this paper we show that the computational power of commuting circuits exhibits a surprisingly rich structure. First we show that every 2-local commuting circuit acting on d-level systems and followed by single-qudit measurements can be efficiently simulated classically with high accuracy. In contrast, we prove that such strong simulations are hard for 3-local circuits. Using sampling methods we further show that all commuting circuits composed of exponentiated Pauli operators e^{i\theta P} can be simulated efficiently classically when followed by single-qubit measurements. Finally, we show that commuting circuits can efficiently simulate certain non-commutative processes, related in particular to constant-depth quantum circuits. This gives evidence that the power of commuting circuits goes beyond classical computation.Comment: 19 page

Game Theory Based Correlated Privacy Preserving Analysis in Big Data

Privacy preservation is one of the greatest concerns in big data. As one of extensive applications in big data, privacy preserving data publication (PPDP) has been an important research field. One of the fundamental challenges in PPDP is the trade-off problem between privacy and utility of the single and independent data set. However, recent research has shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which payoff of each player is dependent on his and his neighbors’ privacy parameters. In this paper, we firstly present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, in which each publishes data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium. We refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium. Finally, we show the correctness of our game analysis via simulation experiments

Quantum Instruction Set Design for Performance

A quantum instruction set is where quantum hardware and software meet. We develop new characterization and compilation techniques for non-Clifford gates to accurately evaluate different quantum instruction set designs. We specifically apply them to our fluxonium processor that supports mainstream instruction $\mathrm{iSWAP}$ by calibrating and characterizing its square root $\mathrm{SQiSW}$. We measure a gate fidelity of up to $99.72\%$ with an average of $99.31\%$ and realize Haar random two-qubit gates using $\mathrm{SQiSW}$ with an average fidelity of $96.38\%$. This is an average error reduction of $41\%$ for the former and a $50\%$ reduction for the latter compared to using $\mathrm{iSWAP}$ on the same processor. This shows designing the quantum instruction set consisting of $\mathrm{SQiSW}$ and single-qubit gates on such platforms leads to a performance boost at almost no cost.Comment: 2 figures in main text and 21 figures in Supplementary Materials. This manuscript subsumes version 1 with significant improvements such as experimental demonstration and materials presentatio

Challenges of EGFR-TKIs in NSCLC and the potential role of herbs and active compounds: From mechanism to clinical practice

Epidermal growth factor receptor (EGFR) mutations are the most common oncogenic driver in non-small cell lung cancer (NSCLC). Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are widely used in the treatment of lung cancer, especially in the first-line treatment of advanced NSCLC, and EGFR-TKIs monotherapy has achieved better efficacy and tolerability compared with standard chemotherapy. However, acquired resistance to EGFR-TKIs and associated adverse events pose a significant obstacle to targeted lung cancer therapy. Therefore, there is an urgent need to seek effective interventions to overcome these limitations. Natural medicines have shown potential therapeutic advantages in reversing acquired resistance to EGFR-TKIs and reducing adverse events, bringing new options and directions for EGFR-TKIs combination therapy. In this paper, we systematically demonstrated the resistance mechanism of EGFR-TKIs, the clinical strategy of each generation of EGFR-TKIs in the synergistic treatment of NSCLC, the treatment-related adverse events of EGFR-TKIs, and the potential role of traditional Chinese medicine in overcoming the resistance and adverse reactions of EGFR-TKIs. Herbs and active compounds have the potential to act synergistically through multiple pathways and multiple mechanisms of overall regulation, combined with targeted therapy, and are expected to be an innovative model for NSCLC treatment

Peripheral Blood Lymphocyte Subsets Predict the Efficacy of Immune Checkpoint Inhibitors in Non–Small Cell Lung Cancer

BackgroundNon–small cell lung cancer (NSCLC) has entered the era of immunotherapy. However, only partial patients were able to benefit from immune checkpoint inhibitors (ICIs). Currently, biomarkers for predicting patients’ response to ICIs are primarily tumor tissue dependent and have limited accuracy. There is an urgent need to explore peripheral blood-based biomarkers to predict the efficacy and safety of ICI therapy.MethodsTo explore the correlation between lymphocyte subsets and the efficacy and safety of ICIs, we retrospectively analyzed peripheral blood lymphocyte subsets and survival prognosis data of 136 patients with stage IV NSCLC treated with ICIs.ResultsThe two factors that had the greatest impact on the prognosis of patients with NSCLC treated with ICIs were CD4+CD45RA− T cell (HR = 0.644, P = 0.047) and CD8+ T/lymphocyte (%) (HR = 1.806, P = 0.015). CD4+CD45RA− T cell showed excellent predictive efficacy (AUC = 0.854) for ICIs monotherapy, with a sensitivity of 75.0% and specificity of 91.7% using CD4+CD45RA− T cell >311.3 × 106/L as the threshold. In contrast, CD8+ T/lymphocyte (%) was only associated with the prognosis but had no predictive role for ICI efficacy. CD4+ T cell and its subsets were significantly higher in patients with mild (grades 1–2) immune-related adverse events (irAEs) than those without irAEs. CD8+CD38+ T cell was associated with total irAEs and severe (grades 3–4) irAEs but was not suitable to be a predictive biomarker.ConclusionPeripheral blood CD4+CD45RA− T cell was associated with the prognosis of patients with NSCLC applying ICIs, whereas CD8+CD38+ T cell was associated with irAEs and severe irAEs

Proton pitch angle distributions in the Martian induced magnetosphere: A survey of Tianwen-1 Mars Ion and Neutral Particle Analyzer observations

The pitch angle distributions of ions and electrons can be affected by various processes; thus, they can serve as an important indicator of the physical mechanisms driving the dynamics of space plasmas. From observations from the Mars Ion and Neutral Particle Analyzer onboard the Tianwen-1 orbiter, we calculated the pitch angle distributions of protons in the Martian induced magnetosphere by using information from the magnetohydrodynamically simulated magnetic field, and we statistically analyzed the spatial occurrence pattern of different types of pitch angle distributions. Even though no symmetrical features were seen in the dataset, we found the dominance of the field-aligned distribution type over the energy range from 188 to 6232 eV. Maps of the occurrence rate showed the preferential presence of a trapped-like distribution at the lower altitudes of the surveyed nightside region. Although our results are more or less restricted by the adopted magnetic field, they indicate the complexity of the near-Mars proton pitch angle distributions and infer the possibility of wave–particle interactions in the Martian induced magnetosphere