Multilevel leapfrogging initialization for quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a prospective hybrid quantum-classical algorithm widely used to solve combinatorial optimization problems. However, the external parameter optimization required in QAOA tends to consume extensive resources to find the optimal parameters of the parameterized quantum circuit, which may be the bottleneck of QAOA. To meet this challenge, we first propose multilevel leapfrogging learning (M-Leap) that can be extended to quantum reinforcement learning, quantum circuit design, and other domains. M-Leap incrementally increases the circuit depth during optimization and predicts the initial parameters at level $p+r$ ($r>1$) based on the optimized parameters at level $p$, cutting down the optimization rounds. Then, we propose a multilevel leapfrogging-interpolation strategy (MLI) for initializing optimizations by combining M-Leap with the interpolation technique. We benchmark its performance on the Maxcut problem. Compared with the Interpolation-based strategy (INTERP), MLI cuts down at least half the number of rounds of optimization for the classical outer learning loop. Remarkably, the simulation results demonstrate that the running time of MLI is 1/3 of INTERP when MLI gets quasi-optimal solutions. In addition, we present the greedy-MLI strategy by introducing multi-start, which is an extension of MLI. The simulation results show that greedy-MLI can get a higher average performance than the remaining two methods. With their efficiency to find the quasi-optima in a fraction of costs, our methods may shed light in other quantum algorithms

Scalable Method for Eliminating Residual ZZZZ Interaction between Superconducting Qubits

Unwanted $ZZ$ interaction is a quantum-mechanical crosstalk phenomenon which correlates qubit dynamics and is ubiquitous in superconducting qubit systems. It adversely affects the quality of quantum operations and can be detrimental in scalable quantum information processing. Here we propose and experimentally demonstrate a practically extensible approach for complete cancellation of residual $ZZ$ interaction between fixed-frequency transmon qubits, which are known for long coherence and simple control. We apply to the intermediate coupler that connects the qubits a weak microwave drive at a properly chosen frequency in order to noninvasively induce an ac Stark shift for $ZZ$ cancellation. We verify the cancellation performance by measuring vanishing two-qubit entangling phases and $ZZ$ correlations. In addition, we implement a randomized benchmarking experiment to extract the idling gate fidelity which shows good agreement with the coherence limit, demonstrating the effectiveness of $ZZ$ cancellation. Our method allows independent addressability of each qubit-qubit connection, and is applicable to both nontunable and tunable couplers, promising better compatibility with future large-scale quantum processors.Comment: Main text: 6 pages, 4 figures; Supplement: 7 pages, 6 figure

Heisenberg-limited quantum metrology using 100-photon Fock states

Quantum metrology has emerged as a promising avenue for surpassing the limitations of classical mechanics in high-precision measurements. However, the practical implementation of quantum metrology is hindered by the challenges of manipulating exotic quantum states in large systems. Here, we propose and demonstrate a hardware-efficient approach to achieve Heisenberg-limited quantum metrology using large photon-number Fock states. We have developed a programmable photon number filter that efficiently generates Fock states with up to 100 photons in a high-quality superconducting microwave cavity. Using these highly nontrivial states in displacement and phase measurements, we demonstrate a precision scaling close to the Heisenberg limit and achieve a maximum metrological gain of up to 14.8 dB. Our hardware-efficient quantum metrology can be extended to mechanical and optical systems and provides a practical solution for high metrological gain in bosonic quantum systems, promising potential applications in radiometry and the search for new particles.Comment: Main text: 10 pages, 4 figures; Supplement: 16 pages, 9 figures, 1 tabl

Beating the break-even point with a discrete-variable-encoded logical qubit

Quantum error correction (QEC) aims to protect logical qubits from noises by utilizing the redundancy of a large Hilbert space, where an error, once it occurs, can be detected and corrected in real time. In most QEC codes, a logical qubit is encoded in some discrete variables, e.g., photon numbers. Such encoding schemes make the codewords orthogonal, so that the encoded quantum information can be unambiguously extracted after processing. Based on such discrete-variable encodings, repetitive QEC demonstrations have been reported on various platforms, but there the lifetime of the encoded logical qubit is still shorter than that of the best available physical qubit in the entire system, which represents a break-even point that needs to be surpassed for any QEC code to be of practical use. Here we demonstrate a QEC procedure with a logical qubit encoded in photon-number states of a microwave cavity, dispersively coupled to an ancilla superconducting qubit. By applying a pulse featuring a tailored frequency comb to the ancilla, we can repetitively extract the error syndrome with high fidelity and perform error correction with feedback control accordingly, thereby exceeding the break-even point by about 16% lifetime enhancement. Our work illustrates the potential of the hardware-efficient discrete-variable QEC codes towards a reliable quantum information processor.Comment: Main text: 8 pages, 3 figures, 1 table; Supplement: 12 pages, 8 figures, 2 table

Prognostic significance of the novel nutrition-inflammation marker of lymphocyte–C-reactive protein ratio in patients with nasopharyngeal carcinoma receiving concurrent chemoradiotherapy

BackgroundRecent studies indicate that the novel lymphocyte–C-reactive protein ratio (LCR) is strongly associated with the survival of various tumors, but its prognostic value in nasopharyngeal carcinoma (NPC) is understudied. This study aimed to explore the relationship between LCR and overall survival (OS) in NPC and develop a predictive model.MethodsA total of 841 NPC patients who received concurrent chemoradiotherapy (CCRT) between January 2010 and December 2014 were retrospectively enrolled and randomly divided into a training cohort (n = 589) and a validation cohort (n = 252), and 122 patients between January 2015 and March 2015 were included as an additional validation cohort. Univariate and multivariate Cox analyses were performed to identify variables associated with OS and construct a predictive nomogram. The predictive accuracy of the nomogram was evaluated and independently validated.ResultsThe LCR score differentiated NPC patients into two groups with distinct prognoses (HR = 0.53; 95% CI: 0.32–0.89, P = 0.014). Multivariate analysis showed that age, T stage, N stage, EBV-DNA status, and LCR score were independently associated with OS, and a predictive nomogram was developed. The nomogram had a good performance for the prediction of OS [C-index = 0.770 (95% CI: 0.675–0.864)]. and outperformed the traditional staging system [C-index = 0.589 (95% CI: 0.385–0.792)]. The results were internally and additionally validated using independent cohorts.ConclusionThe pretreatment LCR could independently predict the overall survival in NPC patients. A novel LCR-based prognostic model of an easy-to-use nomogram was established, and it outperformed the conventional staging system in terms of predictive power. Further external verification remains necessary

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher’s exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis

Local Gene Silencing of Monocyte Chemoattractant Protein-1 Prevents Vulnerable Plaque Disruption in Apolipoprotein E-Knockout Mice

Monocyte chemoattractant protein-1 (MCP-1), a CC chemokine (CCL2), has been demonstrated to play important roles in atherosclerosis and becoming an important therapeutic target for atherosclerosis. The present study was undertaken to test the hypothesis that local RNAi of MCP-1 by site-specific delivery of adenovirus-mediated small hairpin RNA (shRNA) may enhance plaque stability and prevent plaque disruption in ApoE−/− mice. We designed an adenovirus-mediated shRNA against mouse MCP-1 (rAd5-MCP-1-shRNA). Male apolipoprotein E-knockout (ApoE−/−) mice (n = 120) were fed a high-fat diet and vulnerable plaques were induced by perivascular placement of constrictive collars around the carotid artery, intraperitoneal injection of lipopolysaccharide and stress stimulation. Mice were randomly divided into RNA interference (Ad-MCP-1i) group receiving local treatment of rAd5-MCP-1-shRNA suspension, Ad-EGFP group receiving treatment of rAd5-mediated negative shRNA and mock group receiving treatment of saline. Two weeks after treatment, plaque disruption rates were significantly lower in the Ad-MCP-1i group than in the Ad-EGFP group (13.3% vs. 60.0%, P = 0.01), and local MCP-1 expression was significantly inhibited in the Ad-MCP-1i group confirmed by immunostaining, qRT-PCR and western blot (P<0.001). Compared with the Ad-EGFP group, carotid plaques in the Ad-MCP-1i group showed increased levels of collagen and smooth muscle cells, and decreased levels of lipid and macrophages. The expression of inflammatory cytokines and activities of matrix metalloproteinases (MMPs) were lower in the Ad-MCP-1i group than in the Ad-EGFP group. In conclusion, site-specific delivery of adenoviral-mediated shRNA targeting mouse MCP-1 downregulated MCP-1 expression, turned a vulnerable plaque into a more stable plaque phenotype and prevented plaque disruption. A marked suppression of the local inflammatory cytokine expression may be the central mechanism involved

Attenuation of epigenetic regulator SMARCA4 and ERK-ETS signaling suppresses aging-related dopaminergic degeneration

How complex interactions of genetic, environmental factors and aging jointly contribute to dopaminergic degeneration in Parkinson's disease (PD) is largely unclear. Here, we applied frequent gene co‐expression analysis on human patient substantia nigra‐specific microarray datasets to identify potential novel disease‐related genes. In vivo Drosophila studies validated two of 32 candidate genes, a chromatin‐remodeling factor SMARCA4 and a biliverdin reductase BLVRA. Inhibition of SMARCA4 was able to prevent aging‐dependent dopaminergic degeneration not only caused by overexpression of BLVRA but also in four most common Drosophila PD models. Furthermore, down‐regulation of SMARCA4 specifically in the dopaminergic neurons prevented shortening of life span caused by α‐synuclein and LRRK2. Mechanistically, aberrant SMARCA4 and BLVRA converged on elevated ERK‐ETS activity, attenuation of which by either genetic or pharmacological manipulation effectively suppressed dopaminergic degeneration in Drosophila in vivo. Down‐regulation of SMARCA4 or drug inhibition of MEK/ERK also mitigated mitochondrial defects in PINK1 (a PD‐associated gene)‐deficient human cells. Our findings underscore the important role of epigenetic regulators and implicate a common signaling axis for therapeutic intervention in normal aging and a broad range of age‐related disorders including PD

2D Black Phosphorus: from Preparation to Applications for Electrochemical Energy Storage

Black phosphorus (BP) is rediscovered as a 2D layered material. Since its first isolation in 2014, 2D BP has triggered tremendous interest in the fields of condensed matter physics, chemistry, and materials science. Given its unique puckered monolayer geometry, 2D BP displays many unprecedented properties and is being explored for use in numerous applications. The flexibility, large surface area, and good electric conductivity of 2D BP make it a promising electrode material for electrochemical energy storage devices (EESDs). Here, the experimental and theoretical progress of 2D BP is presented on the basis of its preparation methods. The structural and physiochemical properties, air instability, passivation, and EESD applications of 2D BP are discussed systemically. Specifically, the latest research findings on utilizing 2D BP in EESDs, such as lithium‐ion batteries, supercapacitors, and emerging technologies (lithium–sulfur batteries, magnesium‐ion batteries, and sodium‐ion batteries), are summarized. On the basis of the current progress, a few personal perspectives on the existing challenges and future research directions in this developing field are provided