A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells.

Tumor-infiltrating myeloid cells (TIMs) are key regulators in tumor progression, but the similarity and distinction of their fundamental properties across different tumors remain elusive. Here, by performing a pan-cancer analysis of single myeloid cells from 210 patients across 15 human cancer types, we identified distinct features of TIMs across cancer types. Mast cells in nasopharyngeal cancer were found to be associated with better prognosis and exhibited an anti-tumor phenotype with a high ratio of TNF+/VEGFA+ cells. Systematic comparison between cDC1- and cDC2-derived LAMP3+ cDCs revealed their differences in transcription factors and external stimulus. Additionally, pro-angiogenic tumor-associated macrophages (TAMs) were characterized with diverse markers across different cancer types, and the composition of TIMs appeared to be associated with certain features of somatic mutations and gene expressions. Our results provide a systematic view of the highly heterogeneous TIMs and suggest future avenues for rational, targeted immunotherapies.

Entanglement of bosonic modes through an engineered exchange interaction.

Quantum computation presents a powerful new paradigm for information processing. A robust universal quantum computer can be realized with any well controlled quantum system, but a successful platform will ultimately require the combination of highly coherent, error-correctable quantum elements with at least one entangling operation between them1,2. Quantum information stored in a continuous-variable system-for example, a harmonic oscillator-can take advantage of hardware-efficient quantum error correction protocols that encode information in the large available Hilbert space of each element3-5. However, such encoded states typically have no controllable direct couplings, making deterministic entangling operations between them particularly challenging. Here we develop an efficient implementation of the exponential-SWAP operation6 and present its experimental realization between bosonic qubits stored in two superconducting microwave cavities. This engineered operation is analogous to the exchange interaction between discrete spin systems, but acts within any encoded subspace of the continuous-variable modes. Based on a control rotation, the operation produces a coherent superposition of identity and SWAP operations between arbitrary states of two harmonic oscillator modes and can be used to enact a deterministic entangling gate within quantum error correction codes. These results provide a valuable building block for universal quantum computation using bosonic modes.

SPINDLY O-fucosylates nuclear and cytoplasmic proteins involved in diverse cellular processes in plants.

SPINDLY (SPY) is a novel nucleocytoplasmic protein O-fucosyltransferase that regulates target protein activity or stability via O-fucosylation of specific Ser/Thr residues. Previous genetic studies indicate that AtSPY regulates plant development during vegetative and reproductive growth by modulating gibberellin and cytokinin responses. AtSPY also regulates the circadian clock and plant responses to biotic and abiotic stresses. The pleiotropic phenotypes of spy mutants point to the likely role of AtSPY in regulating key proteins functioning in diverse cellular pathways. However, very few AtSPY targets are known. Here, we identified 88 SPY targets from Arabidopsis (Arabidopsis thaliana) and Nicotiana benthamiana via the purification of O-fucosylated peptides using Aleuria aurantia lectin followed by electron transfer dissociation-MS/MS analysis. Most AtSPY targets were nuclear proteins that function in DNA repair, transcription, RNA splicing, and nucleocytoplasmic transport. Cytoplasmic AtSPY targets were involved in microtubule-mediated cell division/growth and protein folding. A comparison with the published O-linked-N-acetylglucosamine (O-GlcNAc) proteome revealed that 30% of AtSPY targets were also O-GlcNAcylated, indicating that these distinct glycosylations could co-regulate many protein functions. This study unveiled the roles of O-fucosylation in modulating many key nuclear and cytoplasmic proteins and provided a valuable resource for elucidating the regulatory mechanisms involved.

Robust bistatic ghost imaging with no physical synchronization.

Ghost imaging (GI) requires each echo from the object being correctly matched with the corresponding illuminiation pattern. We proposed a way for such matching with no physical synchronization towards bistatic configuration. The illumination is dually encoded in spatial and time domain. With aperiodic waveform and progressive correlation, the echoes can be correctly located and images can be obtained. In the experiments, our scheme is verified under different levels of signal to noise ratios, as well as different intensity of crosstalk. Ghost imaging with two transmitters and one receiver is also demonstrated. With our method, it is also possible to improve the imaging speed with multiple sources.

Tight Bounds on Pauli Channel Learning without Entanglement.

Quantum entanglement is a crucial resource for learning properties from nature, but a precise characterization of its advantage can be challenging. In this Letter, we consider learning algorithms without entanglement to be those that only utilize states, measurements, and operations that are separable between the main system of interest and an ancillary system. Interestingly, we show that these algorithms are equivalent to those that apply quantum circuits on the main system interleaved with mid-circuit measurements and classical feedforward. Within this setting, we prove a tight lower bound for Pauli channel learning without entanglement that closes the gap between the best-known upper and lower bound. In particular, we show that Θ(2^{n}ϵ^{-2}) rounds of measurements are required to estimate each eigenvalue of an n-qubit Pauli channel to ϵ error with high probability when learning without entanglement. In contrast, a learning algorithm with entanglement only needs Θ(ϵ^{-2}) copies of the Pauli channel. The tight lower bound strengthens the foundation for an experimental demonstration of entanglement-enhanced advantages for Pauli noise characterization.

Slow-Wave Hybrid Magnonics.

Cavity magnonics is an emerging research area focusing on the coupling between magnons and photons. Despite its great potential for coherent information processing, it has been long restricted by the narrow interaction bandwidth. In this Letter, we theoretically propose and experimentally demonstrate a novel approach to achieve broadband photon-magnon coupling by adopting slow waves on engineered microwave waveguides. To the best of our knowledge, this is the first time that slow wave is combined with hybrid magnonics. Its unique properties promise great potentials for both fundamental research and practical applications, for instance, by deepening our understanding of the light-matter interaction in the slow wave regime and providing high-efficiency spin wave transducers. The device concept can be extended to other systems such as optomagnonics and magnomechanics, opening up new directions for hybrid magnonics.

Predictive Value of the Diffusion Magnetic Resonance Imaging Technique for the Postoperative Outcome of Cervical Spondylotic Myelopathy.

BACKGROUND: Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM). PURPOSE: To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis. STUDY TYPE: Prospective. POPULATION: Post-surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old). FIELD STRENGTH/SEQUENCE: 3.0 T/Turbo spin echo T1/T2-weighted, T2*-weighted multiecho gradient echo and dMRI. ASSESSMENT: Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single-factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed-effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables. STATISTICAL TESTS: Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single-factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant. RESULTS: The single-factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10-1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27-6.04), which was stronger than the other variables. DATA CONCLUSION: The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.

OBJECTIVES: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods. METHODS: ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance. RESULTS: A total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84). CONCLUSIONS: The automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h. CLINICAL RELEVANCE STATEMENT: Perfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time. KEY POINTS: • The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h.

Deterministic teleportation of a quantum gate between two logical qubits.

A quantum computer has the potential to efficiently solve problems that are intractable for classical computers. However, constructing a large-scale quantum processor is challenging because of the errors and noise that are inherent in real-world quantum systems. One approach to addressing this challenge is to utilize modularity-a strategy used frequently in nature and engineering to build complex systems robustly. Such an approach manages complexity and uncertainty by assembling small, specialized components into a larger architecture. These considerations have motivated the development of a quantum modular architecture, in which separate quantum systems are connected into a quantum network via communication channels1,2. In this architecture, an essential tool for universal quantum computation is the teleportation of an entangling quantum gate3-5, but such teleportation has hitherto not been realized as a deterministic operation. Here we experimentally demonstrate the teleportation of a controlled-NOT (CNOT) gate, which we make deterministic by using real-time adaptive control. In addition, we take a crucial step towards implementing robust, error-correctable modules by enacting the gate between two logical qubits, encoding quantum information redundantly in the states of superconducting cavities6. By using such an error-correctable encoding, our teleported gate achieves a process fidelity of 79 per cent. Teleported gates have implications for fault-tolerant quantum computation3, and when realized within a network can have broad applications in quantum communication, metrology and simulations1,2,7. Our results illustrate a compelling approach for implementing multi-qubit operations on logical qubits and, if integrated with quantum error-correction protocols, indicate a promising path towards fault-tolerant quantum computation using a modular architecture.

Immunomodulation in allergic rhinitis: Insights from Th2 cells and NLRP3/IL-18 pathway.

Allergic rhinitis (AR) is characterized by nasal symptoms such as rubbing and sneezing, often triggered by allergen exposure. The purpose of this study is to dissect the roles of NLRP3-mediated immune modulation and macrophage pyroptosis in modulating T cell differentiation within the context of ovalbumin (OVA)-induced AR in mice. OVA-induced AR was established in mice, evaluating nasal symptoms, macrophage infiltration, cytokine levels, and T cell differentiation. Manipulations using NLRP3-/-, ASC-/- mice, clodronate liposome treatment, and NLRP3 inhibitor MCC950 were performed to assess their impact on AR symptoms and immune responses. Following OVA stimulation, increased nasal symptoms were observed in the OVA group along with augmented GATA3 expression and elevated IL-4 and IL-1b levels, indicative of Th2 polarization and cellular pyroptosis involvement. NLRP3-/- and ASC-/- mice exhibited reduced CD3+ T cells post OVA induction, implicating cellular pyroptosis in AR. Macrophage depletion led to decreased IgE levels, highlighting their involvement in allergic responses. Further investigations revealed enhanced macrophage pyroptosis, influencing Th1/Th2 differentiation in AR models. IL-18 released through NLRP3-mediated pyroptosis induced Th2 differentiation, distinct from IL-1b. Additionally, MCC950 effectively mitigated AR symptoms by modulating Th2 responses and reducing macrophage infiltration. This comprehensive study unravels the pivotal role of NLRP3-mediated immune modulation and macrophage pyroptosis in Th1/Th2 balance regulation in OVA-induced AR. Targeting NLRP3 pathways with MCC950 emerged as a promising strategy to alleviate AR symptoms, providing insights for potential therapeutic interventions in AR management.

Microbial creation of ß-Nicotinamide mononucleotide and its regulation of lipid metabolism in the liver of high-fat diet mice.

ß-Nicotinamide mononucleotide (NMN) is a biologically active nucleotide that regulates the physiological metabolism of the body by rapidly increasing nicotinamide adenine dinucleotide (NAD+). To determine the safety and biological activity of NMN resources, we constructed a recombinant strain of P. pastoris that heterologously expresses nicotinamide-phosphate ribosyltransferase (NAMPT), and subsequently catalyzed and purified the expressed product to obtain NMN. Consequently, this study established a high-fat diet (HFD) obese model to investigate the lipid-lowering activity of NMN. The findings showed that NMN supplementation directly increased the NAD+ levels, and reduced HFD-induced liver injury and lipid deposition. NMN treatment significantly decreased total cholesterol (TC) and triglyceride (TG) in serum and liver, as well as alanine aminotransferase (ALT) and insulin levels in serum (p < .05 or p < .01). In conclusion, this study combined synthetic biology with nutritional evaluation to confirm that P. pastoris-generated NMN modulated lipid metabolism in HFD mice, offering a theoretical framework and evidence for the application of microbially created NMN.

Induced transparency by interference or polarization.

Polarization of optical fields is a crucial degree of freedom in the all-optical analogue of electromagnetically induced transparency (EIT). However, the physical origins of EIT and polarization-induced phenomena have not been well distinguished, which can lead to confusion in associated applications such as slow light and optical/quantum storage. Here we study the polarization effects in various optical EIT systems. We find that a polarization mismatch between whispering gallery modes in two indirectly coupled resonators can induce a narrow transparency window in the transmission spectrum resembling the EIT lineshape. However, such polarization-induced transparency (PIT) is distinct from EIT: It originates from strong polarization rotation effects and shows a unidirectional feature. The coexistence of PIT and EIT provides additional routes for the manipulation of light flow in optical resonator systems.

Would the one-stage combined approach lead to better long-term neurological outcomes than the posterior approach alone in multilevel degenerative cervical myelopathy patients with T2-Weighted increased signal intensity? An 8-year follow-up results and propensity score matching analysis.

BACKGROUND: T2-weighted increased signal intensity (ISI) is commonly recognized as a sign of more severe spinal cord lesions, usually accompanied by worse neurological deficits and possibly worse postoperative neurological recovery. The combined approach could achieve better decompression and better neurological recovery for multilevel degenerative cervical myelopathy (MDCM). The choice of surgical approach for MDCM with intramedullary T2-weighted ISI remains disputed. This study aimed to compare the neurological outcomes of posterior and one-stage combined posteroanterior approaches for MDCM with T2-weighted ISI. METHODS: A total of 83 consecutive MDCM patients with confirmed ISI with at least three intervertebral segments operated between 2012 and 2014 were retrospectively enrolled. Preoperative demographic, radiological and clinical condition variables were collected, and neurological conditions were evaluated by the Japanese Orthopedic Assessment score (JOA) and Neck Disability Index (NDI). Propensity score matching analysis was conducted to produce pairs of patients with comparable preoperative conditions from the posterior-alone and combined groups. Both short-term and mid-term surgical outcomes were evaluated, including the JOA recovery rate (JOARR), NDI improvements, complications, and reoperations. RESULTS: A total of 83 patients were enrolled, of which 38 and 45 patients underwent posterior surgery alone and one-stage posteroanterior surgery, respectively. After propensity score matching, 38 pairs of comparable patients from the posterior and combined groups were matched. The matched groups presented similar preoperative clinical and radiological features and the mean follow-up duration were 111.6 ± 8.9 months. The preoperative JOA scores of the posterior and combined groups were 11.5 ± 2.2 and 11.1 ± 2.3, respectively (p = 0.613). The combined group presented with prolonged surgery duration(108.8 ± 28.0 and 186.1 ± 47.3 min, p = 0.028) and greater blood loss(276.3 ± 139.1 and 382.1 ± 283.1 ml, p<0.001). At short-term follow-up, the combined group presented a higher JOARR than the posterior group (posterior group: 50.7%±46.6%, combined group: 70.4%±20.3%, p = 0.024), while no significant difference in JOARR was observed between the groups at long-term follow-up (posterior group: 49.2%±48.5%, combined group: 59.6%±47.6%, p = 0.136). No significant difference was found in the overall complication and reoperation rates. CONCLUSIONS: For MDCM patients with ISI, both posterior and one-stage posteroanterior approaches could achieve considerable neurological alleviations in short-term and long-term follow-up. With greater surgical trauma, the combined group presented better short-term JOARR but did not show higher efficacy in long-term neurological function preservation in patients with comparable preoperative conditions.

Domestication Gene Mlx and Its Partner Mondo Are Involved in Controlling the Larval Body Size and Cocoon Shell Weight of Bombyx mori.

Bombyx mori was domesticated from Bombyx mandarina. The long-term domestication of the silkworm has brought about many remarkable changes to its body size and cocoon shell weight. However, the molecular mechanism underlying the improvement in the economic characteristics of this species during domestication remains unclear. In this study, we found that a transposable element (TE)-Bm1-was present in the upstream regulatory region of the Mlx (Max-like protein X) gene in wild silkworms but not in all domesticated silkworms. The absence of Bm1 caused an increase in the promoter activity and mRNA content of Mlx. Mlx and its partner Mondo belong to the bHLHZ transcription factors family and regulate nutrient metabolism. RNAi of Mlx and Mondo decreased the expression and promoter activity of glucose metabolism-related genes (trehalose transport (Tret), phosphofructokinase (PFK), and pyruvate kinase (PK)), lipogenic genes (Acetyl-CoA carboxylase (ACC) and fatty acid synthase (FAS)), and glutamine synthesis gene (Glutamine synthase 2, (GS2)). Furthermore, the transgenic overexpression of Mlx and Mondo in the fat body of silkworms increased the larval body size, cocoon shell weight, and egg number, but the silencing of the two genes resulted in the opposite phenotypes. Our results reveal the molecular mechanism of Mlx selection during domestication and its successful use in the molecular breeding of Bombyx mori.

Radiomics Analysis of Diffusion-Weighted Imaging and Long-Term Unfavorable Outcomes Risk for Acute Stroke.

BACKGROUND: Diffusion-weighted imaging radiomics could be used as prognostic biomarkers in acute ischemic stroke. We aimed to identify a clinical and diffusion-weighted imaging radiomics model for individual unfavorable outcomes risk assessment in acute ischemic stroke. METHODS: A total of 1716 patients with acute ischemic stroke from 2 centers were divided into a training cohort and a validation cohort. Patient outcomes were measured with the modified Rankin Scale score. An unfavorable outcome was defined as a modified Rankin Scale score greater than 2. The primary end point was all-cause mortality or outcomes 1 year after stroke. The MRI-DRAGON score was calculated based on previous publications. We extracted and selected the infarct features on diffusion-weighted imaging to construct a radiomic signature. The clinic-radiomics signature was built by measuring the Cox proportional risk regression score (CrrScore) and compared with the MRI-DRAGON score and the ClinicScore. CrrScore model performance was estimated by 1-year unfavorable outcomes prediction. RESULTS: A high radiomic signature predicted a higher probability of unfavorable outcomes than a low radiomic signature in the training (hazard ratio, 3.19 [95% CI, 2.51-4.05]; P<0.0001) and validation (hazard ratio, 3.25 [95% CI, 2.20-4.80]; P<0.0001) cohorts. The diffusion-weighted imaging Alberta Stroke Program Early CT Score, age, glucose level before therapy, National Institutes of Health Stroke Scale score on admission, glycated hemoglobin' radiomic signature, hemorrhagic infarction, and malignant cerebral edema were associated with an unfavorable outcomes risk after multivariable adjustment. A CrrScore nomogram was developed to predict outcomes and had the best performance in the training (area under the curve, 0.862) and validation cohorts (area under the curve, 0.858). The CrrScore model time-dependent areas under the curve of the probability of unfavorable outcomes at 1 year in the training and validation cohorts were 0.811 and 0.801, respectively. CONCLUSIONS: The CrrScore model allows the accurate prediction of patients with acute ischemic stroke outcomes and can potentially guide rehabilitation therapies for patients with different risks of unfavorable outcomes.

Exploring Self-Healing and Switchable Adhesives based on Multi-Level Dynamic Stable Structure.

It is a challenge to develop adhesives simultaneously capable of strong adhesion and efficient switchable ability. Herein, the authors report multifunctional switchable adhesives named Cu2+ -curcumin-imidazole-polyurethane (CIPUs:Cu2+ ) by introducing 1-(3-aminopropyl) imidazole and curcumin into polyurethane system crossed by Cu2+ forming dynamic metal-ligand bonds. This CIPUs:Cu2+ has strong adhesion (up to 2.46 MPa) on various material surfaces due to their specially designed functional groups alike the secretions from mussels. It can achieve fast switching speed (30 s) and high switch efficiency through multiple contactless remote stimulations. Importantly, density functional theory (DFT) calculation reveals that such metal-ligand bonds consisting of two components: stronger Cu2+ -curcumin complexes and weaker Cu2+ -imidazole complexes can aggregate to form multi-level dynamic stable structure . The special structure can not only be acted as sacrificial sites for easily broken and reformed, allowing efficient switchable adhesion and enormous energy dissipation but also acted as firm sites to maintain the cohesion of the adhesive and the reversible reconstruction network. Intriguingly, the CIPUs:Cu2+ can achieve self-healing at room temperature without needing external stimuli. Overall, this strategy can further broaden the design of switchable adhesives in the fields of intelligent gadgets, wearable bio-monitoring devices, etc.

Spoofing Cross-Entropy Measure in Boson Sampling.

Cross-entropy (XE) measure is a widely used benchmark to demonstrate quantum computational advantage from sampling problems, such as random circuit sampling using superconducting qubits and boson sampling (BS). We present a heuristic classical algorithm that attains a better XE than the current BS experiments in a verifiable regime and is likely to attain a better XE score than the near-future BS experiments in a reasonable running time. The key idea behind the algorithm is that there exist distributions that correlate with the ideal BS probability distribution and that can be efficiently computed. The correlation and the computability of the distribution enable us to postselect heavy outcomes of the ideal probability distribution without computing the ideal probability, which essentially leads to a large XE. Our method scores a better XE than the recent Gaussian BS experiments when implemented at intermediate, verifiable system sizes. Much like current state-of-the-art experiments, we cannot verify that our spoofer works for quantum-advantage-size systems. However, we demonstrate that our approach works for much larger system sizes in fermion sampling, where we can efficiently compute output probabilities. Finally, we provide analytic evidence that the classical algorithm is likely to spoof noisy BS efficiently.

Analytic Theory for the Dynamics of Wide Quantum Neural Networks.

Parametrized quantum circuits can be used as quantum neural networks and have the potential to outperform their classical counterparts when trained for addressing learning problems. To date, much of the results on their performance on practical problems are heuristic in nature. In particular, the convergence rate for the training of quantum neural networks is not fully understood. Here, we analyze the dynamics of gradient descent for the training error of a class of variational quantum machine learning models. We define wide quantum neural networks as parametrized quantum circuits in the limit of a large number of qubits and variational parameters. Then, we find a simple analytic formula that captures the average behavior of their loss function and discuss the consequences of our findings. For example, for random quantum circuits, we predict and characterize an exponential decay of the residual training error as a function of the parameters of the system. Finally, we validate our analytic results with numerical experiments.

Error Suppression for Arbitrary-Size Black Box Quantum Operations.

Efficient suppression of errors without full error correction is crucial for applications with noisy intermediate-scale quantum devices. Error mitigation allows us to suppress errors in extracting expectation values without the need for any error correction code, but its applications are limited to estimating expectation values, and cannot provide us with high-fidelity quantum operations acting on arbitrary quantum states. To address this challenge, we propose to use error filtration (EF) for gate-based quantum computation, as a practical error suppression scheme without resorting to full quantum error correction. The result is a general-purpose error suppression protocol where the resources required to suppress errors scale independently of the size of the quantum operation, and does not require any logical encoding of the operation. The protocol provides error suppression whenever an error hierarchy is respected-that is, when the ancillary controlled-swap operations are less noisy than the operation to be corrected. We further analyze the application of EF to quantum random access memory, where EF offers hardware-efficient error suppression.

Optimized Protocols for Duplex Quantum Transduction.

Quantum transducers convert quantum signals through hybrid interfaces of physical platforms in quantum networks. Modeled as quantum communication channels, performance of unidirectional quantum transduction can be measured by the quantum channel capacity. However, characterizing performance of quantum transducers used for duplex quantum transduction where signals are converted bidirectionally remains an open question. Here, we propose rate regions to characterize the performance of duplex quantum transduction. Using this tool, we find that quantum transducers optimized for simultaneous duplex transduction can outperform strategies based on the standard protocol of time-shared unidirectional transduction. Integrated over the frequency domain, we demonstrate that the rate region can also characterize quantum transducers with finite bandwidth.