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OPTIMAL RECOVERY QUANTUM ERROR CORRECTION FULL
Some full text articles may not yet be available without a charge during the embargo (administrative interval). When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH Efficient error mitigation practices can prompt developments across the spectrum of activity, from physical experiments seeking feasible quantum computing devices to theoretical research on quantum computing applications. Broader Impacts The results of this research can influence efforts throughout the field of quantum computation. By incorporating principles of quantum theory, information theory, coding theory, and optimization, the research will promote cross-discipline efforts between mathematics, physics, computer science, and engineering. We provide analytic n-qubit results for standard cases with correlated errors on multiple qubits and demonstrate significant improvements to the fidelity bounds and optimal entanglement decay profiles. Intellectual Merit The proposed research will yield a novel approach to error correction in the challenging and emerging field of quantum information systems. In our discussion of error recovery using the nine-qubit code, we have assumed. Research The project's research objectives are to: develop and analyze optimum recovery operations from noisy quantum channels, extend the analysis of optimum recoveries to high dimensional quantum systems, investigate the design of channel-specific encoding and recovery schemes, and determine the impact of optimum recovery operations for fault-tolerant quantum computation. present the theory of quantum error-correcting codes. This optimization may be calculated via a semidefinite program (SDP), a well-established form of convex optimization with efficient algorithms for its solution. Rather than perfectly correcting a subset of the error process, the proposed research will seek to maximize the entanglement fidelity of the recovered state to the input for a given noise model. The PI's propose to reexamine the choice of recovery operation. MATH PRIORITY SOLICITATION, MSPA-INTERDISCIPLINARYĠ625966 Shor In standard Quantum Error Correction, the input to a noisy system is embedded in a coded subspace, and error recovery is performed via an operation designed to perfectly correct specific errors, presumably a highly probable physical noise component. Primary Place of Performance Congressional District: Peter Shor (Principal Investigator) Moe Win (Co-Principal Investigator).Radhakisan Baheti ECCS Div Of Electrical, Commun & Cyber Sys ENG Directorate For Engineering In addition, it is necessary to properly optimize the parameters of the prediction model, which can obviously improve the generalization performance of the multivariable model.DMS- MSPA-Interdisciplinary: Optimum Quantum Error Recovery NSF Org:ĮCCS Div Of Electrical, Commun & Cyber Sys The results indicate that it is reliable to use the colorimetric sensor array with strong specificity for the determination of the AFB 1 in peanuts. kg −1, and ratio performance deviation (RPD) value was 2.4.The root mean square error of prediction (RMSEP) was 5.7 μg Its correlation coefficients of prediction (R P) reached 0.91. The results showed that the SSA-SVR model with the combination of seven characteristic color components obtained the best prediction effect. Compared with GS-SVR model, the model performance of SSA-SVR was better. We derive necessary and sufficient conditions for the approximate correctability of a quantum code, generalizing the Knill-Laflamme conditions for exact error. In this process, the optimization performance of grid search (GS) algorithm and sparrow search algorithm (SSA) on SVR parameter was compared. Support vector regression (SVR) quantitative analysis model was constructed by using the optimized combination of characteristic color components to achieve determination of the AFB 1 in peanuts. Then, genetic algorithm (GA) with back propagation neural network (BPNN) as the regressor was used to optimize the color component of the preprocessed sensor feature image. First, 12 kinds of chemical dyes were selected to prepare a colorimetric sensor to assemble olfactory visualization system, which was used to collect the odor characteristic information of peanut samples. recovery map that works nearly as well as the optimal recovery channel. This study proposes a novel method for detection of aflatoxin B 1 (AFB 1) in peanuts using olfactory visualization technique. Recent work on approximate quantum error correction (QEC) has opened up the.
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