Multi-mass solvers for lattice QCD on GPUs

Graphical Processing Units (GPUs) are more and more frequently used for lattice QCD calculations. Lattice studies often require computing the quark propagators for several masses. These systems can be solved using multi-shift inverters but these algorithms are memory intensive which limits the size of the problem that can be solved using GPUs. In this paper, we show how to efficiently use a memory-lean single-mass inverter to solve multi-mass problems. We focus on the BiCGstab algorithm for Wilson fermions and show that the single-mass inverter not only requires less memory but also outperforms the multi-shift variant by a factor of two.Comment: 27 pages, 6 figures, 3 Table

Quantum Error Correction via Noise Guessing Decoding

Quantum error correction codes (QECCs) play a central role both in quantum communications and in quantum computation, given how error-prone quantum technologies are. Practical quantum error correction codes, such as stabilizer codes, are generally structured to suit a specific use, and present rigid code lengths and code rates, limiting their adaptability to changing requirements. This paper shows that it is possible to both construct and decode QECCs that can attain the maximum performance of the finite blocklength regime, for any chosen code length and when the code rate is sufficiently high. A recently proposed strategy for decoding classical codes called GRAND (guessing random additive noise decoding) opened doors to decoding classical random linear codes (RLCs) that perform near the capacity of the finite blocklength regime. By making use of the noise statistics, GRAND is a noise-centric efficient universal decoder for classical codes, providing there is a simple code membership test. These conditions are particularly suitable for quantum systems and therefore the paper extends these concepts to quantum random linear codes (QRLCs), which were known to be possible to construct but whose decoding was not yet feasible. By combining QRLCs and a newly proposed quantum GRAND, this paper shows that decoding versatile quantum error correction is possible, allowing for QECCs that are simple to adapt on the fly to changing conditions. The paper starts by assessing the minimum number of gates in the coding circuit needed to reach the QRLCs' asymptotic performance, and subsequently proposes a quantum GRAND algorithm that makes use of quantum noise statistics, not only to build an adaptive code membership test, but also to efficiently implement syndrome decoding

Investigation of quantitative measures related to reading disability in a large sample of sib-pairs from the UK

We describe a family-based sample of individuals with reading disability collected as part of a quantitative trait loci (QTL) mapping study. Eighty-nine nuclear families (135 independent sib-pairs) were identified through a single proband using a traditional discrepancy score of predicted/actual reading ability and a known family history. Eight correlated psychometric measures were administered to each sibling, including single word reading, spelling, similarities, matrices, spoonerisms, nonword and irregular word reading, and a pseudohomophone test. Summary statistics for each measure showed a reduced mean for the probands compared to the co-sibs, which in turn was lower than that of the population. This partial co-sib regression back to the mean indicates that the measures are influenced by familial factors and therefore, may be suitable for a mapping study. The variance of each of the measures remained largely unaffected, which is reassuring for the application of a QTL approach. Multivariate genetic analysis carried out to explore the relationship between the measures identified a common factor between the reading measures that accounted for 54% of the variance. Finally the familiality estimates (range 0.32–0.73) obtained for the reading measures including the common factor (0.68) supported their heritability. These findings demonstrate the viability of this sample for QTL mapping, and will assist in the interpretation of any subsequent linkage findings in an ongoing genome scan

Improved and Formal Proposal for Device Independent Quantum Private Query

In this paper, we propose a novel Quantum Private Query (QPQ) scheme with full Device-Independent certification. To the best of our knowledge, this is the first time we provide such a full DI-QPQ scheme using EPR-pairs. Our proposed scheme exploits self-testing of shared EPR-pairs along with the self-testing of projective measurement operators in a setting where the client and the server do not trust each other. To certify full device independence, we exploit a strategy to self-test a particular class of POVM elements that are used in the protocol. Further, we provide formal security analysis and obtain an upper bound on the maximum cheating probabilities for both the dishonest client as well as the dishonest server.Comment: 33 pages, 2 figure

Self-referential thinking and equilibrium as states of mind in games: fMRI evidence

Sixteen subjects' brain activity were scanned using previous termfMRInext term as they made choices, expressed beliefs, and expressed iterated 2nd-order beliefs (what they think others believe they will do) in eight games. Cingulate cortex and prefrontal areas (active in “theory of mind” and social reasoning) are differentially activated in making choices versus expressing beliefs. Forming self-referential 2nd-order beliefs about what others think you will do seems to be a mixture of processes used to make choices and form beliefs. In equilibrium, there is little difference in neural activity across choice and belief tasks; there is a purely neural definition of equilibrium as a “state of mind.” “Strategic IQ,” actual earnings from choices and accurate beliefs, is negatively correlated with activity in the insula, suggesting poor strategic thinkers are too self-focused, and is positively correlated with ventral striatal activity (suggesting that high IQ subjects are spending more mental energy predicting rewards)