Mean-value identities as an opportunity for Monte Carlo error reduction

In the Monte Carlo simulation of both lattice field theories and of models of statistical mechanics, identities verified by exact mean values, such as Schwinger-Dyson equations, Guerra relations, Callen identities, etc., provide well-known and sensitive tests of thermalization bias as well as checks of pseudo-random-number generators. We point out that they can be further exploited as control variates to reduce statistical errors. The strategy is general, very simple, and almost costless in CPU time. The method is demonstrated in the twodimensional Ising model at criticality, where the CPU gain factor lies between 2 and 4.

Single Events in a COTS Soft-Error Free SRAM at Low Bias Voltage Induced by 15-MeV Neutrons

This paper presents an experimental study of the sensitivity to 15-MeV neutrons of Advanced Low Power SRAMs (A-LPSRAM) at low bias voltage little above the threshold value that allows the retention of data. This family of memories is characterized by a 3D structure to minimize the area penalty and to cope with latchups, as well as by the presence of integrated capacitors to hinder the occurrence of single event upsets. In low voltage static tests, classical single event upsets were a minor source of errors, but other unexpected phenomena such as clusters of bitflips and hard errors turned out to be the origin of hundreds of bitflips. Besides, errors were not observed in dynamic tests at nominal voltage. This behavior is clearly different than that of standard bulk CMOS SRAMs, where thousands of errors have been reported.

A model for the time-order error in contrast discrimination.

Trials in a temporal two-interval forced-choice discrimination experiment consist of two sequential intervals presenting stimuli that differ from one another as to magnitude along some continuum. The observer must report in which interval the stimulus had a larger magnitude. The standard difference model from signal detection theory analyses poses that order of presentation should not affect the results of the comparison, something known as the balance condition (J.-C. Falmagne, 1985, in Elements of Psychophysical Theory). But empirical data prove otherwise and consistently reveal what Fechner (1860/1966, in Elements of Psychophysics) called time-order errors, whereby the magnitude of the stimulus presented in one of the intervals is systematically underestimated relative to the other. Here we discuss sensory factors (temporary desensitization) and procedural glitches (short interstimulus or intertrial intervals and response bias) that might explain the time-order error, and we derive a formal model indicating how these factors make observed performance vary with presentation order despite a single underlying mechanism. Experimental results are also presented illustrating the conventional failure of the balance condition and testing the hypothesis that time-order errors result from contamination by the factors included in the model.

Error tolerance of topological codes with independent bit-flip and measurement errors

Topological quantum error correction codes are currently among the most promising candidates for efficiently dealing with the decoherence effects inherently present in quantum devices. Numerically, their theoretical error threshold can be calculated by mapping the underlying quantum problem to a related classical statistical-mechanical spin system with quenched disorder. Here, we present results for the general fault-tolerant regime, where we consider both qubit and measurement errors. However, unlike in previous studies, here we vary the strength of the different error sources independently. Our results highlight peculiar differences between toric and color codes. This study complements previous results published in New J. Phys. 13, 083006 (2011).

Iterative Phase Optimization of Elementary Quantum Error Correcting Codes

Performing experiments on small-scale quantum computers is certainly a challenging endeavor. Many parameters need to be optimized to achieve high-fidelity operations. This can be done efficiently for operations acting on single qubits, as errors can be fully characterized. For multiqubit operations, though, this is no longer the case, as in the most general case, analyzing the effect of the operation on the system requires a full state tomography for which resources scale exponentially with the system size. Furthermore, in recent experiments, additional electronic levels beyond the two-level system encoding the qubit have been used to enhance the capabilities of quantum-information processors, which additionally increases the number of parameters that need to be controlled. For the optimization of the experimental system for a given task (e.g., a quantum algorithm), one has to find a satisfactory error model and also efficient observables to estimate the parameters of the model. In this manuscript, we demonstrate a method to optimize the encoding procedure for a small quantum error correction code in the presence of unknown but constant phase shifts. The method, which we implement here on a small-scale linear ion-trap quantum computer, is readily applicable to other AMO platforms for quantum-information processing.