Fault-tolerant linear optical quantum computing with small-amplitude coherent states

Quantum computing using two optical coherent states as qubit basis states has been suggested as an interesting alternative to single photon optical quantum computing with lower physical resource overheads. These proposals have been questioned as a practical way of performing quantum computing in the short term due to the requirement of generating fragile diagonal states with large coherent amplitudes. Here we show that by using a fault-tolerant error correction scheme, one need only use relatively small coherent state amplitudes ($\alpha > 1.2$) to achieve universal quantum computing. We study the effects of small coherent state amplitude and photon loss on fault tolerance within the error correction scheme using a Monte Carlo simulation and show the quantity of resources used for the first level of encoding is orders of magnitude lower than the best known single photon scheme. %We study this reigem using a Monte Carlo simulation and incorporate %the effects of photon loss in this simulation

Learned changes in outcome associability

When a cue reliably predicts an outcome, the associability of that cue will change. Associative theories of learning propose this change will persist even when the same cue is paired with a different outcome. These theories, however, do not extend the same privilege to an outcome; an outcome’s learning history is deemed to have no bearing on subsequent new learning involving that outcome. Two experiments were conducted which sought to investigate this assumption inherent in these theories using a serial letter-prediction task. In both experiments participants were exposed, in Stage 1, to a predictable outcome (‘X’) and an unpredictable outcome (‘Z’). In Stage 2 participants were exposed to the same outcomes preceded by novel cues which were equally predictive of both outcomes. Both experiments revealed that participants’ learning toward the previously predictable outcome was more rapid in Stage 2 than the previously unpredicted outcome. The implications of these results for theories of associative learning are discussed

Level Density of a Bose Gas and Extreme Value Statistics

We establish a connection between the level density of a gas of non-interacting bosons and the theory of extreme value statistics. Depending on the exponent that characterizes the growth of the underlying single-particle spectrum, we show that at a given excitation energy the limiting distribution function for the number of excited particles follows the three universal distribution laws of extreme value statistics, namely Gumbel, Weibull and Fr\'echet. Implications of this result, as well as general properties of the level density at different energies, are discussed.Comment: 4 pages, no figure

Fault Tolerance in Parity-State Linear Optical Quantum Computing

We use a combination of analytical and numerical techniques to calculate the noise threshold and resource requirements for a linear optical quantum computing scheme based on parity-state encoding. Parity-state encoding is used at the lowest level of code concatenation in order to efficiently correct errors arising from the inherent nondeterminism of two-qubit linear-optical gates. When combined with teleported error-correction (using either a Steane or Golay code) at higher levels of concatenation, the parity-state scheme is found to achieve a saving of approximately three orders of magnitude in resources when compared to a previous scheme, at a cost of a somewhat reduced noise threshold.Comment: LaTeX, 10 pages, introduction updated for journal submissio

Lower bounds on the complexity of simulating quantum gates

We give a simple proof of a formula for the minimal time required to simulate a two-qubit unitary operation using a fixed two-qubit Hamiltonian together with fast local unitaries. We also note that a related lower bound holds for arbitrary n-qubit gates.Comment: 6 page

A Bio-Logical Theory of Animal Learning

This article provides the foundation for a new predictive theory of animal learning that is based upon a simple logical model. The knowledge of experimental subjects at a given time is described using logical equations. These logical equations are then used to predict a subject’s response when presented with a known or a previously unknown situation. This new theory suc- cessfully anticipates phenomena that existing theories predict, as well as phenomena that they cannot. It provides a theoretical account for phenomena that are beyond the domain of existing models, such as extinction and the detection of novelty, from which “external inhibition” can be explained. Examples of the methods applied to make predictions are given using previously published results. The present theory proposes a new way to envision the minimal functions of the nervous system, and provides possible new insights into the way that brains ultimately create and use knowledge about the world

Brokered Graph State Quantum Computing

We describe a procedure for graph state quantum computing that is tailored to fully exploit the physics of optically active multi-level systems. Leveraging ideas from the literature on distributed computation together with the recent work on probabilistic cluster state synthesis, our model assigns to each physical system two logical qubits: the broker and the client. Groups of brokers negotiate new graph state fragments via a probabilistic optical protocol. Completed fragments are mapped from broker to clients via a simple state transition and measurement. The clients, whose role is to store the nascent graph state long term, remain entirely insulated from failures during the brokerage. We describe an implementation in terms of NV-centres in diamond, where brokers and clients are very naturally embodied as electron and nuclear spins.Comment: 5 pages, 3 figure

Potential of a multiparametric optical sensor for determining in situ the maturity components of red and white vitis vinifera wine grapes

A non-destructive fluorescence-based technique for evaluating Vitis vinifera L. grape maturity using a portable sensor (Multiplex ®) is presented. It provides indices of anthocyanins and chlorophyll in Cabernet Sauvignon, Merlot and Sangiovese red grapes and of flavonols and chlorophyll in Vermentino white grapes. The good exponential relationship between the anthocyanin index and the actual anthocyanin content determined by wet chemistry was used to estimate grape anthocyanins from in field sensor data during ripening. Marked differences were found in the kinetics and the amount of anthocyanins between cultivars and between seasons. A sensor-driven mapping of the anthocyanin content in the grapes, expressed as g/kg fresh weight, was performed on a 7-ha vineyard planted with Sangiovese. In the Vermentino, the flavonol index was favorably correlated to the actual content of berry skin flavonols determined by means of HPLC analysis of skin extracts. It was used to make a non-destructive estimate of the evolution in the flavonol concentration in grape berry samplings. The chlorophyll index was inversely correlated in linear manner to the total soluble solids (°Brix): it could, therefore, be used as a new index of technological maturity. The fluorescence sensor (Multiplex) possesses a high potential for representing an important innovative tool for controlling grape maturity in precision viticulture