Optimal state encoding for quantum walks and quantum communication over spin systems

Recent work has shown that a simple chain of interacting spins can be used as a medium for high-fidelity quantum communication. We describe a scheme for quantum communication using a spin system that conserves z-spin, but otherwise is arbitrary. The sender and receiver are assumed to directly control several spins each, with the sender encoding the message state onto the larger state-space of her control spins. We show how to find the encoding that maximises the fidelity of communication, using a simple method based on the singular-value decomposition. Also, we show that this solution can be used to increase communication fidelity in a rather different circumstance: where no encoding of initial states is used, but where the sender and receiver control exactly two spins each and vary the interactions on those spins over time. The methods presented are computationally efficient, and numerical examples are given for systems having up to 300 spins.Comment: 10 pages, LaTeX, 7 EPS figures. Corrected an error in the definition and interpretation of C_B(T

Learned predictiveness training modulates biases towards using boundary or landmark cues during navigation

A number of navigational theories state that learning about landmark information should not interfere with learning about shape information provided by the boundary walls of an environment. A common test of such theories has been to assess whether landmark information will overshadow, or restrict, learning about shape information. Whilst a number of studies have shown that landmarks are not able to overshadow learning about shape information, some have shown that landmarks can, in fact, overshadow learning about shape information. Given the continued importance of theories that grant the shape information that is provided by the boundary of an environment a special status during learning, the experiments presented here were designed to assess whether the relative salience of shape and landmark information could account for the discrepant results of overshadowing studies. In Experiment 1, participants were first trained that either the landmarks within an arena (landmark-relevant), or the shape information provided by the boundary walls of an arena (shape-relevant), were relevant to finding a hidden goal. In a subsequent stage, when novel landmark and shape information were made relevant to finding the hidden goal, landmarks dominated behaviour for those given landmark-relevant training, whereas shape information dominated behaviour for those given shape-relevant training. Experiment 2, which was conducted without prior relevance training, revealed that the landmark cues, unconditionally, dominated behaviour in our task. The results of the present experiments, and the conflicting results from previous overshadowing experiments, are explained in terms of associative models that incorporate an attention variant

Thinking outside of the box: Transfer of shape-based reorientation across the boundary of an arena

The way in which human and non-human animals represent the shape of their environments remains a contentious issue. According to local theories of shape learning, organisms encode the local geometric features of the environment that signal a goal location. In contrast, global theories of shape learning suggest that organisms encode the overall shape of the environment. There is, however, a surprising lack of evidence to support this latter claim, despite the fact that common behaviours seem to require encoding of the global-shape of an environment. We tested one such behaviour in 5 experiments, in which human participants were trained to navigate to a hidden goal on one side of a virtual arena (e.g. the inside) before being required to find the same point on the alternative side (e.g. the outside). Participants navigated to the appropriate goal location, both when inside and outside the virtual arena, but only when the shape of the arena remained the same between training and test (Experiments 1a and 1b). When the arena shape was transformed between these stages, participants were lost (Experiments 2a and 2b). When training and testing was conducted on the outside of two different-shaped arenas that shared local geometric cues participants once again explored the appropriate goal location (Experiment 3). These results provide core evidence that humans encode a global representation of the overall shape of the environments in, or around, which they navigate

Blocking Spatial Navigation Across Environments that have a Different Shape

According to the geometric module hypothesis, organisms encode a global representation of the space in which they navigate, and this representation is not prone to interference from other cues. A number of studies, however, have shown that both human and non-human animals can navigate on the basis of local geometric cues provided by the shape of an environment. According to the model of spatial learning proposed by Miller and Shettleworth (2007, 2008), geometric cues compete for associative strength in the same manner as non-geometric cues do. The experiments reported here were designed to test if humans learn about local geometric cues in a manner consistent with the Miller-Shettleworth model. Experiment 1 replicated previous findings that humans transfer navigational behavior, based on local geometric cues, from a rectangle-shaped environment to a kite-shaped environment, and vice versa. In Experiments 2 and 3, it was observed that learning about non-geometric cues blocked, and were blocked by, learning about local geometric cues. The reciprocal blocking observed is consistent with associative theories of spatial learning; however, it is difficult to explain the observed effects with theories of global-shape encoding in their current form

Quantum states far from the energy eigenstates of any local Hamiltonian

What quantum states are possible energy eigenstates of a many-body Hamiltonian? Suppose the Hamiltonian is non-trivial, i.e., not a multiple of the identity, and L-local, in the sense of containing interaction terms involving at most L bodies, for some fixed L. We construct quantum states \psi which are ``far away'' from all the eigenstates E of any non-trivial L-local Hamiltonian, in the sense that |\psi-E| is greater than some constant lower bound, independent of the form of the Hamiltonian.Comment: 4 page

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

Noise thresholds for optical quantum computers

In this Letter we numerically investigate the fault-tolerant threshold for optical cluster-state quantum computing. We allow both photon loss noise and depolarizing noise (as a general proxy for all local noise), and obtain a threshold region of allowed pairs of values for the two types of noise. Roughly speaking, our results show that scalable optical quantum computing is possible for photon loss probabilities < 3x10(-3), and for depolarization probabilities < 10(-4)

Noise thresholds for optical cluster-state quantum computation

In this paper we do a detailed numerical investigation of the fault-tolerant threshold for optical cluster-state quantum computation. Our noise model allows both photon loss and depolarizing noise, as a general proxy for all types of local noise other than photon loss noise. We obtain a threshold region of allowed pairs of values for the two types of noise. Roughly speaking, our results show that scalable optical quantum computing is possible for photon loss probabilities less than 0.003, and for depolarization probabilities less than 0.0001. Our fault-tolerant protocol involves a number of innovations, including a method for syndrome extraction known as telecorrection, whereby repeated syndrome measurements are guaranteed to agree. This paper is an extended version of [Dawson et al., Phys. Rev. Lett. 96, 020501].Comment: 28 pages. Corrections made to Table I

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the last mile implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain