Computational basis of decision-making impairment in multiple sclerosis

Background: Multiple sclerosis (MS) is commonly associated with decision-making, neurocognitive impairments, and mood and motivational symptoms. However, their relationship may be obscured by traditional scoring methods. Objectives: To study the computational basis underlying decision-making impairments in MS and their interaction with neurocognitive and neuropsychiatric measures. Methods: Twenty-nine MS patients and 26 matched control subjects completed a computer version of the Iowa Gambling Task (IGT). Participants underwent neurocognitive evaluation using an expanded version of the Brief Repeatable Battery. Hierarchical Bayesian Analysis was used to estimate three established computational models to compare parameters between groups. Results: Patients showed increased learning rate and reduced loss-aversion during decision-making relative to control subjects. These alterations were associated with: (1) reduced net gains in the IGT; (2) processing speed, executive functioning and memory impairments; and (3) higher levels of depression and current apathy. Conclusion: Decision-making deficits in MS patients could be described by the interplay between latent computational processes, neurocognitive impairments, and mood/motivational symptoms.

An efficient semi-quantum key distribution protocol based on EPR and single-particle hybridization

Quantum key distribution cannot satisfy some users without quantum capability, so semi-quantum key distribution emerges as the times required. Semi-quantum key distribution protocol is described as Alice has quantum ability to prepare and measure qubits with an arbitrary basis, while Bob only measures qubits with the computational basis or reflects qubits to Alice. However, most existing semi-quantum key distribution protocols have been performed with low eavesdropping detection probability. In this paper, we present an innovative semi-quantum key distribution protocol with high efficiency based on EPR and single-particle hybridization, in which the specific contents of {\scriptsize CTRL} or {\scriptsize SIFT} operations have been newly defined. Then, the security analysis indicates the proposed protocol is asymptotically secure with more high eavesdropping detection probability against individual eavesdropping attacks. Moreover, the efficiency analysis shows that the presented protocol is more efficient than similar literatures.

Towards Characterizing the Canonical Computations Generating Phenomenal Experience

Few people tackle the neural or computational basis of qualitative experience (Frith, 2019). Why? One major reason is that science and philosophy have both struggled to propose how we might even begin to start studying it. Here I propose that metacognitive computations, and the subjective feelings that go along with them, give us a solid starting point. Specifically, perceptual metacognition possesses unique properties that provide a powerful and unique opportunity for studying the neural and computational correlates of subjective experience, falling into three categories: (1) Metacognition is subjective: there is something it is like to feel ‘confident’; (2) Metacognitive processes are objectively characterizable: We can objectively observe metacognitive reports and define computational models to fit to empirical data; (3) Metacognition has multiple hierarchically-dependent “anchors”, presenting a unique computational opportunity for developing sensitive, specific models. I define this Metacognition as a Step Toward Explaining Phenomenology (M-STEP) approach to state that, given these properties, computational models of metacognition represent an empirically-tractable early step in identifying the generative process that constructs qualitative experience. By applying decades of developments in computational cognitive science and formal computational model comparisons to the specific properties of perceptual metacognition, we may reveal new and exciting insights about how the brain constructs subjective conscious experiences and the nature of those experiences themselves.

Efficient quantum measurement of Pauli operators in the presence of finite sampling error

Estimating the expectation value of an operator corresponding to an observable is a fundamental task in quantum computation. It is often impossible to obtain such estimates directly, as the computer is restricted to measuring in a fixed computational basis. One common solution splits the operator into a weighted sum of Pauli operators and measures each separately, at the cost of many measurements. An improved version collects mutually commuting Pauli operators together before measuring all operators within a collection simultaneously. The effectiveness of doing this depends on two factors. Firstly, we must understand the improvement offered by a given arrangement of Paulis in collections. In our work, we propose two natural metrics for quantifying this, operating under the assumption that measurements are distributed optimally among collections so as to minimise the overall finite sampling error. Motivated by the mathematical form of these metrics, we introduce SORTED INSERTION, a collecting strategy that exploits the weighting of each Pauli operator in the overall sum. Secondly, to measure all Pauli operators within a collection simultaneously, a circuit is required to rotate them to the computational basis. In our work, we present two efficient circuit constructions that suitably rotate any collection of k independent commuting n-qubit Pauli operators using at most kn−k(k+1)/2 and O(kn/log⁡k) two-qubit gates respectively. Our methods are numerically illustrated in the context of the Variational Quantum Eigensolver, where the operators in question are molecular Hamiltonians. As measured by our metrics, SORTED INSERTION outperforms four conventional greedy colouring algorithms that seek the minimum number of collections.

Drosophila local search emerges from iterative odometry of consecutive run lengths

SUMMARYThe ability to keep track of one’s location in space is a critical behavior for animals navigating to and from a salient location, but its computational basis remains unknown. Here, we tracked flies in a ring-shaped channel as they executed bouts of search, triggered by optogenetic activation of sugar receptors. Flies centered their back-and-forth local search excursions near fictive food locations by closely matching the length of consecutive runs. We tested a set of agent-based models that incorporate iterative odometry to store and retrieve the distance walked between consecutive events, such as reversals in walking direction. In contrast to memoryless models such as Lévy flight, simulations employing reversal-to-reversal integration recapitulated flies’ centered search behavior, even during epochs when the food stimulus was withheld or in experiments with multiple food sites. However, experiments in which flies reinitiated local search after circumnavigating the arena suggest that flies can also integrate azimuthal heading to perform path integration. Together, this work provides a concrete theoretical framework and experimental system to advance investigations of the neural basis of path integration.

The Prime state and its quantum relatives

The Prime state of n qubits, |Pn⟩, is defined as the uniform superposition of all the computational-basis states corresponding to prime numbers smaller than 2n. This state encodes, quantum mechanically, arithmetic properties of the primes. We first show that the Quantum Fourier Transform of the Prime state provides a direct access to Chebyshev-like biases in the distribution of prime numbers. We next study the entanglement entropy of |Pn⟩ up to n=30 qubits, and find a relation between its scaling and the Shannon entropy of the density of square-free integers. This relation also holds when the Prime state is constructed using a qudit basis, showing that this property is intrinsic to the distribution of primes. The same feature is found when considering states built from the superposition of primes in arithmetic progressions. Finally, we explore the properties of other number-theoretical quantum states, such as those defined from odd composite numbers, square-free integers and starry primes. For this study, we have developed an open-source library that diagonalizes matrices using floats of arbitrary precision.

Transforming task representations to perform novel tasks

An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superhuman performance in specific tasks often fail to adapt to even slight task alterations. To address this, we propose a general computational framework for adapting to novel tasks based on their relationship to prior tasks. We begin by learning vector representations of tasks. To adapt to new tasks, we propose metamappings, higher-order tasks that transform basic task representations. We demonstrate the effectiveness of this framework across a wide variety of tasks and computational paradigms, ranging from regression to image classification and reinforcement learning. We compare to both human adaptability and language-based approaches to zero-shot learning. Across these domains, metamapping is successful, often achieving 80 to 90% performance, without any data, on a novel task, even when the new task directly contradicts prior experience. We further show that metamapping can not only generalize to new tasks via learned relationships, but can also generalize using novel relationships unseen during training. Finally, using metamapping as a starting point can dramatically accelerate later learning on a new task and reduce learning time and cumulative error substantially. Our results provide insight into a possible computational basis of intelligent adaptability and offer a possible framework for modeling cognitive flexibility and building more flexible artificial intelligence systems.