Quantum Isomorphic Simulation

A new type of quantum simulator is proposed which can simulate any quantum many-body system in an isomorphic manner. It can actually synthesize a duplicate of the system to be simulated. The isomorphic simulation has the great advantage that the inevitable coupling of the simulator to the environment can be fully exploited in simulating thermodynamic processes.Comment: 3 page

Single Molecule Magnetic Resonance and Quantum Computation

It is proposed that nuclear (or electron) spins in a trapped molecule would be well isolated from the environment and the state of each spin can be measured by means of mechanical detection of magnetic resonance. Therefore molecular traps make an entirely new approach possible for spin-resonance quantum computation which can be conveniently scaled up. In the context of magnetic resonance spectroscopy, a molecular trap promises the ultimate sensitivity for single spin detection and an unprecedented spectral resolution as well

Static Quantum Computation

Tailoring many-body interactions among a proper quantum system endows it with computing ability by means of static quantum computation in the sense that some of the physical degrees of freedom can be used to store binary information and the corresponding binary variables satisfy some given logic relations if and only if the system is in the ground state. Two theorems are proved showing that the universal static quantum computer can encode the solutions for any P and NP problem into its ground state using only polynomial number (in the problem input size) of logic gates. The second step is to read out the solutions by relaxing the system. The time complexity is relevant when one tries to read out the solution by relaxing the system, therefore our model of static quantum computation provides a new connection between the computational complexity and the dynamics of a complex system.Comment: 8 pages, 3 ps figure

Tailoring Many-Body Interactions to Solve Hard Combinatorial Problems

A quantum machine consisting of interacting linear clusters of atoms is proposed for the 3SAT problem. Each cluster with two relevant states of collective motion can be used to register a Boolean variable. Given any 3SAT Boolean formula the interactions among the clusters can be so tailored that the ground state(s) (possibly degenerate) of the whole system encodes the satisfying truth assignment(s) for it. This relates the 3SAT problem to the dynamics of the properly designed glass system.Comment: Latex 7 pages, 3 ps figure

Emergence of grid-like representations by training recurrent neural networks to perform spatial localization

Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. All these different functional types of neurons have been observed experimentally. The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits

Trapping Quantum Coherence in Local Energy Minima

Clusters of solid-state quantum devices have long-living metastable states of local energy minima which may be used to store quantum information. The low to vanishing rate of dissipation fulfils the prerequisite to maintain quantum coherence. Then physical symmetrization of the devices could minimize the couplings of the clusters to environmental degrees of freedom so to reduce the rate of decoherence. Combined with various other error correction mechanisms and methods, such designs and optimizations could render solid-state devices useful for quantum information processing, which have the advantages of flexibility in state manipulation and system scaling.Comment: Latex fil

NMR Quantum Automata in Doped Crystals

In a lattice ${\cal L}$ of nuclear spins with ABCABCABC... type periodic structure embedded in a single-crystal solid, each ABC-unit can be used to store quantum information and the information can be moved around via some cellular shifting mechanism. Impurity doping marks a special site D$\not\in{\cal L}$ which together with the local spin lattice constitute a quantum automaton where the D site serves as the input/output port and universal quantum logic is done through two-body interactions between two spins at D and a nearby site. The novel NMR quantum computer can be easily scaled up and may work at low temperature to overcome the problem of exponential decay in signal-to-noise ratio in room temperature NMR.Comment: Latex file, one eps figur

Superconductive Static Quantum Logic

Superconducting rings with exactly $\Phi _0/2$ magnetic flux threading are analogous of Ising spins having two degenerate states which can be used to store binary information. When brought close these rings interact by means of magnetic coupling. If the interactions are properly tailored, a system of such superconducting rings can accomplish static quantum logic in the sense that the states of the rings interpreted as Boolean variables satisfy the desired logic relations when and only when the whole system is in the ground state. Such static logic is essential to carry out the static quantum computation [1,2].Comment: Latex 4 pages, 4 ps figure

Dynamic self-organized error-correction of grid cells by border cells

Grid cells in the entorhinal cortex are believed to establish their regular, spatially correlated firing patterns by path integration of the animal's motion. Mechanisms for path integration, e.g. in attractor network models, predict stochastic drift of grid responses, which is not observed experimentally. We demonstrate a biologically plausible mechanism of dynamic self-organization by which border cells, which fire at environmental boundaries, can correct such drift in grid cells. In our model, experience-dependent Hebbian plasticity during exploration allows border cells to learn connectivity to grid cells. Border cells in this learned network reset the phase of drifting grids. This error-correction mechanism is robust to environmental shape and complexity, including enclosures with interior barriers, and makes distinctive predictions for environmental deformation experiments. Our work demonstrates how diverse cell types in the entorhinal cortex could interact dynamically and adaptively to achieve robust path integration.Comment: 6 pages, 4 figure

Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE

The ability to record activities from hundreds of neurons simultaneously in the brain has placed an increasing demand for developing appropriate statistical techniques to analyze such data. Recently, deep generative models have been proposed to fit neural population responses. While these methods are flexible and expressive, the downside is that they can be difficult to interpret and identify. To address this problem, we propose a method that integrates key ingredients from latent models and traditional neural encoding models. Our method, pi-VAE, is inspired by recent progress on identifiable variational auto-encoder, which we adapt to make appropriate for neuroscience applications. Specifically, we propose to construct latent variable models of neural activity while simultaneously modeling the relation between the latent and task variables (non-neural variables, e.g. sensory, motor, and other externally observable states). The incorporation of task variables results in models that are not only more constrained, but also show qualitative improvements in interpretability and identifiability. We validate pi-VAE using synthetic data, and apply it to analyze neurophysiological datasets from rat hippocampus and macaque motor cortex. We demonstrate that pi-VAE not only fits the data better, but also provides unexpected novel insights into the structure of the neural codes