On the relevance of avoided crossings away from quantum critical point to the complexity of quantum adiabatic algorithm

Two recent preprints [B. Altshuler, H. Krovi, and J. Roland, "Quantum adiabatic optimization fails for random instances of NP-complete problems", arXiv:0908.2782 and "Anderson localization casts clouds over adiabatic quantum optimization", arXiv:0912.0746] argue that random 4th order perturbative corrections to the energies of local minima of random instances of NP-complete problem lead to avoided crossings that cause the failure of quantum adiabatic algorithm (due to exponentially small gap) close to the end, for very small transverse field that scales as an inverse power of instance size N. The theoretical portion of this work does not to take into account the exponential degeneracy of the ground and excited states at zero field. A corrected analysis shows that unlike those in the middle of the spectrum, avoided crossings at the edge would require high [O(1)] transverse fields, at which point the perturbation theory may become divergent due to quantum phase transition. This effect manifests itself only in large instances [exp(0.02 N) >> 1], which might be the reason it had not been observed in the authors' numerical work. While we dispute the proposed mechanism of failure of quantum adiabatic algorithm, we cannot draw any conclusions on its ultimate complexity.Comment: 8 pages, 5 figure

Adiabatic Quantum Computing in systems with constant inter-qubit couplings

We propose an approach suitable for solving NP-complete problems via adiabatic quantum computation with an architecture based on a lattice of interacting spins (qubits) driven by locally adjustable effective magnetic fields. Interactions between qubits are assumed constant and instance-independent, programming is done only by changing local magnetic fields. Implementations using qubits coupled by magnetic-, electric-dipole and exchange interactions are discussed.Comment: 10 pages, 10 figures, reference adde

Quantum Adiabatic Evolution Algorithm and Quantum Phase Transition in 3-Satisfiability Problem

In this paper we show that the performance of the quantum adiabatic algorithm is determined by phase transitions in underlying problem in the presence of transverse magnetic field $\Gamma$. We show that the quantum version of random Satisfiability problem with 3 bits in a clause (3-SAT) has a first-order quantum phase transition. We analyze the phase diagram $\gamma=\gamma(\Gamma)$ where $\gamma$ is an average number of clauses per binary variable in 3-SAT. The results are obtained in a closed form assuming replica symmetry and neglecting time correlations at small values of the transverse field $\Gamma$. In the limit of $\Gamma=0$ the value of $\gamma(0)\approx$ 5.18 corresponds to that given by the replica symmetric treatment of a classical random 3-SAT problem. We demonstrate the qualitative similarity between classical and quantum versions of this problem.Comment: 30 pages, 7 figure

Complete spin polarization of degenerate electrons in semiconductors near ferromagnetic contacts

We show that spin polarization of electron density in nonmagnetic degenerate semiconductors can achieve 100%. This effect is realized in ferromagnet-semiconductor $FM-n^{+}$-$n$ junctions even at moderate spin selectivity of the $FM-n^{+}$ contact when the electrons are extracted from the heavily doped $n^{+}-$semiconductor into the ferromagnet. We derived a general equation relating spin polarization of the current to that of the electron density in nonmagnetic semiconductors. We found that the effect of the complete spin polarization is achieved near $n^{+}$-$n$ interface when an effective diffusion coefficient goes to zero in this region while the diffusion current remains finite

Complete spin extraction from semiconductors near ferromagnet-semiconductor interfaces

We show that spin polarization of electrons in nonmagnetic semiconductors near specially tailored ferromagnet-semiconductor junctions can achieve 100%. This effect is realized even at moderate spin injection coefficients of the contact when these coefficients only weakly depend on the current. The effect of complete spin extraction occurs at relatively strong electric fields and arises from a reduction of spin penetration length due to the drift of electrons from a semiconductor towards the spin-selective tunnel junction

Electronic Control and Readout of Qubit States in Solid State Quantum Computing Systems

We demonstrate that an $n^+/i/n^+$ junction is the most suitable candidate for electronic control and readout of qubit states in quantum computing systems based on shallow impurities. The signature of this system is that the $n^+-$regions serve as metallic electrodes separated form the $i-$region by a self-induced barrier (internal workfunction). The $n^+/i/n^+$ system mimics the properties of a metal-vacuum-metal junction with the qubit (impurity atom) placed in a ``vacuum'' $i$-region between two ``metallic'' $n^+$ electrodes. We will show that the self-induced barrier exists in a sufficiently wide range of the concentration of dopants in the $n^+$-semiconductor (e.g. up to $10^{21}$ cm$^{-3}$ for Si) and its height can be controlled by tuning the doping level. A shallow donor placed in a vacuum $i$-region will be populated with one electron in equilibrium. In the case of Li donor in Si the $n^+$-electrodes will be used for a precision placement of the Li atom during the growth process; for voltage control and manipulation of the qubit states; and for a qubit readout by means of the optically stimulated resonant tunnelling. Another important feature of our system is that the qubit states (first two lowest energy levels of Li in Si) are separated by an energy gap from a continuum of the many-body states of the controlling electrodes

Simulations of the adiabatic quantum optimization for the Set Partition Problem

We analyze the complexity of the quantum optimization algorithm based on adiabatic evolution for the set partition problem. We introduce a cost function defined on a logarithmic scale of the partition residues so that the total number of values of the cost function is of the order of the problem size. We simulate the behavior of the algorithm by numerical solution of the time-dependent Schroedinger equation as well as the stationary equation for the adiabatic eigenvalues. The numerical results for the time-dependent quantum evolution indicate that the complexity of the algorithm scales exponentially with the problem size.This result appears to contradict the recent numerical results for complexity of quantum adiabatic algorithm applied to a different NP-complete problem (Farhi et al, Science 292, p.472 (2001)).Comment: 13 pages, 5 figures Added simulation results for n=17 bit

Identification of nonlinear noisy dynamics of an ecosystem from observations of one of its trajectory components

The problem of determining dynamical models and trajectories that describe observed time-series data allowing for the understanding, prediction and possibly control of complex systems in nature is of a great interest in a wide variety of fields. Often, however, only part of the system's dynamical variables can be measured, the measurements are corrupted by noise and the dynamics is complicated by an interplay of nonlinearity and random perturbations. The problem of dynamical inference in these general settings is challenging researchers for decades. We solve this problem by applying a path-integral approach to fluctuational dynamics, and show that, given the measurements, the system trajectory can be obtained from the solution of the certain auxiliary Hamiltonian problem in which measured data act effectively as a control force driving the estimated trajectory toward the most probable one that provides a minimum to certain mechanical action. The dependance of the minimum action on the model parameters determines the statistical distribution in the model space consistent with the measurements. We illustrate the efficiency of the approach by solving an intensively studied problem from the population dynamics of predator-prey system where the prey populations may be observed while the predator populations or even their number is difficult or impossible to estimate. We apply our approach to recover both the unknown dynamics of predators and model parameters (including parameters that are traditionally very difficult to estimate) directly from measurements of the prey dynamics. We provide a comparison of our method with the Markov Chain Monte Carlo technique.Comment: 30 pages, 7 figure

Approximating satisfiability transition by suppressing fluctuations

Using methods and ideas from statistical mechanics, we propose a simple method for obtaining rigorous upper bounds for satisfiability transition in random boolean expressions composed of N variables and M clauses with K variables per clause. Determining the location of satisfiability threshold $\alpha_c=M/N$ for a number of difficult combinatorial problems is a major open problem in the theory of random graphs. The method is based on identification of the core -- a subexpression (subgraph) that has the same satisfiability properties as the original expression. We formulate self-consistency equations that determine macroscopic parameters of the core and compute an improved annealing bound. We illustrate the method for three sample problems: K-XOR-SAT, K-SAT and positive 1-in-K-SAT.Comment: 31 pages, 6 figure

Size dependence of the minimum excitation gap in the Quantum Adiabatic Algorithm

We study the typical (median) value of the minimum gap in the quantum version of the Exact Cover problem using Quantum Monte Carlo simulations, in order to understand the complexity of the quantum adiabatic algorithm (QAA) for much larger sizes than before. For a range of sizes, N <= 128, where the classical Davis-Putnam algorithm shows exponential median complexity, the QAA shows polynomial median complexity. The bottleneck of the algorithm is an isolated avoided crossing point of a Landau-Zener type (collision between the two lowest energy levels only).Comment: 4 pages, 5 figure