A survey on HHL algorithm: From theory to application in quantum machine learning

The Harrow-Hassidim-Lloyd (HHL) algorithm is a method to solve the quantum linear system of equations that may be found at the core of various scientific applications and quantum machine learning models including the linear regression, support vector machines and recommender systems etc. After reviewing the necessary background on elementary quantum algorithms, we provide detailed account of how HHL is exploited in different quantum machine learning (QML) models, and how it provides the desired quantum speedup in all these models. At the end, we briefly discuss some of the remaining challenges ahead for HHL-based QML models and related methods.     

Synthesis and characterization of binary and ternary III–V quantum dots

Quantum dots of InP, GaP, GaInP₂, and GaAs with diameters ranging from 20–80 Å can be synthesized as well-crystallized nanoparticles with bulk zinc blende structure. The synthesis is achieved by heating appropriate organometallic precursors with stabilizers in high boiling solvents for several days to produce QDs, which can then be dissolved in nonpolar organic solvents to form transparent colloidal QD dispersions. The high sample quality of the InP and Gap QDs results in excitonic features in the absorption spectra; excitonic features could not be observed for GaAs or GaInP₂ QDs. The GaP and GaInP₂ QD colloids exhibit very intense (quantum yields of 15–25%) visible photoluminescence at room temperature. The photoluminescence for InP QDs preparations show two emission bands: one band is in the visible at the band edge of the QD, and a second band appears above 800 nm. The near-IR PL is attributed to deep traps, presumably phosphorus vacancies on the QD surface. This band can be removed after controlled addition of etchant; subsequently, very intense band-edge emission (quantum yield 30%), which is tunable with particle size, is obtained. The QDs were characterized by TEM, SAXS, AFM, powder X-ray diffraction, steady-state optical absorption and photoluminescence spectroscopy, ps to ns transient photoluminescence spectroscopy, and fs to ps pump-probe absorption (i.e., hole-burning) spectroscopy.     

Escape from the quantum pigeon conundrum

It has recently been argued in Aharonov et al. (2016) that quantum mechanics violates the Pigeon Counting Principle (PCP) which states that if one distributes three pigeons among two boxes there must be at least two pigeons in one of the boxes. However, this conclusion cannot be justified by rigorous theoretical arguments. The issue is further complicated by experimental confirmation of the transition amplitudes predicted in this paper that nevertheless do not support the conclusion of PCP violation. Here we prove via a set of operator identities that the PCP is not violated within quantum mechanics, regardless of interpretation.     

Universality of black hole quantum computing

By analyzing the key properties of black holes from the point of view of quantum information, we derive a model‐independent picture of black hole quantum computing. It has been noticed that this picture exhibits striking similarities with quantum critical condensates, allowing the use of a common language to describe quantum computing in both systems. We analyze such quantum computing by allowing coupling to external modes, under the condition that the external influence must be soft‐enough in order not to offset the basic properties of the system. We derive model‐independent bounds on some crucial time‐scales, such as the times of gate operation, decoherence, maximal entanglement and total scrambling. We show that for black hole type quantum computers all these time‐scales are of the order of the black hole half‐life time. Furthermore, we construct explicitly a set of Hamiltonians that generates a universal set of quantum gates for the black hole type computer. We find that the gates work at maximal energy efficiency. Furthermore, we establish a fundamental bound on the complexity of quantum circuits encoded on these systems, and characterize the unitary operations that are implementable. It becomes apparent that the computational power is very limited due to the fact that the black hole life‐time is of the same order of the gate operation time. As a consequence, it is impossible to retrieve its information, within the life‐time of a black hole, by externally coupling to the black hole qubits. However, we show that, in principle, coupling to some of the internal degrees of freedom allows acquiring knowledge about the micro‐state. Still, due to the trivial complexity of operations that can be performed, there is no time advantage over the collection of Hawking radiation and subsequent decoding.     

The unique world of the Everett version of quantum theory

We ask whether the basic Everett assumption, that there are no changes of the wavefunction other than those given by the Schrödinger equation, is compatible with experience. We conclude that it is, provided we allow the world of observation to be partially a creation of consciousness. The model suggests the possible existence of quantum paranormal effects.     

Computing with a single qubit faster than the computation quantum speed limit

The possibility to save and process information in fundamentally indistinguishable states is the quantum mechanical resource that is not encountered in classical computing. I demonstrate that, if energy constraints are imposed, this resource can be used to accelerate information-processing without relying on entanglement or any other type of quantum correlations. In fact, there are computational problems that can be solved much faster, in comparison to currently used classical schemes, by saving intermediate information in nonorthogonal states of just a single qubit. There are also error correction strategies that protect such computations.     

Single-shot readout of electron spins in a semiconductor quantum dot

We report on a method for single-shot readout of spin states in a semiconductor quantum dot that is robust against charge noise and can be used even when the electron temperature exceeds the energy splitting between the states. The spin states are first correlated to different charge states using a spin dependence of the tunnel rates. A subsequent fast measurement of the charge on the dot then reveals the original spin state. The method is analyzed theoretically, and compared to a previously used method. We experimentally demonstrate the method by performing readout of the two-electron spin states, achieving a single-shot visibility of more than 80%. We find very long triplet-to-singlet relaxation times (up to several milliseconds), with a strong dependence on in-plane magnetic field.     

基于局部Gabor二进制模式的高光谱伪装目标图像分类

为提高光谱伪装目标图像分类精度,提出了一种基于局部Gabor二进制模式（LGBP)的空间分类方法。LGBP作为一种多尺度算法,被用来提取高光谱图像的纹理特征。然后高光谱图像中的每一个像元可以用一个光谱特征向量及一个纹理特征向量表示。通过这种方法,增大类间距离。最后使用多核支持向量机结合光谱信息和空间纹理信息实现对高光谱伪装目标图像的分类。实验证明了该方法的有效性,分类总体精度和Kappa系数分别达到了95.6%和0.937。所提出的方法对于提高分类精度及鲁棒性具有重要意义。     

Thermodynamic effects of single-qubit operations in silicon-based quantum computing

Silicon-based quantum logic is a promising technology to implement universal quantum computing. It is widely believed that a millikelvin cryogenic environment will be necessary to accommodate silicon-based qubits. This prompts a question of the ultimate scalability of the technology due to finite cooling capacity of refrigeration systems. In this work, we answer this question by studying energy dissipation due to interactions between nuclear spin impurities and qubit control pulses. We demonstrate that this interaction constrains the sustainable number of single-qubit operations per second for a given cooling capacity.     

The Large Hadron Collider: A Marvel of Technology,edited by Lyndon Evans

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.     

Template matching of mixed quantum states

We consider the problem of optimal classification of an unknown input mixed quantum state with respect to a set of predefined patterns C_i, each represented by a known mixed quantum template . The performance of the matching strategy is addressed within a Bayesian formulation where the cost function, as suggested by the theory of monotone distances between quantum states, is chosen to be the fidelity or the relative entropy between the input and the templates. We investigate various examples of quantum template matching for the case of a finite number of copies of a two-level input state and for a generic, group covariant, set of two-level template states.     

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.     

The classical skeleton of open quantum chaotic maps

We have studied two complementary decoherence measures, purity and fidelity, for a generic diffusive noise in two different chaotic systems (the baker map and the cat map). For both quantities, we have found classical structures in quantum mechanics-the scar functions-that are specially stable when subjected to environmental perturbations. We show that these quantum states constructed on classical invariants are the most robust significant quantum distributions in generic dissipative maps.     

An efficient implementation of the gliding box lacunarity algorithm

Lacunarity is a measure of how data fills space. It complements fractal dimension, which measures how much space is filled. Many researchers use the gliding box algorithm to calculate lacunarity. This paper introduces a fast algorithm for making this calculation. This algorithm is akin to fast box-counting algorithms used by some researchers to estimate fractal dimension. A simplified gliding box measure equation, along with key pseudo code implementations for the algorithm, are presented. Applications for the gliding box lacunarity measure have ranged from biological community modelling to target detection.     

Velocity of propagation in diffusional quantum theory

An equation of diffusional quantum theory which takes into account the finite velocity of propagation is derived from Kelvin's telegraph equation and Fürth's relation. The equation is then used to derive the ground state of quantum systems and to derive the Sommerfeld-Dirac expression for the ionization potential of hydrogen-like ions.     

Fast implementation of matched-filter-based automatic alignment image processing

Video images of laser beams imprinted with distinguishable features are used for alignment of 192 laser beams at the National Ignition Facility (NIF). Algorithms for determining the position of these beams enable control systems to perform the task of alignment. Real world beam images suffer from intensity fluctuation or other distortions, making algorithms susceptible to higher position measurement variability. Using matched filtering to identify beam positions results in greater stability of position measurement compared to centroiding techniques. However, this gain is achieved at the expense of extra processing time. This work explores the use of FPGAs to accelerate these computations. Results indicate a performance improvement of 20 times for an FPGA over a 3 GHz Pentium 4 processor.     

Special topics in the quantum theory of angular momentum

Two special topics in the quantum theory of angular momentum are discussed in this article. They are: (i) the relationship between the coupling and recoupling coefficients (for two and three angular momenta, respectively) and sets of generalized hyper-geometric functions of unit argument; and (ii) the ‘non-trivial’ or polynomial zeros of angular momentum coefficients and their classification.     

Dynamics of thermal effects in the spin-wave theory of quantum antiferromagnets

We derive a master equation that allows us to study non-equilibrium dynamics of a quantum antiferromagnet. By resorting to spin-wave theory, we obtain a closed analytic form for the magnon decay rates. These turn out to be closely related to form factors, which are experimentally accessible by means of neutron and Raman scattering. Furthermore, we compute the time evolution of the staggered magnetization showing that, for moderate temperatures, the magnetic order is not spoiled even if the coupling is fully isotropic.     

A curiosity concerning the role of coherent states in quantum field theory

In the usual Fock quantisation of fields in Minkowski space-time, one has the result that the expectation value of the quantum Hamiltonian in any coherent state equals the energy of the classical field at which the state is peaked. It is shown that this property can be used tocharacterise the usual Fock representation. It is also pointed out that the entire analysis goes through for a substantially more general class of systems including, in particular, Bose fields in arbitrary stationary space-times.     

Classical and quantum theory of the massive spin-two field

In this paper, we review classical and quantum field theory of massive non-interacting spin-two fields. We derive the equations of motion and Fierz–Pauli constraints via three different methods: the eigenvalue equations for the Casimir invariants of the Poincaré group, a Lagrangian approach, and a covariant Hamilton formalism. We also present the conserved quantities, the solution of the equations of motion in terms of polarization tensors, and the tree-level propagator. We then discuss canonical quantization by postulating commutation relations for creation and annihilation operators. We express the energy, momentum, and spin operators in terms of the former. As an application, quark–antiquark currents for tensor mesons are presented. In particular, the current for tensor mesons with quantum numbers J^PC=2⁻⁺$J^{PC}=2^{-+}$ is, to our knowledge, given here for the first time.