Quantum Algorithms

This article surveys the state of the art in quantum computer algorithms, including both black-box and non-black-box results. It is infeasible to detail all the known quantum algorithms, so a representative sample is given. This includes a summary of the early quantum algorithms, a description of the Abelian Hidden Subgroup algorithms (including Shor's factoring and discrete logarithm algorithms), quantum searching and amplitude amplification, quantum algorithms for simulating quantum mechanical systems, several non-trivial generalizations of the Abelian Hidden Subgroup Problem (and related techniques), the quantum walk paradigm for quantum algorithms, the paradigm of adiabatic algorithms, a family of ``topological'' algorithms, and algorithms for quantum tasks which cannot be done by a classical computer, followed by a discussion.Comment: 71 pages, 1 figure, to appear in the Springer Encyclopedia of Complexity and Systems Scienc

Secure Biometrics: Concepts, Authentication Architectures and Challenges

BIOMETRICS are an important and widely used class of methods for identity verification and access control. Biometrics are attractive because they are inherent properties of an individual. They need not be remembered like passwords, and are not easily lost or forged like identifying documents. At the same time, bio- metrics are fundamentally noisy and irreplaceable. There are always slight variations among the measurements of a given biometric, and, unlike passwords or identification numbers, biometrics are derived from physical characteristics that cannot easily be changed. The proliferation of biometric usage raises critical privacy and security concerns that, due to the noisy nature of biometrics, cannot be addressed using standard cryptographic methods. In this article we present an overview of "secure biometrics", also referred to as "biometric template protection", an emerging class of methods that address these concerns.Comment: 16 pages, 11 figures, 1 tabl

Abelian Hypergroups and Quantum Computation

Motivated by a connection, described here for the first time, between the hidden normal subgroup problem (HNSP) and abelian hypergroups (algebraic objects that model collisions of physical particles), we develop a stabilizer formalism using abelian hypergroups and an associated classical simulation theorem (a la Gottesman-Knill). Using these tools, we develop the first provably efficient quantum algorithm for finding hidden subhypergroups of nilpotent abelian hypergroups and, via the aforementioned connection, a new, hypergroup-based algorithm for the HNSP on nilpotent groups. We also give efficient methods for manipulating non-unitary, non-monomial stabilizers and an adaptive Fourier sampling technique of general interest.Comment: 41 pages + 6 pages appendices. Added references and corrected typos in this version (sections 1-2

Analog to Digital Cognitive Radio: Sampling, Detection and Hardware

The proliferation of wireless communications has recently created a bottleneck in terms of spectrum availability. Motivated by the observation that the root of the spectrum scarcity is not a lack of resources but an inefficient managing that can be solved, dynamic opportunistic exploitation of spectral bands has been considered, under the name of Cognitive Radio (CR). This technology allows secondary users to access currently idle spectral bands by detecting and tracking the spectrum occupancy. The CR application revisits this traditional task with specific and severe requirements in terms of spectrum sensing and detection performance, real-time processing, robustness to noise and more. Unfortunately, conventional methods do not satisfy these demands for typical signals, that often have very high Nyquist rates. Recently, several sampling methods have been proposed that exploit signals' a priori known structure to sample them below the Nyquist rate. Here, we review some of these techniques and tie them to the task of spectrum sensing in the context of CR. We then show how issues related to spectrum sensing can be tackled in the sub-Nyquist regime. First, to cope with low signal to noise ratios, we propose to recover second-order statistics from the low rate samples, rather than the signal itself. In particular, we consider cyclostationary based detection, and investigate CR networks that perform collaborative spectrum sensing to overcome channel effects. To enhance the efficiency of the available spectral bands detection, we present joint spectrum sensing and direction of arrival estimation methods. Throughout this work, we highlight the relation between theoretical algorithms and their practical implementation. We show hardware simulations performed on a prototype we built, demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of CR.Comment: Submitted to IEEE Signal Processing Magazin

From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals

Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support occupies only a small portion of a wide spectrum. Our primary design goals are efficient hardware implementation and low computational load on the supporting digital processing. We propose a system, named the modulated wideband converter, which first multiplies the analog signal by a bank of periodic waveforms. The product is then lowpass filtered and sampled uniformly at a low rate, which is orders of magnitude smaller than Nyquist. Perfect recovery from the proposed samples is achieved under certain necessary and sufficient conditions. We also develop a digital architecture, which allows either reconstruction of the analog input, or processing of any band of interest at a low rate, that is, without interpolating to the high Nyquist rate. Numerical simulations demonstrate many engineering aspects: robustness to noise and mismodeling, potential hardware simplifications, realtime performance for signals with time-varying support and stability to quantization effects. We compare our system with two previous approaches: periodic nonuniform sampling, which is bandwidth limited by existing hardware devices, and the random demodulator, which is restricted to discrete multitone signals and has a high computational load. In the broader context of Nyquist sampling, our scheme has the potential to break through the bandwidth barrier of state-of-the-art analog conversion technologies such as interleaved converters.Comment: 17 pages, 12 figures, to appear in IEEE Journal of Selected Topics in Signal Processing, the special issue on Compressed Sensin

Quantum computing from a mathematical perspective: a description of the quantum circuit model

This paper is an essentially self-contained and rigorous description of the fundamental principles of quantum computing from a mathematical perspective. It is intended to help mathematicians who want to get a grasp of this quickly growing discipline and find themselves taken aback by the language gap between mathematics and the pioneering fields on the matter: computer science and quantum physics

Automatically generating Feynman rules for improved lattice field theories

Deriving the Feynman rules for lattice perturbation theory from actions and operators is complicated, especially when improvement terms are present. This physically important task is, however, suitable for automation. We describe a flexible algorithm for generating Feynman rules for a wide range of lattice field theories including gluons, relativistic fermions and heavy quarks. We also present an efficient implementation of this in a freely available, multi-platform programming language (\python), optimised to deal with a wide class of lattice field theories

Quantum Fourier Transforms and the Complexity of Link Invariants for Quantum Doubles of Finite Groups

Knot and link invariants naturally arise from any braided Hopf algebra. We consider the computational complexity of the invariants arising from an elementary family of finite-dimensional Hopf algebras: quantum doubles of finite groups (denoted D(G), for a group G). Regarding algorithms for these invariants, we develop quantum circuits for the quantum Fourier transform over D(G); in general, we show that when one can uniformly and efficiently carry out the quantum Fourier transform over the centralizers Z(g) of the elements of G, one can efficiently carry out the quantum Fourier transform over D(G). We apply these results to the symmetric groups to yield efficient circuits for the quantum Fourier transform over D(S_n). With such a Fourier transform, it is straightforward to obtain additive approximation algorithms for the related link invariant. Additionally, we show that certain D(G) invariants (such as D(A_n) invariants) are BPP-hard to additively approximate, SBP-hard to multiplicatively approximate, and #P-hard to exactly evaluate. Finally, we make partial progress on the question of simulating anyonic computation in groups uniformly as a function of the group size. In this direction, we provide efficient quantum circuits for the Clebsch-Gordan transform over D(G) for "fluxon" irreps, i.e., irreps of D(G) characterized by a conjugacy class of G. For general irreps, i.e., those which are associated with a conjugacy class of G and an irrep of a centralizer, we present an efficient implementation under certain conditions such as when there is an efficient Clebsch-Gordan transform over the centralizers. We remark that this also provides a simulation of certain anyonic models of quantum computation, even in circumstances where the group may have size exponential in the size of the circuit.Comment: 30 page

Quantum Complexity: restrictions on algorithms and architectures

A dissertation submitted to the University of Bristol in accordance with the requirements of the degree of Doctor of Philosophy (PhD) in the Faculty of Engineering, Department of Computer Science, July 2009.Comment: 137 pages, 10 figs

Sub-Nyquist Wideband Spectrum Sensing and Sharing

PhDThe rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate e cient usage of the underutilized spectrum. Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access by enhancing the opportunities of exploring spectral holes, but entails a major implemen- tation challenge in compact commodity radios that have limited energy and computation capabilities. The sampling rates speci ed by the Shannon-Nyquist theorem impose great challenges both on the acquisition hardware and the subsequent storage and digital sig- nal processors. Sub-Nyquist sampling was thus motivated to sample wideband signals at rates far lower than the Nyquist rate, while still retaining the essential information in the underlying signals. This thesis proposes several algorithms for invoking sub-Nyquist sampling in wideband spectrum sensing. Speci cally, a sub-Nyquist wideband spectrum sensing algorithm is proposed that achieves wideband sensing independent of signal sparsity without sampling at full bandwidth by using the low-speed analog-to-digital converters based on sparse Fast Fourier Transform. To lower signal spectrum sparsity while maintaining the channel state information, the received signal is pre-processed through a proposed permutation and ltering algorithm. Additionally, a low-complexity sub-Nyquist wideband spectrum sensing scheme is proposed that locates occupied channels blindly by recovering the sig- nal support, based on the jointly sparse nature of multiband signals. Exploiting the common signal support shared among multiple secondary users, an e cient coopera- tive spectrum sensing scheme is developed, in which the energy consumption on signal acquisition, processing, and transmission is reduced with the detection performance guar- antee. To further reduce the computation complexity of wideband spectrum sensing, a hybrid framework of sub-Nyquist wideband spectrum sensing with geolocation database is explored. Prior channel information from geolocation database is utilized in the sens- ing process to reduce the processing requirements on the sensor nodes. The models of the proposed algorithms are derived and veri ed by numerical analyses and tested on both real-world and simulated TV white space signals