Dimitrios Alanis

Quantum-Assisted Routing Optimization for Self-Organizing Networks

Self-organizing networks act autonomously for the sake of achieving the best possible performance. The attainable routing depends on a delicate balance of diverse and often conflicting quality-of-service requirements. Finding the optimal solution typically becomes an nonolynomial-hard problem, as the network size increases in terms of the number of nodes. Moreover, the employment of user-defined utility functions for the aggregation of the different objective functions often leads to suboptimal solutions. On the other hand, Pareto optimality is capable of amalgamating the different design objectives by providing an element of elitism. Although there is a plethora of bioinspired algorithms that attempt to address this optimization problem, they often fail to generate all the points constituting the optimal Pareto front. As a remedy, we propose an optimal multiobjective quantum-assisted algorithm, namely the nondominated quantum optimization algorithm (NDQO), which evaluates the legitimate routes using the concept of Pareto optimality at a reduced complexity. We then compare the performance of the NDQO algorithm to the state-of-the-art evolutionary algorithms, demonstrating that the NDQO algorithm achieves a near-optimal performance. Furthermore, we analytically derive the upper and lower bounds of the NDQO algorithmic complexity, which is of the order of O(N) and O(N√(N)) in the best and worst case scenario, respectively. This corresponds to a substantial complexity reduction of the NDQO from the order of O(N2) imposed by the brute-force method.

Low-Complexity Soft-Output Quantum-Assisted Multiuser Detection for Direct-Sequence Spreading and Slow Subcarrier-Hopping Aided SDMA-OFDM Systems

Low-complexity suboptimal multiuser detectors (MUDs) are widely used in multiple access communication systems for separating users, since the computational complexity of the maximum likelihood (ML) detector is potentially excessive for practical implementation. Quantum computing may be invoked in the detection procedure, by exploiting its inherent parallelism for approaching the ML MUDs performance at a substantially reduced number of cost function evaluations. In this contribution, we propose a soft-output (SO) quantum-assisted MUD achieving a near-ML performance and compare it to the corresponding SO ant colony optimization MUD. We investigate rank deficient direct-sequence spreading (DSS) and slow subcarrier-hopping aided (SSCH) spatial division multiple access orthogonal frequency division multiplexing systems, where the number of users to be detected is higher than the number of receive antenna elements used. We show that for a given complexity budget, the proposed SO-Dürr-Høyer algorithm (DHA) QMUD achieves a better performance. We also propose an adaptive hybrid SO-ML/SO-DHA MUD, which adapts itself to the number of users equipped with the same spreading sequence and transmitting on the same subcarrier. Finally, we propose a DSS-based uniform SSCH scheme, which improves the systems performance by 0.5 dB at a BER of 10-5, despite reducing the complexity required by the MUDs employed.

The Road From Classical to Quantum Codes: A Hashing Bound Approaching Design Procedure

Powerful quantum error correction codes (QECCs) are required for stabilizing and protecting fragile qubits against the undesirable effects of quantum decoherence. Similar to classical codes, hashing bound approaching QECCs may be designed by exploiting a concatenated code structure, which invokes iterative decoding. Therefore, in this paper, we provide an extensive step-by-step tutorial for designing extrinsic information transfer (EXIT) chart-aided concatenated quantum codes based on the underlying quantum-to-classical isomorphism. These design lessons are then exemplified in the context of our proposed quantum irregular convolutional code (QIRCC), which constitutes the outer component of a concatenated quantum code. The proposed QIRCC can be dynamically adapted to match any given inner code using EXIT charts, hence achieving a performance close to the hashing bound. It is demonstrated that our QIRCC-based optimized design is capable of operating within 0.4 dB of the noise limit.

Iterative Quantum-Assisted Multi-User Detection for Multi-Carrier Interleave Division Multiple Access Systems

With the proliferation of smart-phones and tablet PCs, the data rates of wireless communications have been soaring. Hence, the need for power-efficient communications relying on low-complexity multiple-stream detectors has become more pressing than ever. As a remedy, in this paper we design low-complexity soft-input soft-output quantum-assisted multi-user detectors (QMUD), which may be conveniently incorporated into state-of-the-art iterative receivers. Our design relies on extrinsic information transfer charts. Our QMUDs are then employed in multi-carrier interleave-division multiple-access (MC-IDMA) systems, which are investigated in the context of different channel code rate and spreading factor pairs, whilst fixing the total bandwidth requirement. One of our QMUDs is found to operate within 0.5 dB of the classical maximum a posteriori probability MUD after three iterations between the MUD and the decoders, while requiring only half its complexity, at a BER of 10-5 in the uplink of a rank-deficient MC-IDMA system relying on realistic imperfect channel estimation at the receiver, while supporting 14 users transmitting QPSK symbols.

Non-Dominated Quantum Iterative Routing Optimization for Wireless Multihop Networks

Routing in wireless multihop networks (WMHNs) relies on a delicate balance of diverse and often conflicting parameters, when aiming for maximizing the WMHN performance. Classified as a non-deterministic polynomial-time hard problem, routing in WMHNs requires sophisticated methods. As a benefit of observing numerous variables in parallel, quantum computing offers a promising range of algorithms for complexity reduction by exploiting the principle of quantum parallelism (QP), while achieving the optimum full-search-based performance. In fact, the so-called non-dominated quantum optimization (NDQO) algorithm has been proposed for addressing the multiobjective routing problem with the goal of achieving a near-optimal performance, while imposing a complexity of the order of O(N) and O(N√N) in the best and worst case scenarios, respectively. However, as the number of nodes in the WMHN increases, the total number of routes increases exponentially, making its employment infeasible despite the complexity reduction offered. Therefore, we propose a novel optimal quantum-assisted algorithm, namely, the non-dominated quantum iterative optimization (NDQIO) algorithm, which exploits the synergy between the hardware and the QP for the sake of achieving a further complexity reduction, which is on the order of O(√N) and O(N√N) in the best and worst case scenarios, respectively. In addition, we provide simulation results for demonstrating that our NDQIO algorithm achieves an average complexity reduction of almost an order of magnitude compared with the near-optimal NDQO algorithm, while having the same order of power consumption.

Noncoherent Quantum Multiple Symbol Differential Detection for Wireless Systems

In large-dimensional wireless systems, such as cooperative multicell processing, millimeterwave, and massive multiple input multiple output systems, or cells having a high user density, such as airports, train stations, and metropolitan areas, sufficiently accurate estimation of all the channel gains is required for performing coherent detection. Therefore, they may impose an excessive complexity. As an attractive design alternative, differential modulation relying on noncoherent detection may be invoked for eliminating the requirement for channel estimation at the base station, although at the cost of some performance degradation. In this treatise, we propose low-complexity hard-input hard-output, hard-input soft-output, as well as soft-input soft-output quantum-assisted multiple symbol differential detectors (MSDDs) that perform equivalently to the optimal, but highly complex maximum a posteriori probability MSDDs in multiuser systems, where the users are separated both in the frequency domain and in the time domain. When using an MSDD, the detection of a users symbols is performed over windows of differentially modulated symbols; hence, they exhibit an increased complexity with respect to the conventional differential detector while simultaneously improving the performance of the system, especially at high Doppler frequencies.

Fifteen Years of Quantum LDPC Coding and Improved Decoding Strategies

The near-capacity performance of classical low-density parity check (LDPC) codes and their efficient iterative decoding makes quantum LDPC (QLPDC) codes a promising candidate for quantum error correction. In this paper, we present a comprehensive survey of QLDPC codes from the perspective of code design as well as in terms of their decoding algorithms. We also conceive a modified non-binary decoding algorithm for homogeneous Calderbank-Shor-Steane-type QLDPC codes, which is capable of alleviating the problems imposed by the unavoidable length-four cycles. Our modified decoder outperforms the state-of-the-art decoders in terms of their word error rate performance, despite imposing a reduced decoding complexity. Finally, we intricately amalgamate our modified decoder with the classic uniformly reweighted belief propagation for the sake of achieving an improved performance.

Quantum-Aided Multi-User Transmission in Non-Orthogonal Multiple Access Systems

With the research on implementing a universal quantum computer being under the technological spotlight, new possibilities appear for their employment in wireless communications systems for reducing their complexity and improving their performance. In this treatise, we consider the downlink of a rank-deficient, multi-user system and we propose the discrete-valued and continuous-valued quantum-assisted particle swarm optimization (PSO) algorithms for performing vector perturbation precoding, as well as for lowering the required transmission power at the base station (BS), while minimizing the expected average bit error ratio (BER) at the mobile terminals. We use the minimum BER criterion. We show that the novel quantum-assisted precoding methodology results in an enhanced BER performance, when compared with that of a classical methodology employing the PSO algorithm, while requiring the same computational complexity in the challenging rank-deficient scenarios, where the number of transmit antenna elements at the BS is lower than the number of users. Moreover, when there is limited channel state information feedback from the users to the BS, due to the necessary quantization of the channel states, the proposed quantum-assisted precoder outperforms the classical precoder.

Unity-rate codes maximize the normalized throughput of on–off keying visible light communication

In this letter, we aim for maximizing the throughput of a visible light communication (VLC) system. Explicitly, we conceive a soft-in soft-out decoder providing soft feedback for the classic run length limited (RLL) codes, hence facilitating iterative decoding for exchanging valuable extrinsic information between the RLL and the error correction modules of a VLC system. Furthermore, we propose a unity rate code for our VLC system, which, hence, becomes capable of matching the ON–OFF keying capacity, while maintaining a flicker-free dimming value of 50%.

Quantum Error Correction Protects Quantum Search Algorithms Against Decoherence

When quantum computing becomes a wide-spread commercial reality, Quantum Search Algorithms (QSA) and especially Grover’s QSA will inevitably be one of their main applications, constituting their cornerstone. Most of the literature assumes that the quantum circuits are free from decoherence. Practically, decoherence will remain unavoidable as is the Gaussian noise of classic circuits imposed by the Brownian motion of electrons, hence it may have to be mitigated. In this contribution, we investigate the effect of quantum noise on the performance of QSAs, in terms of their success probability as a function of the database size to be searched, when decoherence is modelled by depolarizing channels’ deleterious effects imposed on the quantum gates. Moreover, we employ quantum error correction codes for limiting the effects of quantum noise and for correcting quantum flips. More specifically, we demonstrate that, when we search for a single solution in a database having 4096 entries using Grover’s QSA at an aggressive depolarizing probability of 10−3, the success probability of the search is 0.22 when no quantum coding is used, which is improved to 0.96 when Steane’s quantum error correction code is employed. Finally, apart from Steane’s code, the employment of Quantum Bose-Chaudhuri-Hocquenghem (QBCH) codes is also considered.