arXiv subjects

Burhan Gulbahar

Publications and source records attributed to Burhan Gulbahar.

10 recordsLinked to original sources

arXiv2026

Binary Signal Recovery in Undersampling: Iterative SDP with Majority Voting and Successive Interference Cancellation

Binary compressive sensing (BCS) seeks to recover a $k$-sparse binary vector of length $n$ from $m$ linear measurements. Classical CS guarantees break down for $m < k$ and convex/greedy BCS algorithms with random Gaussian sensing matrices perform poorly. We introduce ISDP-MVSIC, which combines randomized semidefinite programming (SDP) sampling, majority voting (MV) and successive interference cancellation (SIC) across $L \ll n$ stages, wrapped in a residual-cost driven retry loop. The method exposes a tunable complexity--performance trade-off: for $n=100, 144$, raising the worst-case complexity $\mathcal{C}_{max}$ from $7.9 \times 10^9$ to $2.0 \times 10^{10}$ enables empirical exact recovery over $m/k \in [0.4,5.0]$ as the sparsity ratio $s=k/n$ decreases from $0.5$ to $0.1$, by practically targeting the undersampled regime.

cs.IT↗

arXiv2026

Offline Channel-Independent QAOA Angles for RIS Power Aggregation: Unit-Circle Phase Dictionaries and Infinite-Size Spin-Glass Limits

Reconfigurable intelligent surfaces (RIS) maximize received power by setting per-element phases. Discrete-phase optimization is NP-hard in the worst case, while the quantum approximate optimization algorithm (QAOA) applied to RIS faces limited phase alphabets, either per-problem angle optimization or uncharacterized training cost exposed to barren plateaus, and no scalable performance benchmark. We introduce a $2^{M}$-phase $θ$ dictionary for optimizing power $\|\mathbf{A} \, e^{jθ}\|^{2}$ having $K \times N$ channel matrix $\mathbf{A}$ and QAOA angle offline optimization with instance and size-independent infinite-size limit of the mixed-$q$ Gaussian ensemble of Basso et al. Our design bounds the spin-Hamiltonian interaction order to at most quartic for any $M$, and the deployed order-2 reduction lies below the even-$q\!\ge\!4$ regime in which constant-level QAOA limitations are proved. We perform analytical, state-vector, matrix-product-state and Pauli-path-simulation numerical studies for $N=K \leq 100$ and QAOA depth $p=9$, verifying offline angle transfer to Rayleigh, Rician/line-of-sight, cascaded double-fading and spatially-correlated RIS channels at $N\!\in\!\{5,12\}$. We observe performance reaching a near-optimal multi-start single-flip local-search reference for $N\!\le\!16$ under order-2 modeling with $2^{5}{=}32$-phase dictionary while the order-4 model shows a performance ceiling below the classical reference. The approach suggests a route to near-optimal large-$N$ performance on future fault-tolerant (FTQ) quantum computers, which enable the higher-depth QAOA circuits.

quant-ph↗

arXiv2021

K-sparse Pure State Tomography with Phase Estimation

Quantum state tomography (QST) for reconstructing pure states requires exponentially increasing resources and measurements with the number of qubits by using state-of-the-art quantum compressive sensing (CS) methods. In this article, QST reconstruction for any pure state composed of the superposition of $K$ different computational basis states of $n$ qubits in a specific measurement set-up, i.e., denoted as $K$-sparse, is achieved without any initial knowledge and with quantum polynomial-time complexity of resources based on the assumption of the existence of polynomial size quantum circuits for implementing exponentially large powers of a specially designed unitary operator. The algorithm includes $\mathcal{O}(2 \, / \, \vert c_{k}\vert^2)$ repetitions of conventional phase estimation algorithm depending on the probability $\vert c_{k}\vert^2$ of the least possible basis state in the superposition and $\mathcal{O}(d \, K \,(log K)^c)$ measurement settings with conventional quantum CS algorithms independent from the number of qubits while dependent on $K$ for constant $c$ and $d$. Quantum phase estimation algorithm is exploited based on the favorable eigenstructure of the designed operator to represent any pure state as a superposition of eigenvectors. Linear optical set-up is presented for realizing the special unitary operator which includes beam splitters and phase shifters where propagation paths of single photon are tracked with which-path-detectors. Quantum circuit implementation is provided by using only CNOT, phase shifter and $- π\, / \, 2$ rotation gates around X-axis in Bloch sphere, i.e., $R_{X}(- π\, / \, 2)$, allowing to be realized in NISQ devices. Open problems are discussed regarding the existence of the unitary operator and its practical circuit implementation.

quant-ph↗

arXiv2020

Theory of Quantum Path Computing with Fourier Optics and Future Applications for Quantum Supremacy, Neural Networks and Nonlinear Schrödinger Equations

The scalability, error correction and practical problem solving are important challenges for quantum computing (QC) as more emphasized by quantum supremacy (QS) experiments. Quantum path computing (QPC), recently introduced for linear optic based QCs (LOQCs) as an unconventional design, targets to obtain scalability and practical problem solving. It samples the intensity from the interference of exponentially increasing number of propagation paths obtained in multi-plane diffraction (MPD) of classical particle sources. QPC exploits MPD based quantum temporal correlations of the paths and freely entangled projections a<t different time instants, for the first time, with the classical light source and intensity measurement while not requiring photon interactions or single photon sources and receivers. In this article, photonic QPC is defined, theoretically modeled and numerically analyzed for arbitrary Fourier optical or quadratic phase set-ups while utilizing both Gaussian and Hermite-Gaussian source laser modes. Problem solving capabilities already including partial sum of Riemann theta functions are extended. Important future applications, implementation challenges and open issues such as universal computation and quantum circuit implementations determining the scope of QC capabilities are discussed. The applications include QS experiments reaching more than $2^{100}$ Feynman paths, quantum neuron implementations and solutions of nonlinear Schrödinger equation.

quant-ph↗

arXiv2020

Theory of Quantum Path Entanglement and Interference with Multiplane Diffraction of Classical Light Sources

Quantum history states were recently formulated by extending the consistent histories approach of Griffiths to the entangled superposition of evolution paths and were then experimented with Greenberger--Horne--Zeilinger states. Tensor product structure of history-dependent correlations was also recently exploited as a quantum computing resource in simple linear optical setups performing multiplane diffraction (MPD) of fermionic and bosonic particles with remarkable promises. This significantly motivates the definition of quantum histories of MPD as entanglement resources with the inherent capability of generating an exponentially increasing number of Feynman paths through diffraction planes in a scalable manner and experimental low complexity combining the utilization of coherent light sources and photon-counting detection. In this article, quantum temporal correlation and interference among MPD paths are denoted with quantum path entanglement (QPE) and interference (QPI), respectively, as novel quantum resources. Operator theory modeling of QPE and counterintuitive properties of QPI are presented by combining history-based formulations with Feynman's path integral approach. Leggett--Garg inequality as temporal analog of Bell's inequality is violated for MPD with all signaling constraints in the ambiguous form recently formulated by Emary. The proposed theory for MPD-based histories is highly promising for exploiting QPE and QPI as important resources for quantum computation and communications in future architectures.

quant-ph↗

arXiv2019

Quantum Path Computing: Computing Architecture with Propagation Paths in Multiple Plane Diffraction of Classical Sources of Fermion and Boson Particles

Quantum computing (QC) architectures utilizing classical or coherent resources with Gaussian transformations are classically simulable as an indicator of the lack of QC power. Simple optical set-ups utilizing wave-particle duality and interferometers achieve QC speed-up with the cost of exponential complexity of resources in time, space or energy. However, linear optical networks composed of single photon inputs and photon number measurements such as boson sampling achieve solving problems which are not efficiently solvable by classical computers while emphasizing the power of linear optics. In this article, quantum path computing (QPC) set-up is introduced as the simplest optical QC satisfying five fundamental properties all-in-one: exploiting only the coherent sources being either fermion or boson, i.e., Gaussian wave packet of standard laser, simple set-up of multiple plane diffraction (MPD) with multiple slits by creating distinct propagation paths, standard intensity measurement on the detector, energy efficient design and practical problem solving capability. MPD is unique with non-Gaussian transformations by realizing an exponentially increasing number of highly interfering propagation paths while making classical simulation significantly hard. It does not require single photon resources or number resolving detection mechanisms making the experimental implementation of QC significantly low complexity. QPC set-up is utilized for the solutions of specific instances of two practical and hard number theoretical problems: partial sum of Riemann theta function and period finding to solve Diophantine approximation. Quantumness of MPD with negative volume of Wigner function is numerically analyzed and open issues for the best utilization of QPC are discussed.

quant-ph↗

arXiv2018

Energy Harvesting and Magneto-Inductive Communications with Molecular Magnets on Vibrating Graphene and Biomedical Applications in the Kilohertz to Terahertz Band

Magneto-inductive (MI) Terahertz (THz) wireless channels provide significant theoretical performances for MI communications (MIC) and wireless power transmission (WPT) in nanoscale networks. Energy harvesting (EH) and signal generation are critical for autonomous operation in challenging medium including biomedical channels. State of the art electromagnetic (EM) vibrational devices have millimeter dimensions while targeting low frequency EH without any real-time communications. In this article, graphene resonators are combined with single molecule magnets (SMMs) to realize nanoscale EH, MIC and WPT with novel modulation methods achieving simultaneous wireless information and PT (SWIPT). Unique advantages of graphene including atomic thickness, ultra-low weight, high strain and resonance frequencies in the Kilohertz to Terahertz band are combined with high and stable magnetic moments of Terbium(III) bis(phthalocyanine) SMMs. Numerical analyses provide tens of nanowatts powers and efficiencies of $10^4 \, W/m^3$ in acoustic and ultrasound frequencies comparable with vibrational EH devices while millimeter wave carrier generation is numerically analyzed. Proposed model and communication theoretical analysis present a practical framework for challenging applications in the near future by promising simple mechanical design. Applications include nanoscale biomedical tagging including human cells, sensing and communication for diagnosis and treatment, EH and modulation for autonomous nano-robotics, and magnetic particle imaging (MPI).

physics.app-ph↗

arXiv2017

CSSTag: Optical Nanoscale Radar and Particle Tracking for In-Body and Microfluidic Systems with Vibrating Graphene and Resonance Energy Transfer

Single particle tracking systems monitor cellular processes with great accuracy in nano-biological systems. The emissions of the fluorescent molecules are detected with cameras or photodetectors. However, state-of-the-art imaging systems have challenges in the detection capability, collection and analysis of imaging data, penetration depth and complicated set-ups. In this article, a \textit{signaling based nanoscale acousto-optic radar and microfluidic particle tracking system} is proposed based on the theoretical design providing nanoscale optical modulator with vibrating F{ö}rster resonance energy transfer (VFRET) and vibrating CdSe/ZnS quantum dots (QDs) on graphene resonators. The modulator structure combines the significant advantages of graphene membranes having wideband resonance frequencies with QDs having broad absorption spectrum and tunable properties. The solution denoted by chirp spread spectrum (CSS) Tag (\textit{CSSTag}) utilizes classical radar target tracking approaches in nanoscale environments based on the capability to generate CSS sequences to identify different bio-particles. Numerical and Monte-Carlo simulations are realized showing the significant performance for multiple particle tracking (MPT) with a modulator of $10 \, μ$m $\times$ $10 \, μ$m $\times$ $10 \, μ$m dimension and several picograms of weight, signal to noise ratio (SNR) in the range $-7$ dB to $10$ dB and high speed tracking capability for microfluidic and in-body environments.

q-bio.QM↗

arXiv2017

Network Topology Modulation for Energy and Data Transmission in Internet of Magneto-Inductive Things

Internet-of-things (IoT) architectures connecting a massive number of heterogeneous devices need energy efficient, low hardware complexity, low cost, simple and secure mechanisms to realize communication among devices. One of the emerging schemes is to realize simultaneous wireless information and power transfer (SWIPT) in an energy harvesting network. Radio frequency (RF) solutions require special hardware and modulation methods for RF to direct current (DC) conversion and optimized operation to achieve SWIPT which are currently in an immature phase. On the other hand, magneto-inductive (MI) communication transceivers are intrinsically energy harvesting with potential for SWIPT in an efficient manner. In this article, novel modulation and demodulation mechanisms are presented in a combined framework with multiple-access channel (MAC) communication and wireless power transmission. The network topology of power transmitting active coils in a transceiver composed of a grid of coils is changed as a novel method to transmit information. Practical demodulation schemes are formulated and numerically simulated for two-user MAC topology of small size coils. The transceivers are suitable to attach to everyday objects to realize reliable local area network (LAN) communication performances with tens of meters communication ranges. The designed scheme is promising for future IoT applications requiring SWIPT with energy efficient, low cost, low power and low hardware complexity solutions.

cs.IT↗

arXiv2015

Optimal Offline Packet Scheduling in Energy Harvesting 2-user Multiple Access Channel with Common Data

The lifetime and the sustainability of the wireless sensor networks (WSNs) can be increased with energy harvesting transmitters utilizing optimum packet scheduling. On the other hand, WSNs are observed to collect spatially or temporally correlated data which should be taken into account for the optimum packet scheduling in an energy harvesting system. However, the solutions available for 2-user multiple-access channel (MAC) systems with energy harvesting transmitters do not consider the common data or the correlation among the data. In this paper, optimal packet scheduling for energy harvesting 2-user Gaussian MAC with common data is achieved by assuming deterministic knowledge of the data and energy packets, i.e., offline solution. The optimum departure region is found by using Karush- Kuhn-Tucker (KKT) conditions generalizing the solutions obtained for the MAC without common data. An efficient iterative backward water-filling algorithm is defined. The optimum solution is numerically compared with the case of no scheduling, uniform power scheduling and the previous solutions defined for the MAC without common data by showing the improvement obtained with the optimization.

cs.IT↗