ABSTRACT In the fields of quantum computing, nanotechnology and optical computing, reversible computing has become a highly attractive area of study. This paper presents the design and implementation of a field-programmable gate array (FPGA)-based digital down converter (DDC) using reversible adders for software-defined radio (SDR) applications. The DDC comprises a polyphase mixer followed by a cascaded integrator comb (CIC) filter and finally a lowpass finite impulse response (FIR) filter. The canonical implementation of the polyphase mixer reduces the multipliers. The polynomial-based CIC filter improves the passband characteristics. Again, the polyphase transposed FIR filter coefficients are represented in the canonical signed-digit (CSD) encoding technique. This process reduces the adders significantly. Each component of the DDC is implemented with reversible hybrid parallel prefix adders (RHPPAs). The presented RHPPA consists of reversible ripple carry adders (RRCAs), which consume less quantum cost compared to popular parallel adders. The proposed DDC is described with efficient coding in hardware description language (HDL) using the Xilinx Vivado 2024.1 and implemented on the Kintex-7 board. Synthesis results showed that each component of the DDC runs faster compared to its conventional structure at a marginal cost of area. Comparative analysis indicated that the proposed DDC using RHPPA has achieved significant improvement of area-delay product (ADP) by 15.59% and power-delay product (PDP) by 26.16% to the corresponding most recent architecture. Also, the design has achieved a high value of spurious-free dynamic range (SFDR), which is 116 dB.

Abstract The extent to which quantum computers can simulate physical phenomena and solve the partial differential equations (PDEs) that govern them remains a central open question. In this work, one of the most fundamental PDEs is addressed: the multidimensional transport equation with space- and time-dependent coefficients. We present a quantum numerical scheme based on three steps: quantum state preparation, evolution, and measurement of relevant observables. The evolution step combines a high-order centered finite difference with a time-splitting scheme based on product formula approximations, also known as Trotterization. We introduce a novel vector-norm analysis and prove that the number of time-steps can be reduced by a factor exponential in the number of qubits, compared with previously established operator-norm analysis. This new scaling significantly reduces the projected computational resources, independently of the circuit implementation of the trotterized evolution operator. We also present efficient quantum circuits based on (sparse) Walsh approximations along with numerical simulations that confirm the predicted vector-norm scaling. We report results on real quantum hardware for the one-dimensional convection equation, and solve a non-linear ordinary differential equation via its associated Liouville equation, a particular case of transport equations. This work provides a practical framework for efficiently simulating transport phenomena on quantum computers, with potential applications in plasma physics, molecular gas dynamics and non-linear dynamical systems, including chaotic systems.

We provide practical simulation methods for scalar field theories on a quantum computer that yield improved asymptotics as well as concrete gate estimates for the simulation and physical qubit estimates using the surface code. We achieve these improvements through two optimizations. First, we consider a finite volume approach for estimating the elements of the S-matrix. This approach is appropriate in general for 1+1D and for certain low-energy elastic collisions in higher dimensions. Second, we implement our approach using a series of different fault-tolerant simulation algorithms for Hamiltonians formulated both in the field occupation basis and field amplitude basis. Our algorithms are based on either second-order Trotterization or qubitization. The cost of Trotterization in occupation basis scales as O ( λ N 7 | Ω | 3 / ( M 5 / 2 ϵ 3 / 2 ) ) where λ is the coupling strength, N is the occupation cutoff, | Ω | is the volume of the spatial lattice, M is the mass of the particles and ϵ is the uncertainty in the energy calculation used for the S -matrix determination. Qubitization in the field basis scales as O ( | Ω | 2 ( k 2 Λ + k M 2 ) / ϵ ) , where k is the cutoff in the field and Λ is a scaled coupling constant. We find in both cases that the bounds suggest physically meaningful simulations can be performed using on the order of 4 × 10 6 physical qubits and 10 12 T -gates which corresponds to roughly one day on a superconducting quantum computer with surface code and a cycle time of 100 ns. This places the simulation of scalar field theory within striking distance of the gate counts for the best available chemistry simulation results.

Abstract The NP-hardness of the closest vector problem (CVP) is an important basis for quantum-secure cryptography, in much the same way that integer factorisation's conjectured hardness is at the foundation of cryptosystems like RSA. Recent work with heuristic quantum algorithms indicates the possibility to find close approximations to (constrained) CVP instances that could be incorporated within fast sieving approaches for factorisation. This work explores both the practicality and scalability of the proposed heuristic approach to explore the potential for a quantum advantage for approximate CVP, without regard for the subsequent factoring claims. We also extend the proposal to include an antecedent "pre-training" scheme to find and fix a set of parameters that generalise well to increasingly large lattices, which both optimises the scalability of the algorithm, and permits direct numerical analyses. Our results further indicate a noteworthy quantum speed-up for lattice problems obeying a certain 'prime' structure, approaching fifth order advantage for QAOA of fixed depth p=10 compared to classical brute-force, motivating renewed discussions about the necessary lattice dimensions for quantum-secure cryptosystems in the near-term.

This present theoretical study investigates the geometric control of quantum conformal field theories through radius compression-induced quantum critical phenomena. It reveals an intrinsic relationship between the Berry connection and variations in both the radial component, δR, and spin components, Sa, within the quantum conformal subspace. Radius compression in such systems can generate a Berry curvature analogous to magnetic fields, which releases topological waves via quantum tunneling mechanisms. External electric fields offer effective control over spin S, while laser techniques enable precise modulation of the radial variation δR. Within the context of conformal space compression, tunneling effects may couple with Berry magnetic fields that possess topological protection. These findings demonstrate that geometric quantum bits (such as arbitrary control of anyons in topological quantum computing) and directional transport designs for novel transistors can be realized through radius-compression-induced Berry fields. For a quantum anomalous Hall insulator to maintain its Chern number (C = 1) and chiral edge state conduction within the topologically protected regime, the compression of its radius may lead to the observation of Berry-curvature-induced magnetic fields by strong external electric field.

Abstract Quantum error correction (QEC) is the core mechanism enabling scalable and fault-tolerant quantum computation. Among various QEC schemes, Calderbank–Shor–Steane (CSS) codes play a central role. Efficiently verifying whether a quantum state remains within the target CSS code subspace is therefore essential both for understanding the theoretical performance of these codes and for assessing the error-correcting capability of practical quantum platforms. In this work, we develop an efficient framework for verifying whether a quantum system remains within the encoded subspace of a CSS code. We first show that the stabilizer structure of any CSS code can be mapped to a two-colorable graph, enabling the design of a hybrid verification strategy based solely on local Pauli measurements. We further analytically optimize the measurement probabilities and derive the resulting sample complexities for representative CSS codes. Specifically, for the Toric code, the efficiency of our hybrid strategy increases with lattice size and asymptotically approaches near-optimal performance. These results establish a unified and scalable approach to CSS code subspace verification, providing a foundation for certifiable and fault-tolerant quantum information processing.

Abstract. The legalization of medical cannabis in Morocco’s northern Rif region requires precision agriculture systems capable of supporting highly controlled, traceable and quality-driven cultivation. Medical cannabis is biologically sensitive to micro-variations in soil moisture, vapor pressure deficit (VPD), canopy temperature and nutrient levels, which makes it a demanding testbed for advanced decision-support methods. In this work, we propose and numerically evaluate an end-to-end hybrid quantum–classical framework that combines IoT sensor networks, Sentinel-2 and UAV imagery, GIS integration and quantum-enhanced analytics for regulated medical cannabis cultivation in the Al-Hoceïma region. The framework instantiates three quantum modules: (i) a variational quantum linear solver (VQLS) for Kriging-based spatial interpolation under sparse sensing, (ii) a variational quantum classifier (VQC) for early stress detection from multi-source features, and (iii) a Quantum Approximate Optimization Algorithm (QAOA) for constrained irrigation scheduling. All experiments are conducted on synthetic yet agro-ecologically calibrated data generated for a 4-hectare virtual plot; no real cannabis-field data or quantum hardware are used. In this controlled simulation setting, the quantum-inspired modules achieve moderate improvements over classical baselines (Kriging, Random Forest, neural networks, MILP), for example reducing interpolation RMSE by about 20% and improving early-stress F1-score by several percentage points. We explicitly do not claim hardware-level quantum advantage, nor do we provide a formal proof that VQLS or VQC must outper- form classical Kriging or machine learning in this regime. Instead, the contribution is a transparent formulation and simulation- based assessment of quantum-compatible workflows for precision agriculture in regulated contexts, together with a critical discus- sion of their current limitations and the conditions under which they might become competitive in practice.

Modern industrial environments are evolving into data-intensive cyber-physical systems that require robust computational frameworks for performance prediction and optimization. While existing literature has addressed developments in statistical methods, artificial intelligence, and quantum computing individually, there remains a lack of systematic reviews examining the integrated evolution and data processing capabilities of these three paradigms. This review addresses the need to clarify the capabilities, limitations, and application domains of each approach to enable engineers to select appropriate data-driven methodologies for specific optimization challenges. In this review, we traced the historical development of optimization methodologies from design of experiments and response surface methodology through neural networks and generative models to variational quantum algorithms, presented chronological development tables documenting key milestones in each paradigm, and analyzed industrial implementation cases including conversion rate increases and emission reductions. The analysis reveals that statistical methods exhibit unique strengths in systematic data analysis, AI in complex pattern recognition, and quantum computing in high-complexity simulation, with their hybrid integration providing optimal performance. This study provides significance in offering a comprehensive framework necessary for connected industries to strategically deploy multi-paradigm optimization strategies within integrated network environments to achieve sustainability goals while maintaining global competitiveness.

The emergence of quantum computers heralds a new frontier in computational power, empowering quantum algorithms to address challenges that defy classical computation. However, the design of quantum algorithms is challenging as it largely requires the manual efforts of quantum experts to transit mathematical expressions to quantum circuit diagrams. To ease this process, particularly for prototyping, educational, and modular design workflows, we propose to bridge the textual and visual contexts between mathematics and quantum circuits through visual linking and transitions. We contribute a design space for quantum algorithm design, focusing on the textual and visual elements, interactions, and design patterns throughout the quantum algorithm design process. Informed by the design space, we introduce QuRAFT, a visual interface that facilitates a seamless transition from abstract mathematical expressions to concrete quantum circuits. QuRAFT incorporates a suite of eight integrated visual and interaction designs tailored to support users in the formulation, implementation, and validation process of the quantum algorithm design. Through two detailed case studies and a user evaluation, this paper demonstrates the effectiveness of QuRAFT. Feedback from quantum computing experts highlights the practical utility of QuRAFT in algorithm design and provides valuable implications for future advancements in visualization and interaction design within the quantum computing domain.

Adaptive congestion-aware high performance scalable 2-D and 3-D topologies for network-on-chip based interconnect for quantum computing

Fundus imaging based quantum computing: Grading of severity of vision threatening diabetic retinopathy

Bibliometric analysis of secure IoT for quantum computing

Internal quantum constraints of natural computation in autopoietic systems.

Curvature-induced operational regime transitions in computer quantum stirling cycles: A Shannon vs. Tsallis entropy perspective

Superconducting quantum computers have emerged as a leading platform for next-generation computing, offering exceptional scalability and unprecedented computational speeds. However, scaling these systems to millions of qubits for practical applications poses substantial challenges, particularly due to interconnect bottlenecks. To address this challenge, extensive research has focused on developing cryogenic multiplexers that enable minimal wiring between room-temperature electronics and quantum processors. This paper investigates the viability of commercial microelectromechanical system (MEMS) switches for cryogenic multiplexers in large-scale quantum computing systems. DC and RF characteristics of the MEMS switches are evaluated at cryogenic temperatures (<10 K) through finite element simulations and experimental measurements. Our results demonstrate that MEMS switches exhibit improved on-resistance, lower operating voltage, and superior RF performance at cryogenic temperatures. In particular, an engineered gate-pulse waveform is introduced to suppress beam bouncing caused by the quasi-vacuum conditions inside the package, enabling stable dynamic operation exceeding 100 million cycles even at cryogenic temperatures. Furthermore, stable single-pole four-throw (SP4T) switching and logical operations, including NAND and NOR gates, are demonstrated at cryogenic temperatures, validating their potential for quantum computing. These results underscore the promise of MEMS switches in realizing large-scale quantum computing systems.

We analyze the power of the recently proposed Lorentz quantum computer (LQC), a theoretical model leveraging hyperbolic bits (hybits) governed by complex Lorentz transformations. We define the complexity class BLQP (bounded-error Lorentz quantum polynomial-time) and demonstrate its equivalence to the complexity class P♯P (the class of problems solvable by a deterministic polynomial-time Turing machine with access to a ♯P oracle). LQC algorithms are shown to solve NP-hard problems, such as the maximum independent set (MIS), in polynomial time, thereby placing NP and co-NP within BLQP. Furthermore, we establish that LQC can efficiently simulate quantum computing with postselection (PostBQP), while the reverse is not possible, highlighting LQC’s unique “super-postselection” capability. By proving BLQP =P♯P, we situate the entire polynomial hierarchy (PH) within BLQP and reveal profound connections between computational complexity and physical frameworks like Lorentz quantum mechanics. These results underscore LQC’s theoretical superiority over conventional quantum computing models and its potential to redefine boundaries in complexity theory.

This study examines how global uncertainty influences the financial dynamics of technology firms involved in quantum computing, a strategically significant but structurally fragile segment of emerging deep-tech markets. Using daily data from January 2015 to May 2025, the analysis integrates principal component decomposition, panel regression, Granger causality testing and volatility diagnostics to assess the transmission of market volatility and geopolitical risk. The findings show that market volatility, proxied by the VIX index, exerts a persistent and adverse influence on stock returns, confirming its role as a systemic risk factor. Geopolitical risk, measured through the ACT and THREAT sub-indices of the Geopolitical Risk Index (GPR), also affects return behaviour, but through asymmetric and time-varying transmission mechanisms that emerge under heightened uncertainty and global strategic tension. The results further reveal heterogeneous vulnerability profiles across firms, indicating conditional risk spillovers rather than uniform market reactions. The study contributes new empirical evidence on the interplay between financial and geopolitical risk in advanced technology sectors and offers a replicable framework for uncertainty modelling in frontier markets.

Abstract Simulating quantum dynamics to extract time-evolving observables constitutes a central challenge in quantum computing, with both fundamental significance and broad practical applications. Classical approaches suffer from the exponential scaling of Hilbert space, while existing quantum algorithms face limitations from deep circuits and sequential error accumulation on near-term devices. This work introduces a physics-informed quantum subspace (PIQS) method for the efficient estimation of dynamical properties of quantum systems. The core innovation is a globally physics-informed loss function that incorporates the time-dependent Schrödinger equation as a physics-based penalty. This enforces quantum evolution constraints directly during optimization, thereby circumventing the error accumulation inherent in stepwise simulations.&amp;#xD;By strategically relaxing the normalization constraint, we obtain convexified loss functions whose optimization reduces to solving a linear system, guaranteeing global convergence and significantly mitigating the convergence issues and barren plateaus common in variational quantum algorithms. Theoretically, we prove that under suitable conditions the true dynamical solution can be approximated with high accuracy within a subspace whose dimension scales only as $\mathcal{O}(T\log(1/\varepsilon))$, thus breaking the curse of dimensionality in classical simulation. Numerical experiments demonstrate that the proposed method outperforms conventional Trotterization and variational quantum benchmarks in terms of computational cost, convergence speed, and robustness against measurement noise, offering a viable and efficient pathway for practical dynamical simulation on noisy intermediate-scale quantum hardware.

Quantum error-correcting code in continuous-variable (CV) systems attracts much attention due to its flexibility and high resistance against specific noise. However, the theory of fault tolerance in CV systems is premature and lacks a general strategy to translate noise in CV systems into noise in logical qubits, leading to severe restrictions on correctable noise models. In this paper, we show that Markovian-type noise in CV systems is translated into Markovian-type noise in the logical qubits through the Gottesman-Kitaev-Preskill code. We analyze an upper bound on the resulting noise strength in terms of our newly introduced noise parameterization. Combined with the established threshold theorem of concatenated codes against Markovian-type noise, we show that CV quantum computation has a fault-tolerant threshold against general Markovian-type noise, closing the existing crucial gap in CV quantum computation. We also give a new insight into the fact that careful management of the energy of a state is required to achieve fault tolerance in CV systems.

The surface code family is a promising approach to implementing fault-tolerant quantum computations. Universal fault-tolerance requires error-corrected non-Clifford operations, in addition to Clifford gates, and for the former, it is imperative to experimentally demonstrate additional resources known as magic states. Another challenge is to efficiently embed surface codes into quantum hardware with connectivity constraints. This work simultaneously addresses both challenges by employing a qubit-efficient rotated heavy-hexagonal surface code for IBM quantum processors (ibm_fez) and implementing the magic state injection protocol. Our work reports error thresholds for both logical bit- and phase-flip errors, of [Formula: see text] and [Formula: see text], respectively, which are higher than the threshold values previously reported with traditional embedding. The post-selection-based preparation of logical magic states [Formula: see text] and [Formula: see text] achieve fidelities of [Formula: see text] and [Formula: see text], respectively, which are both above the magic state distillation threshold. The post-selection process yields an average success rate of [Formula: see text]. Additionally, we report the minimum fidelity among injected arbitrary single logical qubit states as [Formula: see text]. Our work demonstrates the potential for realising non-Clifford logical gates by producing high-fidelity logical magic states on IBM quantum devices.