Quantum information processing enables secure communication, quantum teleportation, and computation. However, current protocols are limited by the narrow electronic bandwidth of standard measurement devices (megahertz to gigahertz), vastly underusing the broad optical bandwidth (10 to 100 terahertz) of readily available quantum light sources. We introduce a general framework for frequency-multiplexing of quantum channels along with methods for efficient processing of quantum information in those channels across the full optical bandwidth. Using a broadband squeezed-light source, spectral manipulation, and parametric homodyne detection, we generate, process, and measure multiple quantum channels in parallel. We demonstrate this through multiplexed protocols of both continuous-variable quantum key distribution (CV-QKD) and quantum teleportation. We experimentally demonstrate a proof-of-principle realization of multiplexed CV-QKD over 23 independent spectral channels with eavesdropping detection in each channel. These techniques pave the way for massively parallel quantum processing, potentially boosting the throughput of quantum protocols by orders of magnitude.

Abstract Optically active spin qubits are promising platforms for applications such as quantum sensing, quantum computing, and quantum communication. However, in many cases, materials challenges remain major hurdles for realizing these quantum technologies. These challenges include the effects of the host material, surfaces, and device integration, which can both undermine intrinsic ideal quantum properties, but also provide necessary engineering controls. In this issue, materials opportunities and challenges that impact the properties of spin qubits interfaced with light are discussed. The material platforms considered are diamond, silicon, silicon carbide, 2D materials and molecular systems. Both fundamental, engineering, and computational design aspects are considered. Such multifaceted, exploratory, and materials-centric advances are required to unlock the potential of these quantum systems. Graphical abstract

ABSTRACT In‐memory computing (IMC) is an emerging non‐von Neumann paradigm that leverages the intrinsic physics of memory devices to perform computations directly within the memory array. Among the various candidates, phase‐change memory (PCM) has emerged as a leading non‐volatile technology, showing significant promise for IMC, particularly in deep learning acceleration. PCM‐based IMC is also poised to play a pivotal role in cryogenic applications, including quantum computing and deep‐space electronics. In this work, we present a comprehensive characterization of PCM devices across temperatures down to 5 K, covering the conditions most relevant to these domains. We systematically investigate key physical mechanisms such as phase transitions and threshold switching that govern device programming at low temperatures. In addition, we study attributes including electrical transport, structural relaxation, and read noise, which critically affect read‐out behavior and, in turn, the precision achievable in computational tasks.

Recent advancements in quantum algorithms have reached a state where we can consider how to capitalize on quantum and classical computational resources to accelerate molecular resonance state identification. Here, we identify molecular resonances with a method that combines quantum computing with classical high-throughput computing (HTC). This algorithm, which we term qDRIVE (the quantum deflation resonance identification variational eigensolver), exploits the complex absorbing potential formalism to distill the problem of molecular resonance identification into a network of hybrid quantum-classical variational quantum eigensolver tasks and harnesses HTC resources to execute these interconnected but independent tasks both asynchronously and in parallel, a strategy that minimizes wall time to completion. We show qDRIVE successfully identifies resonance energies and wave functions in simulated quantum processors with current and planned specifications, which bodes well for qDRIVE's ultimate application in disciplines ranging from photocatalysis to quantum control and places a spotlight on the potential offered by integrated heterogeneous quantum computing/HTC approaches in computational chemistry.

We use Pauli Path simulation to variationally obtain parametrized circuits for preparing ground states of various quantum many-body Hamiltonians. These include the quantum Ising model in one dimension, in two dimensions on square and heavy-hex lattices, and the Kitaev honeycomb model, all at system sizes of one hundred qubits or more – sizes at which generic quantum circuits are beyond the reach of exact state-vector simulation – thereby reaching utility scale. We benchmark the Pauli Path simulation results against exact ground-state energies when available, and against density-matrix renormalization group calculations otherwise, finding strong agreement. To further assess the quality of the variational states, we evaluate the magnetization in the x and z directions for the quantum Ising models and compute the topological entanglement entropy for the Kitaev honeycomb model. Finally, we prepare approximate ground states of the Kitaev honeycomb model with 48 qubits, in both the gapped and gapless regimes, on Quantinuum's System Model H2 quantum computer using parametrized circuits obtained from Pauli Path simulation. We achieve a relative energy error of approximately 5 % without error mitigation and demonstrate the braiding of Abelian anyons on the quantum device beyond fixed-point models.

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.

Autonomous materials science, where active learning is used to navigate large compositional phase space, has emerged as a powerful vehicle to rapidly explore new materials. A crucial aspect of autonomous materials science is exploring new materials using as little data as possible. Gaussian process-based active learning allows effective charting of multi-dimensional parameter space with a limited number of training data and, thus, is a common algorithmic choice for autonomous materials science. An integral part of the autonomous workflow is the application of kernel functions for quantifying similarities among measured data points. A recent theoretical breakthrough has shown that quantum kernel models can achieve similar performance with less training data than classical kernel models. This signals the possible advantage of applying quantum kernel machine learning to autonomous materials discovery. In this work, we compare quantum and classical kernels for their utility in sequential phase space navigation for autonomous materials science. In particular, we compute a quantum kernel and several classical kernels for x-ray diffraction patterns taken from an Fe–Ga–Pd ternary composition spread library. We conduct our study on both IonQ’s Aria trapped ion quantum computer hardware and the corresponding classical noisy simulator. We experimentally verify that a quantum kernel model can outperform some classical kernel models. The results highlight the potential of quantum kernel machine learning methods for accelerating materials discovery and suggest that complex x-ray diffraction data are a candidate for robust quantum kernel model advantage.

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