Neutrosophic information processing extends conventional information processing by explicitly representing three logically independent components: truth, indeterminacy, and falsity. While neutrosophic models have been extensively developed at the mathematical level, their physical implementation remains comparatively underdeveloped. This paper investigates possible physical architectures for neutrosophic information processing, with particular emphasis on quantum systems. We distinguish three levels of implementation. At the first level, ordinary physical devices are used to represent neutrosophic information as a classical data structure. At the second level, physical systems such as photonic, spin, superconducting, atomic, or solid-state platforms encode the truth, indeterminacy, and falsity components in different physical degrees of freedom. At the third level, a genuinely quantum-neutrosophic processor is considered, in which quantum superposition, measurement uncertainty, noise, and explicitly represented indeterminacy are integrated into a single computational model. A mathematical framework based on neutrosophic triples (T, I, F) is introduced together with neutrosophic bits, density operators, measurement maps, and elementary transformation operators. We then discuss possible implementations using multilevel quantum systems, photonic polarization, superconducting circuits, trapped ions, and hybrid quantum-classical architectures. The paper emphasizes the distinction between physical uncertainty already present in quantum mechanics and an explicit logical indeterminacy variable. This distinction is essential for designing experimental systems capable of processing neutrosophic information. The paper concludes with a research program for experimental neutrosophic processors and identifies several open problems concerning noise, calibration, readout, error correction, and complexity.
Neutrosophic information processing extends conventional information processing by explicitly representing three logically independent components: truth, indeterminacy, and falsity. While neutrosophic models have been extensively developed at the mathematical level, their physical implementation remains comparatively underdeveloped. This paper investigates possible physical architectures for neutrosophic information processing, with particular emphasis on quantum systems. We distinguish three levels of implementation. At the first level, ordinary physical devices are used to represent neutrosophic information as a classical data structure. At the second level, physical systems such as photonic, spin, superconducting, atomic, or solid-state platforms encode the truth, indeterminacy, and falsity components in different physical degrees of freedom. At the third level, a genuinely quantum-neutrosophic processor is considered, in which quantum superposition, measurement uncertainty, noise, and explicitly represented indeterminacy are integrated into a single computational model. A mathematical framework based on neutrosophic triples (T, I, F) is introduced together with neutrosophic bits, density operators, measurement maps, and elementary transformation operators. We then discuss possible implementations using multilevel quantum systems, photonic polarization, superconducting circuits, trapped ions, and hybrid quantum-classical architectures. The paper emphasizes the distinction between physical uncertainty already present in quantum mechanics and an explicit logical indeterminacy variable. This distinction is essential for designing experimental systems capable of processing neutrosophic information. The paper concludes with a research program for experimental neutrosophic processors and identifies several open problems concerning noise, calibration, readout, error correction, and complexity.
At the most microscopic level, all physical systems are governed by the laws of quantum mechanics. Quantum information processing is the study of how information is gathered, transformed, and transmitted at the quantum level—in atoms, ions, photons, elementary particles, and microscopic solid-state systems, which obey fundamentally quantum mechanical laws. Quantum computers, quantum communication channels, and quantum sensors are devices that can attain the ultimate limits of information processing. The laws of quantum mechanics give rise to counterintuitive effects. Quantum information processors use “quantum weirdness” to perform tasks that classical information processors cannot. Quantum computers are conceived to process information stored on atomic, optical, and solid-state systems: they aim to use counterintuitive effects including quantum superposition and entanglement to perform tasks such as quantum simulation, quantum search, and factoring/code-breaking to solve problems that are hard or impossible on conventional classical computers. Quantum communication systems transmit information encoded in individual photons: they exploit the fact that quantum measurement is inevitably stochastic and destructive to enact quantum-encrypted communication whose security is guaranteed by the laws of physics. Quantum sensors and measurement devices operate at the greatest possible sensitivity and precision allowed by physical law: from magnetometers to quantum clocks to advanced gravitational interferometers (e.g., LIGO), quantum metrology supplies the techniques required to push measurement to its ultimate limits.
As transistor dimensions approach atomic scale, quantum-mechanical effects such as tunneling and spin become important ingredients in accurate performance models of integrated circuits. Theoretical work suggests that power-density required to separate signal from noise motivates a departure from common practice of representing logic 0s and 1s by charges, voltages or currents. Conventional information processing relies on discretized analog signals to convey digital information. This is problematic at the nanoscale due to the inherent uncertainty and noise that manifest themselves in many different ways, already plaguing leading-edge CMOS technologies. At the nanoscale signals are drowned by noise, much of it quantum-mechanical in origin. Therefore we depart from the simple discretization of analog signals, and instead rely on ”a two-valued quantum degree of freedom” suggested in 1924 by Paul Dirac, now commonly known as the spin of nuclei, electrons and other particles. Spin is an example of a logical quantum bit or qubit, which replaces the conventional bit in quantum computing. Qubits can also be implemented by the polarization (horizontal and vertical) of photons. A well-publicized demonstration by IBM in 2000 subjected nuclear-spin qubits to quantum gates implemented by RF pulse trains. More recent work shows that such stationary qubits can be converted into “flying” photonic qubits, and back, while single-photon quantum communication has been demonstrated at distance of 100 kilometers in the open air. While quantum information processing seems inevitable at the nanoscale in one form or another, the behavior of quantum circuits can differ drastically from that of modern digital electronics. A key difference lies in the fact that quantum behavior is inherently uncertain. For example, state measurement is probabilistic and the measurement process itself affects the state being measured. Nevertheless, practical computation is possible in the quantum domain, and, in a few important cases, quantum algorithms can achieve far higher computation speeds than any comparable classical algorithms. Moreover, advantage can be taken of quantum uncertainty to achieve highly secure digital communications. Quantum behavior also has both digital and analog aspects, and quantum devices are highly error-prone. For example, quantum gate operations are defined by analog parameters that allow small errors to arise and propagate to other gates. Unlike classical analog signals, however, qubits are amenable to error correction. Qubits have many more failure modes than are found in non-quantum circuits. For example, quantum signal states are inherently unstable and tend to decay rapidly due to interaction with the environment (decoherence). In spite of this, highly effective error-correction methods are known for quantum computation. This talk will discuss computation with quantum circuits, and show how their behavioral uncertainty can be overcome. In particular, the challenges of modeling, testing and tolerating errors in quantum circuits will be addressed.
Quantum computation exploits superposition, entanglement, interference, and measurement to process information.Research has moved from theoretical algorithms to functional devices where the performance is not only limited by qubit physics, but also by the control, compilation, error mitigation, and error correction.This review considers quantum computing on various levels of abstraction, including physical qubits, quantum algorithms, system architecture, fault-tolerance, and emerging applications.Study-specific evidence regarding scale, operational quality, connectivity, control and suppression of errors is compared across the different types of superconducting circuits, trapped ions, semiconductor spin qubits, neutral atoms and photonic processors.The review of the fundamental algorithms, noisy intermediate-scale quantum computing, variational and hybrid quantum-classical approaches, quantum simulation, quantum machine learning, and security implications is also discussed.Measurable progress has been demonstrated in published experiments for logical-qubit operation, below-threshold surface-code memory, programmable photonic sampling and platformspecific scaling.However, the reported metrics are not directly interchangeable, and practical advantage remains task-and baseline-dependent.Further progress will need to be accompanied by coordinated developments in hardware reliability, error correction, classical control, estimates of algorithmic resources, and clear classical method comparisons.
Quantum mini-toc
In a world overwhelmed by increasing amounts of data, finding new ways to store and process information has become a necessity. Conventional silicon-based electronics has experienced rapid and steady growth, thanks to the progressive miniaturization of its basic component, the transistor, but that trend cannot continue indefinitely.
![Figure][1]
CREDIT: PHOTO: CURT SUPLEE AND EMILY EDWARDS, JOINT QUANTUM INSTITUTE AND UNIVERSITY OF MARYLAND; CHIP FABRICATION: SANDIA NATIONAL LABORATORIES WITH SUPPORT FROM THE INTELLIGENCE ADVANCED RESEARCH PROJECTS ACTIVITY
In conventional devices, information is stored and manipulated in binary form: The elementary components of these devices—the so-called bits—have two states, each of which encodes the binary 0 or 1. To move beyond the binary system, one can exploit the laws of quantum mechanics. A quantum-mechanical object with two energy levels at its disposal can occupy either of those two levels, but also an arbitrary combination (superposition) of the two, much like an electron in a two-slit experiment can go through both slits at once. This results in infinitely many quantum states that a single quantum bit, or qubit, can take; together with another strange property of quantum mechanics—entanglement—it allows for a much more powerful information platform than is possible with conventional components.
Quantum information processing (QIP) uses qubits as its basic information units. QIP has many facets, from quantum simulation, to cryptography, to quantum computation, which is expected to solve problems more complex than those within the capabilities of conventional computers. To be useful for QIP, a qubit needs to be both isolated from its environment and tightly controllable, which places stringent requirements on its physical realization. But this is only the first step; to build a quantum computer, for example, we must also have a scalable architecture and error correction that can be performed in parallel with computation; in addition, efficient quantum algorithms must exist for solving the problem at hand—a considerable theoretical challenge.
A number of qubit types have been proposed and experimentally realized that satisfy at least some of these criteria, and tremendous progress has been made over the past decade in improving the figures of merit, such as the coherence time. In this special section, four Reviews look into the future of QIP in some of its most promising physical realizations. On p. [1164][2], Monroe and Kim discuss the challenges of scaling trapped ion architectures to hundreds and thousands of qubits and beyond. Devoret and Schoelkopf (p. [1169][3]) speculate on the future of superconducting circuits, whereas Awschalom et al. (p. [1174][4]) focus on the many promising qubit flavors based on spins in semiconductors. Finally, Stern and Lindner (p. [1179][5]) lay out the prospects for quantum computation using the entirely different approach of topologically protected states.
The future of QIP appears bright in spite of the many remaining challenges. As a bonus, overcoming these challenges will probably also advance basic research.
[1]: pending:yes
[2]: /lookup/volpage/339/1164?iss=6124
[3]: /lookup/volpage/339/1169?iss=6124
[4]: /lookup/volpage/339/1174?iss=6124
[5]: /lookup/volpage/339/1179?iss=6124
The pursuit of a universal, fault-tolerant quantum computer has transformed quantum computing from a predominantly theoretical discipline into a rapidly diversifying hardware ecosystem. This review presents a comprehensive and technically grounded analysis of the principal physical qubit modalities: superconducting circuits, trapped ions, photonic platforms, neutral atoms, semiconductor spin qubits, and topological approaches, focusing on their underlying operating principles, performance characteristics, and system-level constraints. We examine how these qubit technologies are embedded within scalable quantum processor architectures, including control and readout infrastructures, connectivity topologies, and quantum error correction strategies. The review provides a comparative study of key performance metrics, such as coherence times, gate fidelities, error rates, and scalability, reveals fundamental trade-offs between speed, robustness, and operational complexity that shape distinct technological roadmaps and target applications. We further highlight the ongoing transition from noisy intermediate-scale quantum (NISQ) devices toward architectures centred on logical qubits, driven by advances in materials engineering, precision control, and quantum information theory. Together, these developments sketch the emerging routes toward fault-tolerant quantum computing and practical quantum advantage, while offering readers a coherent entry point and reference guide to the current quantum hardware landscape.
Quantum computing exploits superposition and entanglement to perform computations beyond the capabilities of classical machines. However, practical quantum systems operate under substantial environmental noise, measurement uncertainty, and decoherence, leading to ambiguous or partially determined quantum states. Conventional probabilistic descriptions capture stochastic behaviour but do not explicitly represent indeterminacy arising from incomplete information or measurement ambiguity. In this work, we introduce a neutrosophic representation of qubit states that integrates principles of Neutrosophic Logic with the mathematical framework of Quantum Computing. In the proposed model, a qubit is characterised by three independent parameters representing truth, indeterminacy, and falsity. This formulation enables explicit representation of ambiguous quantum states and uncertain measurement outcomes. We extend the neutrosophic formalism to quantum gate operations and analyse how the model captures uncertainty propagation in quantum circuits. Through theoretical examples and graphical analysis, we demonstrate that neutrosophic representation provides a richer description of uncertainty compared with conventional probabilistic approaches. The proposed framework offers a conceptual pathway for improved modelling of noisy intermediate-scale quantum devices and may contribute to the development of uncertainty-aware quantum computing architectures.