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Pushpendra Singh, Pathik Sahoo, C. S. Yadav, Laxmidhar Behera, [Anirban Bandyopadhyay](https://orcid.org/0000-0002-8823-4914)

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[An Optical Quantum Computer that Uses Both Quantum Logic Gate and Quantum Annealing](https://mdr.nims.go.jp/datasets/ceae1512-972c-43cb-80e7-3e8be3eb676f)

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1413An Optical Quantum Computer that Uses Both Quantum Logic Gate and Quantum AnnealingPushpendra Singh1, Pathik Sahoo1, C.S. Yadav2, 3, Laxmidhar Behera2, 3, Anirban Bandyopadhyay11International Center for Materials Nanoarchitectronics (MANA), NIMS, 1-2-1 Sengen, Tsukuba, Ibaraki-3050047, Japan.2School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Mandi 175075,  India.3Center for Quantum Science and Technologies (CQST), Indian Institute of Technology Mandi, Kamand, Mandi 175075, IndiaAbstract. In conventional logic gate-based quantum computing, qubits have specific functions but require cryogenic temperatures. Quantum annealing-based quantum computing faces challenges like decoherence and non-adiabatic transitions. To address these issues, we introduce the QGPU (Quantum Graphical Processing Unit), which operates at room temperature, instantly converting patterns in data into organized information. It utilizes an organic gel processor, self-assembled nanowires, and single-photon reading to create optical vortex holograms revealing relationships. The gel can be reused for different tasks, and multiple processors communicate via quantum teleportation, eliminating the need for traditional quantum algorithms.Keywords: Quantum computer, Optical vortex, 3D single photon, Helical nanowire self assembly, clocks assembly.IntroductionRoom temperature quantum systems, as challenged by protecting qubits from external disturbances [1], face issues related to energy level adjustments to match available energy (kBT). This environment increases decoherence rates, requiring quantum error correction [2]. With cooling impractical, alternative strategies like remote qubit coupling via electromagnetic resonance and enhanced squeezing to combat errors are explored [3]. New materials like diamond NV centers offer room temperature quantum computing potential [4], promising practical applications.In quantum computing, entanglement enables instantaneous connections between choices. Entangled qubits possess geometric phases, modified by quantum logic gates [5], while dynamic phases in the classical world remain constant [6]. Quantum circuits map these phase connections, yielding answers for connected phases. Current quantum computers link a few classical states (Qudits) to numerous quantum choices, constraining their capabilities [7].Generating single photons, particularly through SPDC (Single Photon Down Conversion), faces challenges. Common approaches use nonlinear materials for parametric down conversion, producing a photon pair separable by spectral, spatial, or polarization properties [8]. While it provides tunable wavelengths and potential for photon detection, it exhibits Poisson-like statistical emission, lacking on-demand single photon creation [9]. Researchers prefer antibunched radiation from quantum emitters for deterministic single photon creation [10]. Addressing losses during emission, collection, and detection is crucial. This study improves single photon generation using efficient organic molecule emitters, refined excitation pulses, optimized optics, and innovative metallo-dielectric antennas [11].The challenge with logic gate-based quantum computers lies in the need for individual qubits to operate independently, as these computers rely on formal circuitry where each qubit plays a specific role [12]. To achieve this, the entire circuit must be enclosed within a cryogenic environment to shield it from external noise. Conversely, quantum annealing-based quantum computers face issues related to decoherence and non-adiabatic transitions, as qubits are collectively addressed [13]. As the system point traverses a potential landscape with local and global minima, crucial peak characteristics shift, leading to alterations in computation Hamiltonians. Adiabatic quantum computation uses the adiabatic transformation of a quantum system to solve computational problems [14]. It transitions from the ground state of a driving Hamiltonian to the ground state of a problem Hamiltonian, containing the solution [15]. Gate-based quantum computing relies on superposition and entanglement for concurrent processing, demanding precise qubit manipulation [16 ]. It has the potential to create a universal Turing machine, offering a different quantum computing paradigm.Adiabatic quantum computing uses adiabatic evolution, transitioning from a simple Hamiltonian to a problem Hamiltonian to find the answer [17]. It excels at handling specific errors and optimization problems. Gate-based quantum computing is versatile but handles a broader range of tasks [18]. The choice depends on the problem and available resources. While there have been efforts to create an adiabatic quantum computer capable of performing logical operations (Fig.1), these attempts have not been successful so far. If we could combine both types of computations, it would allow us to manipulate individual qubits and include all qubits in the driving Hamiltonian, which would transition them together to the problem Hamiltonian. This way, the problem Hamiltonian could find the solution to the problem.DiscussionWhat are the existing challenges, and how do we address them in our quantum computing approach?(i). Developing quantum computers faces the challenge of categorizing potential solutions into groups for Quantum Fourier Transform (QFT) operations. This process is time-consuming and requires software engineers to understand all potential solutions and their relationships using QFT before creating quantum algorithms. The classification process is often manual and involves simulations. Only after overcoming these challenges and designing a quantum algorithm can it be implemented on a quantum processor.We've created a protocol that makes use of microwave and radiowave-induced organic synthesis to automate the time-consuming tasks in the mentioned process.(ii). A major challenge in quantum computing is ensuring qubits remain active throughout complex quantum circuit operations. This extends beyond increasing connected qubits; maintaining connections, even with distant qubits, is crucial. Research on linking these operations is limited, and practical implementation of accessing data without disrupting connections remains theoretical. Handling information across different connection layers is challenging, and the "quantum wire problem" arises due to limited wire packing in quantum circuits, potentially causing unwanted connections or requiring connectivity changes.We addressed three quantum computing challenges by developing a 12-qubit hologram forming a dodecahedron-shaped 3D structure with optical vortex rings. These rings allow diverse 3D shapes while preserving data integrity, enabling entire quantum circuits within a single structure [19]. Special sensors detect variable changes, storing events as distinct Hilbert spaces forming 3D clock assemblies. The computer analyzes new data, predicting outcomes and representing the structure mathematically. Quantum computing resolves qubit preservation, complex connections, and data management challenges for error-free operation.(iii). Creating algorithms for classical computers involves step-by-step instructions, even for self-learning algorithms. Logic gate-based quantum computers require individual qubit handling and connectivity [12], while adiabatic quantum computers may overlook problem details [17]. Combining the strengths of both computer types by merging them effectively has not yet been achieved.To combine both computing concepts, we create a specialized qubit that collaborates with others to form a structure (Fig.1). This structure transforms from the starting Hamiltonian to the problem Hamiltonian, encoding input details precisely. Logic gates craft an accurate driving Hamiltonian, and as the structure evolves, it organizes QFT modules, representing the problem Hamiltonian. Quantum annealing condenses these modules into the solution.Fig. 1. Elementary quantum computing device alternative to logic gate: A. The device performs orthogonal operations using, ; where A=, B=; It generates an invariant with in helical nanowires based on two variables, A and B, and deforms a 3D optical structure. B. We highlight the contrast between quantum annealing and classical annealing through a potential profile. Molecular precursors for helical organic nanowires' gel are represented by black dots. Nanowire dimensions (length, pitch, diameter) are determined by a function, f, related to symmetrical energy minima. Input data is encoded with microwaves, causing nanowires to self-assemble and generate unique 3D light patterns, including vector vortex beams, polyatomic time crystals, and 3D light holograms. C. Quantum computing has two categories: logic gate-based and quantum annealing. Our approach combines both with seven extra features, including advanced network modeling.2.2. Advantages for invariant quantum computing2.2.1. Alleviating the need for a quantum algorithm by using QFT-like operations during the initial processing of input information: In conventional quantum computers, when dealing with a problem or dataset that contains all potential solutions [20], we split it into different groups where similar data is stored or generated. Each of these groups then needs to be programmed as a QFT system, which can be complex for real-world problems that no quantum computer has successfully tackled. To address this, we've created a system that captures these complex relationships and essential QFT settings from periodic events in the input information. This way of capturing information is quite different, and once the relationships are identified, they are built into the system in an analog way, ensuring that these relationships remain intact throughout the processing.2.2.2. Creating quantum logic gates and quantum Fourier transform circuits automatically: In conventional quantum computer design, a logic gate's job is to take a bunch of qubits as input and adjust how relative phases connected in the output while keeping their entanglement intact [21]. The main goal is to change the geometric phases of these qubits in a way that's needed for our calculations. In our quantum computer, we use a unique nanowire structure that starts from a single molecule and grows to a larger size in a chemical reaction vessel, acting as a processor. This growth is guided by a set of rules that stay the same among different factors. It's important to note that in our quantum computer, we don't take a stream of qubits and use various logic gates to change how they're connected in a way that leads to the desired result through a sequence of measurements.2.2.4. Ensuring stability and protection from interference: Our quit combines photons spinning in both directions (clockwise and anticlockwise), forming a 3D shape, ensuring entanglement remains undisturbed. Noise struggles to disrupt the balanced structure due to aperiodic signals. Combining two 3D light structures (Fig.1b,c) is similar to atoms bonding in a molecule. The singularity points in these structures connect, resembling a bonded molecule. Topological stability enables quantum computing at room temperature in ambient conditions.2.2.6. Wireless Processing memory and processor are same device: In our computing method, we arrange qubits using photons on a 3D geometric structure, and each layer of this structure has a specific way it's organized. A helical nanowire can create shapes like a 12-plane dodecahedron or a 20-plane icosahedrons [19]. When these helical nanowires build layer upon layer, they create a network made up of millions of nanowires, forming a structured hierarchy. During resonant communication, these self-similar symmetries come together to create a single interconnected element. In simpler terms, it's like having a wireless connection for quantum circuits. But there's something even more important here. Because these nanowires keep stacking on top of each other in a self-assembling way, the symmetries in this hierarchical structure make sure that there's a circuit within a circuit.2.2.7. Pure Optical Quantum Computing: No Need for cryogenic Low Temperatures: We use organic helical nanowires as the basic decision-making components of our quantum computer. Now, we might wonder how a quantum state can survive in a hot chemical beaker. But here's the key: we're not actually creating qubits inside the nanowire or its assembled structure. We only use matter to adjust the topological photon or 3D hologram structure, which represents a 12-qubit optical setup serving as a logic gate processor. To make this work, we need to make changes in the 12 singularity points within this photon structure. Therefore, we don't need to freeze the nanowires to extremely low temperatures [Fig.2].Fig. 2. a. This figure illustrates a single photon source setup using a Neatcell laser pen, emitting 630nm wavelength pulses (1mW) through neodymium magnets (1.4 tesla), ferromagnetic fluid (FMF) with Ni nanowires in oil spheres, and a magnetic pulse generator (MPG) (30KHz frequency, 2-6 tesla field strength). A vortex lens (VL) with oils of varying refractive indices converts the laser pulse into single photons. Panel b displays laser pulse light diffraction, reflection, transmission, and refraction during nanowire evolution to helical structures, observed by reaction vessel and projection cameras. Panel c presents a truth table for optical vortices-based logic gate [22] with two inputs and two outputs (dodecahedron light shape), analyzed using Image J. Panel d highlights the quantum annealing-like behavior with increased nanowire twisting.2.3. How is our quantum computer different from other versions of quantum computer?2.3.1. We use a polyatomic time crystal to alleviate the need for Quantum Fourier Transform (QFT): Logic gate-based quantum computing works by dealing with groups of options where all possible paths to find solutions are connected or entangled in a certain way, and they are processed together using microwave signals. We have a different approach. Instead of handling these options separately, we treat recurring events on a cosmic scale as a single unit, identify various types of these units, arrange them in a 3D space, which we call a polyatomic time crystal [23], and then use it for computation in a chemical beaker. So, our input is prepared to provide more information than QFT. In QFT, individual options or variables are processed separately, but with our method, we capture the relationships and combinations among different cosmic events, units, or variables.2.3.2. We're changing the way we think about dimension to create the basic operation of a computer. In conventional quantum computing, adding a new dimension means extending a new radial vector from the center of the Bloch sphere in the same space [24]. But we do things differently. We use quantum technology to perform a generic orthogonal transformation (Fig.1a). This transformation helps us discover invariants by looking at how light and matter interact. The result we get, which we call an "invariant," belongs to a higher dimension. It's not just a rotation within the same space.2.3.3. Replace quantum logic gate with unique self-assembly process of helical nanowires. In this process, helical nanowires self-assemble, creating invariants layer by layer, with each layer resonating in its own time domain. A single photon, a geometric shape like a dodecahedron, interacts with all these layers, causing deformation in all 12 planes [19]. Each layer of nanowires modifies each plane, resulting in a 12D vector. The output is the differential product of two 12D complex vectors. While a quantum logic gate rotates the phases of input states to create a unique relative relationship among them in the output, our process involves a combination of rotations and phase shifts, connecting invariants of different dimensions (Fig.1a). By synthesizing this network of invariants, we achieve much more than what a conventional quantum logic gate can deliver.2.3.4. Problem specific circuit synthesizer instead of conventional quantum computer that uses a pre-built quantum circuit. Here, we transform a chemical beaker that typically synthesizes organic gel into a circuit fabricator. We achieve this by directing an infra-red laser into the beaker precisely when single precursor molecules are forming helical nanowires. This laser-controlled process allows us to finely adjust the nanowires' geometry. By leveraging chemical energy and coherent phonons, we transform the reaction beaker into a plasma state, which is neither a liquid nor a gel. In this state, it becomes receptive to microwave input, enabling the creation of a customized 3D circuit for a specific problem. What's unique is that we explore the possibility of naturally synthesizing quantum circuits in real-time, without any human intervention. Additionally, the gel can be melted and reused for future applications.. 2.3.5. Harnessing the True Essence of Quantum Mechanics: Despite being physically separated, 16 chemical beakers collectively generate a single quantum state, forming a 16x16 tensor. We employ teleportation to enable interactions between the components of this tensor, originating from the 16 distinct beakers (Fig.5). By altering the compositions of these beakers, we guide the identical information towards distinct symmetries, allowing teleportation to facilitate the automated evaluation of various potential scenarios. This process involves phase shifts and rotation mappings, which help establish conditional connections between different future outcomes, offering a glimpse into the intricate web of possibilities.2.4. Construction of various tools 2.4.1. Generating a Single Photon Source: We achieve a single photon source by mechanically accelerating helical nanowires [25] within a magnetic film using a laser light source. Inside an oil bubble, we have Ni nanowires arranged in a thin film, which is exposed to laser light, converting it into a single photon (Fig.2a)2.4.2. Topological Photon Source: Our quantum computer relies on molding light [26] into 3D shapes like cubes, prisms, spheres, and more. It compares these modified structures with the original. Light-matter interaction provides input information and influences hidden symmetries embedded in the light structure [27]. The result is a statistical distribution of altered light structures revealing the input phenomenon's evolution in the output. This approach eliminates the need for cooling, as nanowires serve as memory storage and topology modulators for light structures [28] (Fig. 2b). The qubit is encoded within the light structure, not the nanowire, with the evanescent wave carrying all the information.2.4.3. Memory and Processing in Quantum Computing: Our quantum computer takes the form of an optical device, a 3D photon structure comprising 12/20 planes that dynamically alter their geometry for computation. It is a well-established fact that unlike conventional computers, optical structures cannot possess memory that can be stored, read, and rewritten [29]. In our computing setup, the memory is stored within a helical nanowire, which is synthesized on demand within the chemical reaction chamber based on the input data. This memory device manipulates the 3D structure of light to encode and retain information (Fig.2b). However, multiple 3D structures interfere in space or exchange quantum information. 2.4.4. 3D Electromagnetic Field Network: Interactive Quantum Mode for User-Defined Applications: Within our quantum system, we utilize a 3D electromagnetic field network for interactive quantum operations [30]. Single photons from 16 chemical processors are directed into an optical fiber within a cavity where their outputs combine (Fig.5). This optical cavity creates 3D electromagnetic patterns, or "knots," allowing user engagement. Users can pose questions and gain insights about symmetries and even receive future predictions by interacting with the superimposed 3D light structure via the electromagnetic field network.2.5. How our computer executes the computing process: In decision-making, we create a 3D clock assembly (or GML) from initial input, yielding a map of variable interactions and potential outcomes. This assembly also holds inherent features for novel ideas. It replaces the role of a software engineer by generating new compositions from a single input melody. Unlike traditional algorithms, it focuses on relationships between facts, forming invariants as memory. These invariants connect like musics, revealing patterns and unique compositions that influence events. Each prime number has a distinct characteristic in this context. In our computing system, we employ a Geometric Musical Language (GML), where variables or periodicity of an event are identified as a clock integrated into a phase sphere or Bloch sphere [31]. Consequently, a 3D collection of clocks corresponds to a 3D collection of Bloch spheres. The uniqueness lies in our utilization of this geometric arrangement of Bloch spheres, which facilitates the encoding of quantum information in higher dimensions.The 3D clock assembly acts as a quantum circuit with embedded smaller 3D assemblies, enveloped externally by a single clock. These inner "within" and outer "above" worlds are encoded in orthogonal spaces or distinct dimensions. Input data in these layers yields invariants from their interactions, facilitated by Dodecanion or multinion algebra [32]. Multinion algebra maps diverse multinions across dimensions, forming memory layers with geometric shape invariants. These invariants emerge from interactions between dimensions, forming an interconnected invariant network in the output.2.5. How do we ensure the fusion of a logic gate and quantum annealing? We achieve the fusion of a logic gate and quantum annealing by introducing tiny molecules into a chemical beaker, transmitting input information using electromagnetic signals, which causes the molecules to form nanowires representing the initial problem Hamiltonian aligned with helical symmetry. The nanowires autonomously assemble into a superstructure, forming a discernible correlation and heightened connectivity among them. This connectivity facilitates the emergence of optimized solutions in the form of optical vortices. Increased cell occupancy expands the options for problem-solving, akin to quantum annealing, where more accessible configurations enhance solution selection (Fig. 2b, d).Fig. 3. Designing a 16D Quantum Computer: In the first phase, a single photon is created as laser light passes through two layers: L1, containing a ferromagnetic nanowire liquid with a magnet pair, and L2, an oil microsphere film filled with Ni nanowires. This process generates squeezed light (S2). Custom multi-layered vortex lenses are introduced after L2, featuring a dodecahedron, truncated icosahedron, and icosahedron, representing topological phases. These phases superimpose with metastable ones, forming a superposed topological vortex (Tv). On the right side, input data transforms into a polyatomic time crystal, with associated frequencies directed to 16 organic gel solutions. Each gel generates helical nanowires with specific symmetries, resulting in distinct gel structures (C2, C3, C5, C7, C11, etc., up to C43 and C47). When S2 light passes through these gels, it forms a 3D vortex structure similar to Tv, creating a differential clock architecture, manifested as a polyatomic time crystal. This structure, comprising variable-invariant clocks, represents unknown data as a dynamic model, elucidating our computational process.An infrared laser further propels system evolution and correction. The process results in two types of Hamiltonians: one for logic gate operations and the other for quantum annealing. We've constructed a truth table illustrating optical vortices' logic gate [22] behavior with two inputs: applied frequency and illuminated area (Fig. 2c). The gate produces two outputs related to optical vortex areas beyond certain thresholds. We've derived this truth table from experiments, enabling precise qubit control during information processing.2.6. How do we prove that quantum computing is really happening in our quantum computer? We're comparing two computing outputs: one using a single photon (Fig. 3a or Fig. 2a) and the other is using a topological photon from helical nanowires [26] (Fig 3b), both processing the same input. If unique symmetries among periodic events are present only when a single photon is used, it satisfies Bell's inequality, indicating contributions beyond the limit in the chemical beaker-based processor. Our comprehensive quantum computer interface allows users to input graphics, algorithms, and frequency spectrums, which are converted into electromagnetic signal frequencies and mapped to build a 3D structure (Fig.3) in the chemical beaker over time. This analog approach captures the true topological form of the input data, unlike digital cameras. The first version of our quantum computer prototype is shown here (Fig. 4). Fig. 4. A prototype of our quantum computer2.7. Getting rid of thermal noise: adiabatic insulation: To counteract disruptions caused by thermal noise and environmental signals, we employ a molecular concept. We consider each molecule as having two parts: a rapidly vibrating, isolated central core and a slower-moving outer periphery that interacts with the environment. This results in a significant mismatch in vibrational frequencies, creating an adiabatic system (Fig. 5). An additional vibration with a geometric phase occurs at the junction between these regions. We select molecules with this two-layer structure as the nanowire grows, ensuring the system's resilience against disruptions while preserving entanglement integrity.Our approach employs a consistent spiraling pattern that responds to noise by either converging or diverging, generating positive or negative vortices. This pattern remains consistent across our computing process, including the initial electromagnetic signals that are transformed into vortex pattern adding an angular momentum [33]. Fig. 5. To ensure entanglement is maintained, we addressed the concept of molecular properties, developing a structure with a significant mismatch in vibrational frequencies between the inner and outer cores. This creates an adiabatic system that is resilient to disruptions from thermal noise and unwanted signals, preserving entanglement.Our materials, from small helical wires to larger structures, remain to this helical pattern. We've created a complex network of these vortex patterns (Fig.2b), where each wire contributes to the overall structure. Noise would need to disrupt the entire network of vortex patterns to cause decoherence or abrupt changes. We depart from the conventional approach of using the ground state of a potential well as the operational state for our quantum computer. Instead, we opt for a specific higher excited state level as the exclusive operational state for the entire circuit. This choice ensures that any deviations in energy levels are within the limits of available thermal energy (kBT) in the environment. Our lightweight quantum computer utilizes light compression, allowing us to perform computations by modifying the compressed light's geometric configuration while preserving qubit entanglement as long as the light structure remains intact.ConclusionIn conclusion, current quantum computing methods face limitations and often necessitate intricate and expensive cryogenic setups. However, our work introduces the QGPU, a room-temperature quantum computer that utilizes a novel clock-based approach to tackle intricate problems without relying on conventional quantum algorithms. This breakthrough has the potential to transform numerous domains, such as science, medicine, and engineering, enabling advancements in drug development, material design, financial modeling, and beyond. Moreover, the QGPU's ability to function at room temperature enhances its accessibility, opening doors for a broader user base to harness its computational power.        Acknowledgements: The authors acknowledge the Asian Office of Aerospace R&D (AOARD), a part of the United States Air Force (USAF), for Grant no. FA2386-16-1-0003 (2016–2019) on electromagnetic resonance-based communication and intelligence of biomaterials. This work was partly supported by World Premier International Research Center Initiative (WPI Initiative), MEXT, Japan.References1. Gundogdu, K., et al.: Quantum Analog of Vibration Isolation: From Room Temperature Superfluorescence to High Temperature Superconductivity. (2022).https://browse.arxiv.org/pdf/2204.09807.pdf2. Novais, E., & Baranger, H.U.: Decoherence by correlated noise and quantum error correction. Physical review letters 97 (4), 040501 (2005).  .3. Rodríguez-Briones, N.A., Li, J.Y., Peng, X., Mor, T., Weinstein, Y., & Laflamme, R.: Heat-bath algorithmic cooling with correlated qubit-environment interactions. New Journal of Physics 19 113047 (2017).4. Childress, L.I., & Hanson, R.:  Diamond NV centers for quantum computing and quantum networks. MRS Bulletin 38, 134-138 (2013).5. Chen, M., Chen, R., & Yang, Z.: Geometric phase of entangled atomic qubits coupled to Lorentzian reservoirs. Annals of Physics 387, 271-280 (2017).6. Hamazaki, R.: Exceptional Dynamical Phase Transitions in Periodically Driven Quantum Systems. Nature Communications 12, 5108 (2021).7. Tacchino, F., Chiesa, A., Carretta, S., & Gerace, D.: Quantum Computers as Universal Quantum Simulators: State‐of‐the‐Art and Perspectives. Advanced Quantum Technologies 3, 27 (2019).8. Peřina, J., Centini, M., Sibilia, C., & Bertolotti, M.: Photon-pair generation in random nonlinear layered structures. Physical Review A 80, 033844 (2009).9. Lounis, B., Lounis, B., & Moerner, W.E.: Single photons on demand from a single molecule at room temperature. Nature 407, 491-493 (2000).10. Abudayyeh, H., Blake, A.V., Bräuer, A., Liran, D., Lubotzky, B., Majumder, S., Hollingsworth, J.A., Fleischer, M., & Rapaport, R.: Highly Efficient and Pure Single Photon Streams at Room-Temperature by Deterministic Nano Positioning of Quantum Emitters. Conference on Lasers and Electro-Optics (CLEO), 1-2 (2021).11. Morozov, S., Gaio, M., Maier, S.A., & Sapienza, R.: Metal-Dielectric Parabolic Antenna for Directing Single Photons. Nano letters 18 (5), 3060-3065 (2018).12. Hunger, D.: Quantum logic at a distance. Science 371, 576 – 576 (2021).13. Albash, T., & Lidar, D.A.: Decoherence in adiabatic quantum computation. Physical Review A 91, 062320 (2015).14. Nakayama, S., Peng, G., & Iimura, I.: Study on Quantum Parallel Processing by Adiabatic Quantum Computation in Bernstein-Vazirani Problem. 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies, 678-682 (2012).15. Takashi, I., et al.: Demonstration of the excited-state search on the D-wave quantum annealer. (2023).  https://arxiv.org/pdf/2305.15974.pdf16. Sinuco-León, G. A. and Garraway, B. M.: Addressed qubit manipulation in radio-frequency dressed lattices, New J. Phys. 18 035009 (2016).17. Subaşı, Y., Somma, R.D., & Orsucci, D.: Quantum algorithms for systems of linear equations inspired by adiabatic quantum computing. Phys. Rev. Lett. 122, 060504, (2019)18. Reiserer, A., Kalb, N., Rempe, G., & Ritter, S.: A quantum gate between a flying optical photon and a single trapped atom. Nature 508, 237-240 (2014).19. Sahoo, P., et al.: A general-purpose organic gel computer that learns by itself. Neuromorphic Computing and Engineering (under press) (2023)20. Steeb, W., & Hardy, Y.: Problems & solutions in quantum computing & quantum information. 2nd Revised ed. World Scientific Publishing Company  (2004).21. Daiss, S., et al.: A Quantum-Logic Gate between Distant Quantum-Network Modules. Science 371, ( 6529) 614-617 (2021).22. Liu, H., Deng, H., Deng, S., Teng, C., Chen, M., & Yuan, L.: Vortex Beam Encoded All-Optical Logic Gates Based on Nano-Ring Plasmonic Antennas. Nanomaterials  9(12), 1649 (2019).23. Saxena, K., et al.: Polyatomic time crystals of the brain neuron extracted microtubule are projected like a hologram meters away. Journal of Applied Physics  132, 194401 (2022). https://doi.org/10.1063/5.013061824. Wharton, K.B., & Koch, D.M.: Unit quaternions and the Bloch sphere. Journal of Physics A: Mathematical and Theoretical 48, 235302 (2014).25. Claudon, J., Gregersen, N., Lalanne, P., & Gerard, J.M.: Harnessing light with photonic nanowires: fundamentals and applications to quantum optics. Chemphyschem : a European journal of chemical physics and physical chemistry 14 11, 2393-402 (2013) .26. Yan, Q., Hu, X., Fu, Y., Lu, C., Fan, C., Liu, Q., Feng, X., Sun, Q., & Gong, Q.: Quantum Topological Photonics. Advanced Optical Materials 9 (2021). 27. Zambrana-Puyalto, X., Vidal, X. & Molina-Terriza, G.: Angular momentum-induced circular dichroism in non-chiral nanostructures. Nat Commun 5, 4922 (2014).28. Sun, C., Li, J., Song, Q., Ma, Y., Zhang, Z., De, J., Liao, Q., Fu, H., Yao, J., & Zhang, H.: Lasing from Organic Micro-Helix. Angewandte Chemie (2020). 29. Rajchman, J.A.: An optical read-write mass memory. Applied optics 9 (10), 2269-71 (1970).30. Cetto, A.M., & Peña, L.D.: The Electromagnetic Vacuum Field as an Essential Hidden Ingredient of the Quantum-Mechanical Ontology. Entropy 24(12), 1717 (2022).31. Lokesh, A., et al.: Fractal Information Theory (FIT)-Derived Geometric Musical Language (GML) for Brain-Inspired Hypercomputing. Advances in Intelligent Systems and Computing, Springer 584 343–372 (2018). 32. Pramanik, S., et al.: 1D to 20D Tensors Like Dodecanions and Icosanions to Model Human Cognition as Morphogenesis in the Density of Primes. Lecture Notes in Networks and Systems, Springer 618, 449–467 (2023).33. Sahoo, P., et al.: A Third Angular Momentum of Photons. Symmetry 15, 158 (2023). image2.pngimage3.pngimage4.jpegimage5.pngimage1.jpeg