The quest to build artificial quantum computers has driven decades of research into maintaining coherence at millikelvin temperatures, requiring extreme isolation and consuming enormous resources. Yet nature has already solved the quantum computing problem: electrons operate continuously at the R=F equilibrium point, where rotational and feedback processes balance perfectly. This equilibrium manifests as the fine structure constant α = 1/137.036, representing the optimal architecture for quantum information processing.Rather than attempting to artificially recreate quantum coherence, we propose a paradigm shift: developing electron-interface technology that harnesses the natural computational capacity of electrons at their intrinsic operating point. Within the framework of Fractal Uncertainty Theory (FUT), we demonstrate why electrons represent superior quantum computers compared to artificial systems, explain α as a fundamental computational architecture, and outline a research roadmap toward interfacing with nature’s quantum substrate rather than building fragile imitations of it.The implications are profound: room-temperature quantum computing, trillion-qubit scalability, and energy-efficient quantum information processing—all by working with nature’s design rather than against it.
The quest to build artificial quantum computers has driven decades of research into maintaining coherence at millikelvin temperatures, requiring extreme isolation and consuming enormous resources. Yet nature has already solved the quantum computing problem: electrons operate continuously at the R=F equilibrium point, where rotational and feedback processes balance perfectly. This equilibrium manifests as the fine structure constant α = 1/137.036, representing the optimal architecture for quantum information processing.Rather than attempting to artificially recreate quantum coherence, we propose a paradigm shift: developing electron-interface technology that harnesses the natural computational capacity of electrons at their intrinsic operating point. Within the framework of Fractal Uncertainty Theory (FUT), we demonstrate why electrons represent superior quantum computers compared to artificial systems, explain α as a fundamental computational architecture, and outline a research roadmap toward interfacing with nature’s quantum substrate rather than building fragile imitations of it.The implications are profound: room-temperature quantum computing, trillion-qubit scalability, and energy-efficient quantum information processing—all by working with nature’s design rather than against it.
The prevailing assumption in quantum computing, that quantum effects require near-absolute-zero temperatures to be computationally useful, rests on an engineering convenience, not a physical law. Biological systems routinely exploit quantum coherence at 310 °K (human body temperature), as demonstrated by photosynthetic energy transfer, avian magnetoreception, and enzyme catalysis. This paper argues that these biological precedents point to an unexplored class of room-temperature quantum architectures that could fundamentally transform artificial intelligence. Room-temperature quantum computing is not merely possible; it may be the only path to artificial general intelligence that captures the genuine computational richness of biological cognition. The current consensus, while defensible given gate-model limitations, mistakes one frozen form of quantum computing for all possible forms. Life shows us a different way: not isolating from noise, but structuring it into a computational ally. This roadmap presents: (1) the physical basis for room-temperature quantum coherence; (2) four candidate hardware architectures; (3) a five-phase research agenda spanning 2025-2040; (4) the theoretical foundations that explain why a warm quantum observer might be invisible to its own formalism; and (5) investment and policy recommendations. Throughout, we maintain a rigorous, skeptical stance: no appeals
At the dawn of the twentieth century, the underpinnings of centuries-old classical physics were beginning to be unravelled by the advent of quantum mechanics. As well as fundamentally shifting the way we understand the very nature of reality, this quantum revolution has subsequently shaped and created entire fields, paving the way for previously unimaginable technology. The quintessential instance of such technology is the quantum computer, whose building blocks - quantum bits, or qubits - are premised on the uniquely quantum principles of superposition and entanglement. It is predicted that quantum computers will be capable of efficiently solving certain classically intractable problems. To build a quantum computer, it is necessary to find a system which exhibits these uniquely quantum phenomena. The success of silicon-based integrated circuits for classical computing made semiconductors an obvious architecture in which to focus experimental quantum computing efforts. The two-dimensional electron gas which forms at the interface of GaAs/AlGaAs heterostructures constitutes an ideal platform for isolating and controlling single electrons, encoding quantum information in their spin and charge states. This thesis broadly addresses three key challenges to quantum computing with GaAs qubits: scalability, particularly in the context of readout, unwanted interactions between fragile quantum states and their environment, and the facilitation of controllable, strong interactions between separated qubits as a means of generating entanglement. These significant, unavoidable challenges must be addressed in order for a future solid-state quantum computer to be viable.
This paper proposes a novel computation architecture centered around the exploitation of quantum coherence. The core concept involves structuring a computational system to leverage the inherent parallelism afforded by quantum coherence, thereby aiming to significantly enhance computational efficiency compared to traditional architectures. We outline a framework where interconnected quantum systems operate in a manner that maximizes the duration of quantum coherence, allowing for parallel processing of multiple computational tasks simultaneously. The architecture is designed to mitigate decoherence effects, a primary challenge in quantum computing, through strategic system design and operational protocols. This approach represents a fundamentally new paradigm in computation, shifting from sequential, deterministic operations to a massively parallel, probabilistic one based on the quantum mechanical principles of superposition and coherence. The potential impact of this architecture extends across various fields, including scientific simulations, optimization problems, and machine learning, where the ability to process vast amounts of data concurrently offers a transformative advantage. ---
This paper proposes a novel quantum computer architecture design rooted in the principles of quantum biology. Traditional quantum computing architectures often struggle with decoherence and scalability, presenting significant hurdles to practical implementation. This work posits that quantum effects, previously observed and utilized within biological systems, offer a potentially more robust and efficient approach. Specifically, we explore how processes like quantum entanglement, quantum tunneling, and quantum coherence – frequently employed by biological organisms for tasks ranging from photosynthesis to bird navigation – can be harnessed within a computational framework. The core claim is that by directly mimicking and leveraging these biological mechanisms, we can design quantum computer architectures exhibiting improved fidelity, reduced error rates, and enhanced scalability. The architecture presented here incorporates several key elements, including a novel qubit design inspired by photosynthetic reaction centers and a control system modeled after biological feedback loops. Mathematical formulations are provided to illustrate the principles of operation and to quantify the potential improvements. The research presents a fundamental shift in perspective, transitioning from purely physics-based approaches to one that draws upon the remarkable efficiency and resilience found in nature's quantum systems.
Quantum computing promises an exponential speed-up of computation compared to what is nowadays achievable with classical computers. In this way, it enables the evaluation of more complex models and the breaching of current security algorithms. For the operation of a quantum system, many questions remain to be answered. Currently, there are several quantum technologies that promise to be both reliable and scalable, two features required for large scale quantum operations. Common to all technologies is the operating temperature that needs to be close to absolute zero, i.e. below 100 mK, to suppress environment noise and allow the quantum properties to become 'visible'. In order for any quantum processor to be operated, a so-called quantum-classical interface is required for the quantum bit (qubit) read-out and control. This interface consists of various electronic blocks, such as analog-to-digital converters, digital-to-analog converters, mixers, amplifiers and a digital controller. Especially the analog blocks require effort to meet the noise and stability constraints as not to disturb the very sensitive qubits and allow reading of the tiny signals. As the qubits live in extremely deep-cryogenic temperatures, long wires interface the cryogenic with the room temperature environment, where most of the electronics is situated. However, for a scalable system, heat injection becomes a serious problem, with many wires between 300 K and sub-Kelvin. Furthermore, such amount of interconnects is challenging to mechanically place in a dilution refrigerator. Therefore, in this work, we propose to implement the electronics not at room temperature, but at a temperature much closer to the qubits, for example at 4 K. This not only reduces significantly the wiring between room temperature and the qubits, but we can also benefit from lower electronic noise at such a temperature. The operation of conventional electronics almost 200°C below its normal temperature range is not trivial as device properties alter significantly and most circuits no longer operate as intended. In CMOS processes, the main technology in the integrated electronics world, the behaviour of the transistors, required for the implementation of any circuit, deviates considerably at low temperatures. The transistor's threshold voltage goes up, the mobility increases and the subthreshold slope becomes steeper, to name just a few of these deviations. Although there are improvements in performance, there are also some counter effects, and characterization of the transistors is needed to observe the changes. Once devices are characterized at such low temperatures, new models can be built and circuits can be simulated and adapted to operate properly at cryogenic temperatures. Luckily, there are various commercially available devices that can already withstand the chills of cold. We demonstrated various commercially available devices, such as a field-programmable gate array (FPGA) implemented in a 28 nm CMOS process, to be operating without major concerns at 4~K. Its properties alter only slightly, within 5 to 10%, and all tested circuit implementations, working at 300~K, also worked at 4 K. We combined both commercially available devices, that operate 200 K below their specified temperature range, with custom designed CMOS circuits to implement a cryogenic read-out platform for spin qubits. This system comprises amplifiers, an ADC, an FPGA, voltage regulators and a clock generator. It allows to amplify the tiny signal from the qubits and digitize it directly in the FPGA in order to process the data locally at 4~K. This system is one of the first systematic attempts at operating a part of the quantum-classical interface at cryogenic temperatures and forms the basis for future systems comprising the complete electronic interface for qubits to operate at such low temperatures. One of the main problems to tackle for cryogenic electronic systems is their power consumption. Power budgets are simply limited to roughly 1 or 2 Watts at 4 K and exponentially lower at deeper cryogenic temperatures, thus limiting the size of large-scale electronic systems. Our approach of combined commercial and custom circuits will have to be steadily replaced by a single (custom) technology that meets both power and scalability constraints. One of the best candidates is CMOS, a technology that the industry has relied upon for several decades and benefits from many optimizations thanks to Moore's law.
Context. Nature is the relation among processes and phenomena. Nothing exists in the universe without relations. Computer is transactions of relations between data with the help of control and execution mechanisms. Quantum relations are a superposition of particles and their states. Superposition and entanglement are equivalent concepts. Entanglement is a non-local superposition of deterministic states. A quantum computer is unconditional transactions of relations between qubit data. Quantum computer is an analog device for parallel solution of combinatorial problems. Practically oriented definitions of the quantum computer concepts are the path to development of scalable quantum parallel algorithms for combinatorial problems solving. Any algorithm can be reduced to a sequence of operations without conditions, because any truth table is a collection of a complete system of conditions-states. Any sequence of actions can always be reduced to one parallel operation. Conditions and sequences arise only when the developer wants to use previously created primitive constructs to build an always non-optimal computing unit. The paradigm of quantum computer creation is determined through the use of photonic transactions on the electrons of an atom may exclude the use of quantum logic. The evolutionary path of a quantum computer from the classical one: “memory-address-transaction” (MAT) → “electron-addresstransaction” → “electron-address-quantaction” (EAQ) → state-superposition-logic. The meeting point of classical and quantum computers is photon transactions on the structure of electrons. Everything that is calculated on a quantum computer can be calculated in parallel on a classical one on account of memory redundancy. The given example is a memory-driven algorithm for modeling digital products based on qubit-vector forms of functionality description for significant performance boost of computing processes by parallel execution of logical operations. Objective. Simulation of the correct SoC-component behavior based on vector representation of the logic. Formation of the triggering development of a computing based on the superposition of the classical, quantum and analog computing process, which in its development should be based on technological qubit, tabular and vector data structures for the parallel solution of combinatorial problems. Method. MAT-computing implements any algorithms on account of transactions (read-write) in memory. Qubit-vector models for describing functionalities, which differ from known truth tables in compactness of description and manufacturability for the implementation of parallel algorithms of the synthesis and analysis of digital devices and SoC-components. Results. 1) The metric of the technological data structures, focused on parallel troubleshooting in digital systems based on the usage of two logical vector operations, was proposed for the first time. 2) The metric of relations between the individual components of QC, allowing organizing a quantum deterministic computer, has been further developed. 3) Quantum architectural solutions, that allow solving coverage problems in a quasi-parallel mode, were proposed for the first time. 4) Architectural solutions based on an analog-to-digital computing, which can be used to solve the problems of the digital systems parallel analysis, have been further developed. 5) Vector-qubit structures of the logic data, that allow a quasi-parallel simulation of digital circuits, were proposed. Conclusions. Qubit models, quantum methods and combinatorial algorithms for technical diagnostics of digital devices have been implemented, which can significantly (up to 25%) reduce the time of test synthesis, deductive modeling of faulty and correct behavior, search for defective states by introducing an innovative idea of using qubit-vector data structures for describing logical components. Comparative assessments of qubit models and methods usage show an increase in the efficiency of algorithms for modeling digital devices compared to tabular ones. The superposition of a classical, quantum and analog computer is integrally represented, which allows to find the best solutions for recognition and decision making.