Traditional computing uses transistors and binary logic in order to perform computing operations. This approach has proved successful as long as the number of transistors per integrated circuit can be scaled to accommodate increasing speed and heat requirements. Unfortunately, this approach does not allow speeds above the limit where the size of the transistors approaches the limit of the integrated circuit substrates' molecular size. In this condition, electrons cannot reliably be contained within the boundaries separating one transistor from the next and increasingly small circuits become impractical. The current solution to this dilemma is to increase the number of functional units on an integrated circuit and thereby eliminate the need for increasingly small transistors. However whether we increase the number of functional units or the speed, there are inherent limitations in the number of transistors we can put onto one chip. We therefore look to another functional approach to generate the next generation of integrated circuits. We therefore look to using the molecules and elementary particles themselves as a means of computation. In order to accomplish molecular computation we need to modify the model of traditional computation from a transistor based binary method to analog based arbitrary basis model. This approach is not unknown since optical computation among other has used analog processing for highly parallel computation. The issue with molecular and quantum computing methods has traditionally been that understanding the state the computing function itself is often subject to high degree uncertainty. This uncertainty is not unexpected since the number of possible states that can exist when electromagnetic energy interrogates a collection of molecules, atoms, or electrons is quite large. In order to develop a method of representing these states we use a partially observable Markov decision process. We will then develop a finite state machine approach to computation based on a model for our molecular or quantum system.
Traditional computing uses transistors and binary logic in order to perform computing operations. This approach has proved successful as long as the number of transistors per integrated circuit can be scaled to accommodate increasing speed and heat requirements. Unfortunately, this approach does not allow speeds above the limit where the size of the transistors approaches the limit of the integrated circuit substrates' molecular size. In this condition, electrons cannot reliably be contained within the boundaries separating one transistor from the next and increasingly small circuits become impractical. The current solution to this dilemma is to increase the number of functional units on an integrated circuit and thereby eliminate the need for increasingly small transistors. However whether we increase the number of functional units or the speed, there are inherent limitations in the number of transistors we can put onto one chip. We therefore look to another functional approach to generate the next generation of integrated circuits. We therefore look to using the molecules and elementary particles themselves as a means of computation. In order to accomplish molecular computation we need to modify the model of traditional computation from a transistor based binary method to analog based arbitrary basis model. This approach is not unknown since optical computation among other has used analog processing for highly parallel computation. The issue with molecular and quantum computing methods has traditionally been that understanding the state the computing function itself is often subject to high degree uncertainty. This uncertainty is not unexpected since the number of possible states that can exist when electromagnetic energy interrogates a collection of molecules, atoms, or electrons is quite large. In order to develop a method of representing these states we use a partially observable Markov decision process. We will then develop a finite state machine approach to computation based on a model for our molecular or quantum system.
Q uantum mechanics has had an enormous technological and societal impact.To grasp this point, it is sufficient to cite the invention of the transistor, perhaps the most remarkable among the countless other applications of quantum mechanics.It is also easy to see the enormous impact of computers on everyday life.The importance of computers is such that it is appropriate to say that we are now living in the information age.This information revolution became possible thanks to the invention of the tran sistor, that is, thanks to the synergy between computer science and quantum physics.Today this synergy offers completely new opportunities and promises exciting advances in both funda mental science and technological application.We are referring here to the fact that quantum mechanics can be used to process and transmit information [1,2].Miniaturization provides us with an intuitive way of under standing why, in the near future, quantum laws will become important for computation.The electronics industry for com puters grows hand-in-hand with the decrease in size of integrated circuits.This miniaturization is necessary to increase computa tional power, that is, the number of floating-point operations per second (flops) a computer can perform.In the 1950's, electronic computers based on vacuum-tube technology were capable of performing approximately 103 floating-point operations per second, while nowadays there exist supercomputers whose power is greater than lOteraflops (1013 flops).As we have remarked, this enormous growth of computational power has been made possi ble owing to progress in m in ia tu riz a tio n , w hich m ay be quantified empirically in Moore's law.This law is the result of a remarkable observation made by Gordon Moore in 1965: the number of transistors on a single integrated-circuit chip doubles approximately every 18 24 months.This exponential growth has not yet saturated and Moore's law is still valid.At the present time the limit is approximately 108 transistors per chip and the typical size of circuit components is of the order of 100 nanometres.Extrapolating Moore's law, one would estimate that around the year 2020 we shall reach the atomic size for storing a single bit of inform atio n .At th a t p o in t, q u a n tu m effects will becom e unavoidably dominant.One should be aware that, besides quantum effects, other fac tors could bring Moore's law to an end.In the first place, there are economic considerations.Indeed, the cost of building fabri cation facilities to m an u factu re chips has also increased exponentially with time.Nevertheless, it is important to under stand the ultimate limitations set by quantum mechanics.Even though we might overcome economic barriers by means of tech nological breakthroughs, quantum physics sets fundamental limitations on the size of the circuit components.The first ques tion under debate is whether it would be more convenient to push References
Nature is the relations between processes and phenomena. Nothing exists in a Universe without relations. Computing is the transaction of relations between data using control and execution mechanisms. Quantum relations are a superposition of particles and their states. Quantum computing is unconditional transactions of relations between qubit data. Entanglement is a nonlocal superposition of deterministic states. A quantum computer is an analog device for solving combinatorial problems in parallel. Practically focused definitions of the concepts of quantum computing represent a path to the development of scalable quantum parallel algorithms for solving combinatorial problems. 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 uses previously created primitives-constructs to build an always non-optimal calculator. The deterministic paradigm of creating quantum computing by using photon transactions on the electrons of an atom can exclude the use of quantum logic. The evolutionary path of quantum computing from the classical one: Memory-Address-Transaction (MAT) → Electron-Address-Transaction →Electron-Address-Quantaction (EAQ)→State-Superposition- Logic. What is calculated on a quantum computer can be calculated in parallel on a classical computer due to the redundancy of memory. MAT-computing implements any algorithms through transactions (read-write) in memory. Qubit-vector models for describing functionalities differ from the known truth tables in terms of compactness of description and technological implementation of parallel algorithms for the synthesis and analysis of digital devices and SoC-components. An example is given – a memory-driven algorithm for simulating digital devices based on qubit-vector forms of describing functionalities for a significant increase in the productivity of computing processes by parallel execution of logical operations. Comparative evaluations of the use of qubit models and methods show an increase in the efficiency of algorithms for simulating digital devices in comparison with tabular ones. The superposition of classical, quantum and analog computing is integrally presented, which makes it possible to search the best solutions for recognition and decision making.
In terms of leveraging the total power of quantum computing, the prevalent current (2020) model of designing quantum computation devices to follow the von Neuman model of abstraction is highly unlikely to be making full use of the full range of computational assistance possible at the atomic and molecular level. This is particularly the case for molecular modeling, in using computational models that more directly leverage the quantum effects of one set of molecules to estimate the behavior of some other set of molecules would remove the bottleneck of insisting that modeling first be converted to the virtual binary or digital format of quantum von Neuman machines. It is argued that even though this possibility of “fighting molecular quantum dynamics with molecular quantum dynamics” was recognized by early quantum computing founders such as Yuri Manin and Richard Feynman, the idea was quickly overlooked in favor of the more computer-compatible model that later developed into qubits and qubit processing.
Analog computing using bosonic computational states is a leading approach to surpassing the computational speed and energy limitations of von Neumann architectures. But the challenges of manufacturing large-scale photonic integrated circuits (PIC) has led to hybrid solutions that integrate optical analog and electronic digital components. A notable example is the coherent Ising machine (CIM), that was primarily invented for solving quadratic binary optimization problems. In this paper, we focus on a mean-field interpretation of the dynamics of optical pulses in the CIM as solutions to Langevin dynamics, a stochastic differential equation (SDE) that plays a key role in non-convex optimization and generative AI. This interpretation establishes a computational framework for understanding the system's operation, the computational role of each component, and its performance, strengths, and limitations. We then infer that the CIM is inherently a continuous state machine, capable of integrating a broad range of SDEs, in particular for solving a continuous global (or mildly constrained) optimization problems. Nevertheless, we observe that the iterative digital-to-analog and analog-to-digital conversions within the protocol create a bottleneck for the low power and high speed of optics to shine. This observation underscores the need for major advances in PIC technologies as we envision that fully analog opto-electronic realizations of such experiments can open doors for broader applications, and orders of magnitude improvements in speed and energy consumption.
As computers become faster they must become smaller because of the finiteness of the speed of light. The history of computer technology has involved a sequence of changes from one type of physical realisation to another – from gears to relays to valves to transistors to integrated circuits and so on. The step to the molecular scale – the quantum level – will be next. Quantum theory is already important in the design of microelectronic components. But soon it will be necessary to harness quantum theory, rather than simply take it into account, to give components their functionality.
Molecules are serious candidates for building hardware for quantum computers. They can encode quantum information onto electron or nuclear spins and some of them show important features as the scalability of the number of qubits and a universal set of quantum gates. In this paper we present our advances in the development of a classical simulation infrastructure for molecular Quantum Computing: starting from the definition of simplified models taking into account the main physical features of each analyzed molecule, quantum gates are defined over these models, thus permitting to take into account the real behavior of each technology during the simulation. An interface with a hardware-agnostic description language has been also developed. The knowledge of the behavior of real systems permits to optimize the design of quantum circuits at both physical and compilation levels. Elementary quantum algorithms have been simulated on three different molecular technologies by changing the physical parameters of polarization and manipulation and quantum circuit design strategies. Results confirm the dependency of the fidelity of the results on both levels, thus proving that the choice of optimal operating points and circuit optimization techniques as virtual-Z gates are fundamental for ensuring the execution of quantum circuits with negligible errors.