We relate quantum mechanics to the concept of an experimental trial, borrowed from statistics, in order to make clear that devices designed using quantum-mechanical equations require, in effect, management by a classical digital process-control computer (CPC). Energy dissipation essential for tolerance to component imperfections in classical digital computers is shown not to mix with quantum superposition expressed by the quantum dynamics of a Schrodinger equation. This precludes so-called quantum computers from producing super-linear speed-up at a scale large enough to be useful, and at the same clarifies a conceptual separation between digital computing and devices exhibiting quantum phase sensitivity. Quantum effects, such as random-number generation and photon correlations, are know to be useful and show further great potential for devices for computational and other purpose. Several such quantum-based devices are discussed that require or can be made more effective by bridging between these devices and CPCs that manage them. While the interdisciplinary promise of 'quantum computing' has faded, the area of quantum-to- classical bridging, once liberated from the misleading goal of super-linear speed-up, invites vigorous study and invention.
We relate quantum mechanics to the concept of an experimental trial, borrowed from statistics, in order to make clear that devices designed using quantum-mechanical equations require, in effect, management by a classical digital process-control computer (CPC). Energy dissipation essential for tolerance to component imperfections in classical digital computers is shown not to mix with quantum superposition expressed by the quantum dynamics of a Schrodinger equation. This precludes so-called quantum computers from producing super-linear speed-up at a scale large enough to be useful, and at the same clarifies a conceptual separation between digital computing and devices exhibiting quantum phase sensitivity. Quantum effects, such as random-number generation and photon correlations, are know to be useful and show further great potential for devices for computational and other purpose. Several such quantum-based devices are discussed that require or can be made more effective by bridging between these devices and CPCs that manage them. While the interdisciplinary promise of 'quantum computing' has faded, the area of quantum-to- classical bridging, once liberated from the misleading goal of super-linear speed-up, invites vigorous study and invention.
A mathematical model of computation is an idealized abstraction. We design algorithms and perform analysis on the assumption that the mathematical operations we specify will be carried out exactly, and without error. Physical devices that implement an abstract model of computation are imperfect and of limited precision. For example, when a digital circuit is implemented on a physical circuit board, unwanted electrical noise in the environment may cause components to behave differently than expected, and may cause voltage levels (bit-values) to change. These sources of error must be controlled or compensated for, or else the resulting loss of efficiency may reduce the power of the information-processing device. If individual steps in a computation succeed with probability p, then a computation involving t sequential steps will have a success probability that decreases exponentially as pt. Although it may be impossible to eliminate the sources of errors, we can devise schemes to allow us to recover from errors using a reasonable amount of additional resources. Many classical digital computing devices use error-correcting codes to perform detection of and recovery from errors. The theory of error-correcting codes is itself a mathematical abstraction, but it is one that explicitly accounts for errors introduced by the imperfection and imprecision of realistic devices. This theory has proven extremely effective in allowing engineers to build computing devices that are resilient against errors. Quantum computers are more susceptible to errors than classical digital computers, because quantum mechanical systems are more delicate and more difficult to control. If large-scale quantum computers are to be possible, a theory of quantum error correction is needed. The discovery of quantum error correction has given researchers confidence that realistic large-scale quantum computing devices can be built despite the presence of errors. We begin by considering fundamental concepts for error correction in a classical setting. We will focus on three of these concepts: (a) the characterization of the error model, (b) the introduction of redundancy through encoding, and (c) an error recovery procedure. We will later see that these concepts generalize quite naturally for quantum error correction.
In this chapter, the authors discuss a part in an effort to analyze the physical limitations of computers due to the laws of physics. It is a result of computer science that a universal computer can be made by a suitably complex network of interconnected primitive elements. There are, however, a number of questions that the people would like to discuss in more detail such as the question of imperfections. It is analogous to the use of transistors in conventional machines where the people don't properly use all the analog continuum of the behavior of transistors, but just try to run them as saturated on or off digital devices so the logical analysis of the system behavior is easier. Of course the large system obeys quantum mechanics, but it is in interaction with the heat baths and other things that could make it effectively irreversible.
This report explores the profound and symbiotic relationship between quantum technology and classical electronics, highlighting their convergence as a transformative force in modern engineering and computation. It details how classical electronic systems are indispensable for the control, measurement, and operation of nascent quantum technologies—including quantum computing, communication, sensing, and simulation—particularly through advanced cryogenic and interface architectures. Concurrently, the inherent scaling limitations of classical electronics at the nano scale are driving innovation inspired by quantum mechanical principles, leading to the development of next-generation devices and the exploration of exotic quantum materials. The analysis addresses significant technical challenges such as noise, scalability, latency, and thermal management, alongside the immense economic and strategic opportunities presented by this frontier. Ultimately, the report projects a future defined by seamless quantum-classical hybrid systems, underscoring that the advancement of quantum technology is deeply reliant on breakthroughs in classical electronics, physics, and computer science. Quantum technology represents a transformative frontier that bridges fundamental physics, advanced electronics, and cutting-edge computer science. Rooted in the principles of quantum mechanics—such as superposition, entanglement, and tunneling—quantum technologies are reshaping how information is processed, transmitted, and measured. The interplay between physics provides the theoretical foundation, while electronics enables precise control and manipulation of quantum systems through devices like qubits and quantum sensors. Meanwhile, computer science contributes with algorithms, error correction, and software frameworks essential for quantum computation and communication. This interdisciplinary synergy is driving breakthroughs in quantum computing, quantum cryptography, and quantum sensing, promising profound impacts on industries ranging from cybersecurity to materials science and artificial intelligence.
The design and operation of a quantum-mechanical device as a laboratory instrument puts models written in equations of quantum mechanics in contact with instruments. This contact is recordable in files of a Classical Digital Process-control Computer (CPC) used both to calculate with the equations and to manage the instruments. By noticing that equations and instruments make contact in a CPC, we rewrite equations of quantum mechanics to explicitly include functions of CPC-commands to the instruments. This sets up a proof that a scientist's choice in linking mathematical models to instruments is unresolvable without guesswork to narrow the set of models from which one is to be chosen. As for implications of the proof, scientists inherit choices from the past and frame choices for the future, choices open to guesswork and visible in CPC files. To picture these choices, we adapt colored Petri nets, and the availability of these net fragments makes choice and guesswork part and parcel of physics. Net fragments as a means of expressing guess-demanding choices are applied to portray guesswork needed in testing and calibrating a quantum computer. The sample size required to test a quantum gate in a quantum computer is shown to grow as the inverse square of the error allowed in implementing the gate.
Quantum computing promises computational performance that is exponentially faster than any conceivable classical computer. This is due to the theoretically expected scaling of N entangled qubits, with parallel evolution of 2^N quantum states. This is in sharp contrast to classical computing, where N bits may have 2^N classical states, but only one at a time. It is widely believed that quantum superposition and entanglement have been demonstrated in several experimental systems, and that practical quantum computing can be achieved once sufficiently long quantum relaxation times are obtained. On the contrary, we suggest that there may be serious problems with quantum computing on both the macroscopic and microscopic levels, and that the experiments thus far have not proven the existence of non-classical superposition states, which are necessary for the proper functioning of qubits. In order to investigate this further, we propose new experiments in three physical systems: electron spins, single photons, and superconducting loops. We further suggest that certain more limited classes of quantum computing, such as quantum annealing, do not require quantum entanglement, and can achieve significant performance enhancements even if universal quantum computing proves to be impossible.