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International Journal of Modern Physics B

QUANTUM STEADY COMPUTATION

Giuseppe Castagnoli

Published Aug 10, 1991• 3 citations
Publisher

Abstract

Current conceptions of "quantum mechanical computers" inherit from conventional digital machines two apparently interacting features, machine imperfection and temporal development of the computational process. On account of machine imperfection, the process would become ideally reversible only in the limiting case of zero speed. Therefore the process is irreversible in practice and cannot be considered to be a fundamental quantum one. By giving up classical features and using a linear, reversible and non-sequential representation of the computational process — not realizable in classical machines — the process can be identified with the mathematical form of a quantum steady state. This form of steady quantum computation would seem to have an important bearing on the notion of cognition.

DOI
10.1142/s0217979291000870

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Current conceptions of "quantum mechanical computers" inherit from conventional digital machines two apparently interacting features, machine imperfection and temporal development of the computational process. On account of machine imperfection, the process would become ideally reversible only in the limiting case of zero speed. Therefore the process is irreversible in practice and cannot be considered to be a fundamental quantum one. By giving up classical features and using a linear, reversible and non-sequential representation of the computational process — not realizable in classical machines — the process can be identified with the mathematical form of a quantum steady state. This form of steady quantum computation would seem to have an important bearing on the notion of cognition.
In Part II of this book we show how computations can be implemented using quantum systems. As we will see, the differences between bits and qubits, briefly outlined in Part I, lead to some important consequences for quantum computing. We begin with a brief introduction to the fundamental principles underlying quantum computing: specific implementations will be considered in later chapters. Reversible computing While quantum computation in its modern form is still a relatively young discipline, researchers have been interested in the relationship between quantum mechanics and computing for a long time. Early workers were not interested in the ideas of quantum parallelism , which will be explored in the next section, but rather in the question of whether explicitly quantum mechanical systems could be used to implement classical computations. In addition to its intrinsic interest, there are two technological reasons why this might be considered an important question. The first reason is a direct consequence of Moore's laws. After the development of integrated circuits, computing technology began its headlong dash down the twin roads of ever-faster and ever-smaller devices. These two phenomena are closely related: as computing devices must communicate within themselves, and as the speed of information transfer cannot exceed the speed of light, faster computers must indeed be smaller. There is, however, a limit to this process, defined by the atomic scale: once the size of individual transistors becomes comparable with that of atoms, the old-fashioned approach of micro-electronics becomes completely untenable.
I provide an alternative way of seeing quantum computation. First, I describe an idealized classical problem solving machine that, thanks to a many body interaction, reversibly and nondeterministically produces the solution of the problem under the simultaneous influence of all the problem constraints. This requires a perfectly accurate, rigid, and reversible relation between the coordinates of the machine parts - the machine can be considered the many body generalization of another perfect machine, the bounching ball model of reversible computation. The mathematical description of the machine, as it is, is applicable to quantum problem solving, an extension of the quantum algorithms that comprises the physical representation of the problem-solution interdependence. The perfect relation between the coordinates of the machine parts is transferred to the populations of the reduced density operators of the parts of the computer register. The solution of the problem is reversibly and nondeterministically produced under the simultaneous influence of the state before measurement and the quantum principle. At the light of the present notion of simultaneous computation, the quantum speed up turns out to be "precognition" of the solution, namely the reduction of the initial ignorance of the solution due to backdating, to before running the algorithm, a time-symmetric part of the state vector reduction on the solution; as such, it is bounded by state vector reduction through an entropic inequality. PACS numbers: 03.67.Lx, 01.55.+b, 01.70.+w
Any computer can create a model of reality. The hypothesis that quantum computer can generate such a model designated as quantum, which coincides with the modeled reality, is discussed. Its reasons are the theorems about the absence of “hidden variables” in quantum mechanics. The quantum modeling requires the axiom of choice. The following conclusions are deduced from the hypothesis. A quantum model unlike a classical model can coincide with reality. Reality can be interpreted as a quantum computer. The physical processes represent computations of the quantum computer. Quantum information is the real fundament of the world. The conception of quantum computer unifies physics and mathematics and thus the material and the ideal world. Quantum computer is a non-Turing machine in principle. Any quantum computing can be interpreted as an infinite classical computational process of a Turing machine. Quantum computer introduces the notion of “actually infinite computational process”. The discussed hypothesis is consistent with all quantum mechanics. The conclusions address a form of neo-Pythagoreanism: Unifying the mathematical and physical, quantum computer is situated in an intermediate domain of their mutual transformation.
A computer is imagined in which all the parts are simple two-state objects obeying quantum mechanical laws. A time-independent Hamiltonian for such a system is written and analyzed. The computation is done in logically reversible steps. It is used to study possible limitations on computers due to fundamental physical laws. The ideal machine can run ballistically, coasting through the calculation in a short time TB. If there are imperfections due to irregularities in construction or finite temperatures the forward moving calculation is often reversed or scattered. External work must be done to drag the computer forward. The free energy lost in doing a calculation is kT In2 per logical step divided by turn factors. One is the mean free number of steps per scattering, measuring how perfectly the machine is built. The other is the mean time (divided by TB) that the calculation actually takes, which measures how gently you urge it forward.
Quantum reservoir computing exploits fixed quantum dynamics and a trainable linear readout to process temporal data, yet reversing the transformation -- reconstructing the input from the reservoir output -- has been considered intractable due to the recursive nonlinearity of sequential quantum state evolution. We introduce the quantum reservoir autoencoder, a four-equation encode--decode protocol with cross-key pairing, and constructively empirically demonstrate that satisfying reservoir--key combinations can be found using a full XYZ Hamiltonian reservoir (10~data qubits, feature dimension~76, 16~random Hamiltonian realizations). Under ideal conditions the mean-squared error (MSE) reaches ∼10−17{\sim}10^{-17}∼10−17 for data lengths up to 30; under shot noise (1\,000~shots) and depolarizing noise (p=0.005p = 0.005p=0.005), the MSE degrades to 10−310^{-3}10−3--10−110^{-1}10−1. Asymmetric resource allocation -- 10~shots for encoding, 10510^5105 for decoding -- yields a 102-fold MSE improvement (16~seeds ×\times× 3~trials). Comparison of single-body features (dimension~31) with the full feature set and six baselines identifies the iterative protocol structure -- not the feature dimension -- as the dominant noise bottleneck: baselines solving the linear system in a single step retain machine precision under identical noise, whereas per-iteration noise inconsistency in the coupled solver limits the MSE to ∼10−1{\sim}10^{-1}∼10−1. The current protocol requires plaintext access during decoder training, restricting practical deployment. These results establish a proof-of-concept for bidirectional information transformation within quantum reservoir computing and identify iterative noise mismatch and blind decryption as the principal open challenges.