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Advances in parallel computing

Will We Ever Have a Quantum Computer?

M. I. Dyakonov

Published Jan 1, 2020• 1 citation
Publisher

Abstract

In the hypothetical quantum computing one replaces the classical twostate bit by a quantum element (qubit) with two basic states, ↑ and ↓.Its arbitrary state is described by the wave function ψ = a↑+ b↓, where a and b are complex amplitudes, satisfying the normalization condition.Unlike the classical bit, that can be only in one of the two states, ↑ or ↓, the qubit can be in a continuum of states defined by the quantum amplitudes a and b.The qubit is a continuous object.At a given moment, the state of a quantum computer with N qubits is characterized by 2 N quantum amplitudes, which are continuous variables restricted by the normalization condition only.Thus, the hypothetical quantum computer is an analog machine characterized by a super-astronomical number of continuous variables (even for N~100÷1000).Their values cannot be arbitrary, they must be under our control.Thus the answer to the question in title is: When physicists and engineers will learn to keep under control this number of continuous parameters, which means -never.

DOI
10.3233/apc200019

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In the hypothetical quantum computing one replaces the classical twostate bit by a quantum element (qubit) with two basic states, ↑ and ↓.Its arbitrary state is described by the wave function ψ = a↑+ b↓, where a and b are complex amplitudes, satisfying the normalization condition.Unlike the classical bit, that can be only in one of the two states, ↑ or ↓, the qubit can be in a continuum of states defined by the quantum amplitudes a and b.The qubit is a continuous object.At a given moment, the state of a quantum computer with N qubits is characterized by 2 N quantum amplitudes, which are continuous variables restricted by the normalization condition only.Thus, the hypothetical quantum computer is an analog machine characterized by a super-astronomical number of continuous variables (even for N~100÷1000).Their values cannot be arbitrary, they must be under our control.Thus the answer to the question in title is: When physicists and engineers will learn to keep under control this number of continuous parameters, which means -never.
The quantum computer is supposed to process information by applying unitary transformations to 2 N complex amplitudes defining the state of N qubits. A useful machine needing N~10 3 or more, the number of continuous parameters describing the state of a quantum computer at any given moment is at least 2 1000 ~10 300 which is much greater than the number of protons in the Universe. However, the theorists believe that the feasibility of large-scale quantum computing has been proved via the “threshold theorem”. Like for any theorem, the proof is based on a number of assumptions considered as axioms. However, in the physical world none of these assumptions can be fulfilled exactly. Any assumption can be only approached with some limited precision. So, the rather meaningless “error per qubit per gate” threshold must be supplemented by a list of the precisions with which all assumptions behind the threshold theorem should hold. Such a list still does not exist. The theory also seems to ignore the undesired free evolution of the quantum computer caused by the energy differences of quantum states entering any given superposition. Another important point is that the hypothetical quantum computer will be a system of 10 3 –10 6 qubits PLUS an extremely complex and monstrously sophisticated classical apparatus. This huge and strongly nonlinear system will generally exhibit instabilities and chaotic behavior.
In general, all forms of information processing can be considered in a quantum mechanical context, including for example communication, channel capacities, and the quantum limits to information extraction. In this chapter we introduce the basic features of quantum information processing. We first present qubits as abstractions of two-level addressable quantum systems, before generalizing this concept to higher-dimensional qudit systems, and continuous variables. We introduce the stabilizer formalism towards the beginning of our discussion as a convenient way to track quantum information. In Section 2.2 we derive the no-cloning theorem, which leads us to cryptography, teleportation, and quantum repeaters. Section 2.3 describes quantum computing with qubits. We start with a brief description of the circuit model, and use it to define cluster states and the one-way model. We study the properties of cluster states and discuss error correction. In the last section we introduce quantum computing over continuous variables. Quantum information Classically, information is carried by ‘bits’, which are physical systems that have two (macroscopic) states denoted by 0 and 1. These bits are described classically, which immediately raises the question of how we should extend this information-theoretic description to the quantum mechanical case. This will lead to the quantum unit of information, the quantum bit or ‘qubit’. 2.1.1 Classical and quantum bits We can define the qubit as a two-dimensional quantum system, which means that the state space of the system is a two-dimensional complex Hilbert space ℋ.
Introduction In an ordinary computer, information is stored in a collection of tiny circuits each of which is designed to have two stable and easily distinguishable configurations: each represents a bit . In our study of quantum cryptography, we have seen how it can be useful to express information not in ordinary bits but in qubits . Whereas a bit can have only two values, say 0 and 1, a qubit can be in any quantum superposition of ∣0〉 and ∣1〉. Moreover, a qubit can be entangled with other qubits. Thus one might wonder whether a quantum computer, in which the basic elements for storing and processing information are qubits, can outperform an ordinary (classical) computer in certain ways. This question was addressed by researchers starting in the 1980s. In terms of practical consequences, perhaps the most dramatic answer has been given by Peter Shor in his 1994 factoring algorithm for a quantum computer, an algorithm that is exponentially faster than any known classical algorithm. As we have seen in Chapter 1, the difficulty of factoring a product of two large primes is the basis of the security of the RSA cryptosystem. So if one could build a large enough quantum computer – and there is no reason in principle why this could not be done – the RSA system would be rendered ineffective. In this chapter we present the basics of quantum computation and then focus on Shor's factoring algorithm.
The first computer originated as an ordinary calculator in 19th century. Subsequently, the rapid evolution of computers began. The massive amount of processing power generated by computer manufacturers has always failed to quench the thirst for speed and computing capacity. If, as Moore's Law states, the number of transistors on a microprocessor continues to double every 18 months, then soon we will find the circuits on a microprocessor being measured on an atomic scale. Today's advanced lithographic techniques can squeeze fraction of micron wide logic gates and wires onto the surface of silicon chips. Thus it can be seen that very soon we will be facing the need to create quantum computers which can harness the power of atoms and molecules to perform memory and processing tasks. Quantum computers have the potential to perform calculations a billion times faster than any silicon-based computer. Also, theories suggest that every physical object, even the universe, is in some sense a quantum computer. If this is the case, then according to Turing's work which says that all computers are functionally equivalent; computers should be able to model every physical process. Scientists have already built basic quantum computers that can perform certain calculations; but a practical quantum computer is still years away. In this paper, we will be discussing about the history, development and the future scope of quantum computing. The pros and cons of this future technology have also been compared and our analysis has been put forth.
We propose a model for computing of a certain set of analytical functions based on estimating the output distribution of multiphoton outcomes in an optical scheme with an initial single-mode squeezed vacuum (SMSV) state and photonic states measuring the number of photons in one of the output modes of the beam splitter (BS) by photon number resolving (PNR) detector. The set of considered analytical functions is polynomial expressions including arbitrary derivatives of certain functions which can take on very large values even on small interval in their argument and small values of the parameter indicating the number of the subtracted photons. The large values that the analytic functions can take are offset by a very small term including the factorial of the number of subtracted photons, which guarantees an output normalized distribution of multiphoton measurement outcomes. The quantum computing algorithm makes it possible to find the values of the analytical functions for each number of extracted photons after a sufficiently large number of trials that would allow replacing the measurement repetition rate of multiphoton events by their probabilities. Changing the initial parameters (squeezing amplitude of the SMSV state and BS parameter) makes it possible to implement calculations of the functions over the entire (or, at least, significant) continuous interval of alteration in their argument. The potential of optical quantum computing based on nonclassical states of a certain parity can be expanded both by adding new optical elements such as BSs, and by using other continuous variable (CV) states of definite parity.