Computer technology

Introduction to Quantum Computing

In 5G, cloud technologies have become an essential part of radio access network (RAN) management, for example our 5G vRAN solution. The ability to orchestrate both equipment and functions enables more automated and intelligent forms of network management, from network configuration to alarm management, license renewal, and more. Such tasks, for example, can now be performed more efficiently by intelligent agents that make optimized decisions in real time and under unpredictable circumstances.

However, these intelligent agents have to analyze the huge volume of data flows. The computing power required for such tasks exceeds the capabilities of the most cutting-edge devices available to us today. One solution comes in the form of quantum computing technology.

Quantum Computing vs Classical Computing

Built using a quantum processor, a quantum computer (QC) can potentially reduce runtime to hours and days for problems that previously would have taken hundreds of years to solve on our best supercomputers, also called classical computers. The basic element of a QC is the quantum bit (called a qubit). Quantum bits are the quantum analogue of classical bits and therefore the basic unit of quantum information. While in the classical realm they function as a two-level system, in this case qubits must obey the laws of quantum mechanics.

The building blocks of quantum mechanics

The birth of quantum mechanics took place in the first 27 years of the 20th century to overcome severe limitations in the validity of classical physics, the first inconsistency being Plank’s law of radiation. Einstein, Debye, Bohr, de Broglie, Compton, Heisenberg, Schrödinger, Dirac among others pioneered the development of the theory of quantum mechanics as we know it today.

The basic elements of quantum mechanics are:

  1. Quantification: energy, momentum, angular momentum and other physical quantities of a bound system are limited to discrete (quantized) values
  2. Wave-particle duality: objects are both waves and particles
  3. Heisenberg principle: the more accurately the position of a particle is determined, the less accurately its momentum can be known, and vice versa. There is therefore a fundamental limit to the precision of measurement of the physical quantities of a particle
  4. Overlay: two quantum states can be added, and the result is another valid quantum state
  5. Tangle: when the quantum state of a particle belonging to a system cannot be described independently of the state of other particles, even separated by a large distance, the particles are entangled
  6. Fragility: by measuring a quantum system we destroy all previous information. From this, it follows the non-cloning theorem which states: it is impossible to create an identical copy of an arbitrary unknown quantum state

Qubits and quantum computing technology

Since the first idea of ​​a quantum computer was proposed by Benioff, Manin, Feynman and Deutsch in the 1980s [benioff- Deutsch], many technological advances have taken place. We can now count on more than 10 technologies that industry and academia are studying with the aim of building a quantum computer. Some of them aim for a “universal” quantum computer, others for a specialized quantum machine that will speed up specific problems.

This is made possible by qubits. Qubits can be made with several technologies, for example, quantum dots are structures that can confine and manipulate a single electron to act like a qubit [loss, watson]. Another way of manipulating an electron’s spin is through the nitrogen vacancy centers in diamond. Transmon qubits are a type of superconducting qubits that use Josephson junctions to create a unique magnetic flux for use as a qubit [koch]. Ion-based qubits use ion traps to store qubits in individual ionized atoms suspended in vacuum and controlled using lasers and electric and magnetic fields [blatt]. Qubits can also be obtained by controlling photon polarization, or the number of photons. Unlike classical computing, qubits can be in 1, 0, or a superposition of 1 or 0 quantum states until we measure them. Following this logic, a pair of qubits can be in any four-state quantum superposition and three qubits will be in any 8-state superposition. By extrapolation, we can generalize that n qubits will be in a superposition of up to n different quantum states simultaneously. It is this property of simultaneity that leads us to a potential quantum advantage. If we can use this massive parallelism, we can compute multiple operations at the same time, thus inferring a timing advantage. Besides qubits, and to run a quantum computer, we need the full stack of quantum hardware and software layers (see Figure 1).

Figure 1. Quantum computer hardware and software stack

Quantum Algorithms vs Classical Algorithms

Residing at the hardware level, quantum gates will perform the mathematical operations dictated by the quantum algorithm in the Q abstraction layer. In between we have the specific implementation layers for quantum languages, compilers and also code error correction. As classical computers are distinct from quantum computers, so are algorithms. A quantum algorithm is different from a classical algorithm in that it is not entirely deterministic. A quantum algorithm is probabilistic, which means that the correct solution is associated with some known probability. However, if we have a QC that is a million times faster than a typical computer, we can always repeat the calculation multiple times and use voting to choose which one comes up most often. Due to this probabilistic nature, quantum variants of classical algorithms will need to be developed to run in a QC.

Most basic algorithms like Fourier transform and linear equations are needed for RAN user data plane functionality and algorithms like clustering, support vector machines and kernel methods, and parsing core components are required for RAN management plane functionality. However, to make these algorithms work in a CQ with specific quantum gates, we need a quantum version of the classical algorithm. To date, not all classical algorithms are described in the quantum world, and only a few of them have a quantum counterpart.

In a following article, we explore the quantum counterparts of some of the most basic algorithms used in the data and management planes of radio access networks. Visit our author pages to see this or other related blog posts.

Visit our Future Technologies pages to learn more about our latest work at Ericsson Research.

The references

  • Benioff, Paul, The Computer as a Physical System: A Microscopic Quantum Mechanical Hamiltonian Model of Computers Represented by Turing Machines, Journal of Statistical Physics. 22 (5) (1980)
  • Manine, Yu. I. . Vychislimoe and nevychislimoe [Computable and Noncomputable] (in Russian). Sov.Radio. p. 13-15. (1980)
  • Feynman, RPu, Simulating Physics with Computers, International Journal of Theoretical Physics. 21 (6): 467-488 (1982)
  • Deutsch, David, Quantum theory, the Church-Turing principle and the universal quantum computer, Proceedings of the Royal Society of London A. 400 (1818) (1985)
  • D. Loss and DP DiVincenzo, Quantum computing with quantum dots, Phys. Rev. A, Gen. Physics, vol. 57, no. 1, pages 120-126, 1998, doi:10.1103/PhysRevA.57.120.
  • TF Watson, SGJ Philips, E. Kawakami, DR Ward, P. Scarlino, M. Veldhorst, DE Savage, MG Lagally, M. Friesen, SN Coppersmith, MA Eriksson and LMK Vandersypen, A Programmable Two-Qubit Silicon Quantum Processor, Nature, vol. 555, no. 7698, p. 633–637, March 2018, doi: 10.1038/nature25766
  • J. Koch et al., “Charge-insensitive qubit design derived from the Cooper pair box”, Phys. Rev. A76, 04319 (2007)
  • R. Blatt; DJ Wineland “Entangled States of Trapped Atomic Ions” Nature. 453, 7198 (2008)