Fundamental Physics
Informatics
14 Jan 2021

Quantum information processing

The concept of employing quantum mechanical systems to perform computations or optimisations has developed over the past 40 years from an initial principle to an eagerly anticipated technology. Researchers hope that by leveraging the phenomena of superposition and entanglement, the complexity of solving common difficult problems may be reduced. Currently, there are two main approaches to using quantum systems to solve information processing problems.

Near-term quantum computers will be accessed remotely to tackle specific subroutines. Credit: IBM
Near-term quantum computers will be accessed remotely to tackle specific subroutines. Credit: IBM

The first approach is to develop a quantum computer [1]. This is a device comprised of a number of logical qubits whose state can be prepared, manipulated, and read. A growing collection of algorithms have been found [2] that can run on the same, universal, quantum computer to tackle various problems. A notable example is Shor’s algorithm for factoring numbers, which outperforms the best classical methods with a superpolynomial speedup.

The second approach is to develop a quantum annealer, or adiabatic quantum computer [3]. Quantum annealers show potential to solve difficult optimisations by using qubits with tunable mutual interactions to produce Hamiltonians that encode a problem. By slowly changing the Hamiltonian of the system, an initial ground state moves to the ground state of the target potential and a solution to the problem is found.

Although these two approaches differ in objective, both need to overcome the obstacle of decoherence. All quantum information processing technologies require careful shielding of fragile quantum states whilst allowing for selective interaction between parts of the system, and eventually measurement. Overcoming decoherence represents an enormous engineering challenge, often incorporating low temperatures and precision fabrication.

Beyond the technical feasibility of these technologies, conceptual issues remain to be solved. Quantum information processing is often touted to provide applications in many areas, from material science, drug research, and machine learning. However, these devices will not generically speed up all computation [4]. The algorithms discovered so far address a small class of problems, leaving many classically difficult problems still practically intractable. Substantial work is needed to identify precisely how future quantum technologies could be used to tackle these real-world tasks [5].

Project Overview

The work of the ACT in this field focuses on quantum information processing technologies that can be applied to ESA's activities. Part of this effort entails reaching out to academia for ideas that represent major advancements, or novel approaches, to current established work.

Within the foreseeable future, quantum computers will be limited in their scale and fidelity to the extent that their use will be restricted to performing part of a larger classical computation. As such, we have great interest in using quantum devices to perform subroutines that are used in machine learning applications [6]. Parallel to these efforts, we look to find applications of quantum annealers. For example, in their use to solve quadratic unconstrained binary optimisation problems, such as air traffic management [7].

References:

  1. Michael A. Nielsen and Isaac L. Chuang. Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press, 2010.

  2. Quantum Algorithm Zoo.

  3. Tameem Albash and Daniel A. Lidar. Adiabatic quantum computation. Rev. Mod. Phys., 90:015002, Jan 2018.

  4. Scott Aaronson. Read the fine print. Nature Physics, 11(4):291–293, Apr 2015.

  5. Ashley Montanaro. Quantum algorithms: An overview, 2016. npj Quantum Inf 2, 15023 (2016).

  6. Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N. & Lloyd, S. Quantum machine learning. Nature 549, 195–202 (2017).

  7. T. Stollenwerk, B. O’Gorman, D. Venturelli, S. Mandra, O. Rodionova, H. Ng, B. Sridhar, E. G. Rieffel, and R. Biswas. Quantum annealing applied to de-conflicting optimal trajectories for air traffic management. IEEE Transactions on Intelligent Transportation Systems, 21(1):285–297, 2020.

Hamburger icon
Menu
Advanced Concepts Team