Quantum computing

HDCRS IEEE GRSS Working Group: Research Topics

Quantum computing

Quantum computing is considered as a next-generation information processing technology. It is a computing paradigm that exploits explicitly quantum mechanical properties (superposition, entanglement, interference…) of matter in order to do calculations. The basic element of a quantum computing system is a quantum bit, often called a qubit. Over the last few decades, considerable progress has been made toward realizing quantum computing systems by physically implementing a qubit in various systems.

There are two main approaches to quantum computing. The first generates the desired result by initializing the state of a quantum system and then using direct control of the Hamiltonian to evolve the quantum state in a way that has a high probability of answering the question of interest. In these systems, the Hamiltonian is often smoothly changed, so the quantum operations are truly analog in nature and cannot be fully error corrected, and can be referred to as “analog quantum computing”. This approach includes adiabatic quantum computing (AQC), quantum annealing (QA), and direct quantum simulation. The second approach, called “gate-based quantum computing” is similar to today’s classical approaches, in that the problem is broken down into a sequence of a few very basic “primitive operations”, or gates, which have well-defined “digital” measurement outcomes for certain input states.

Physical qubits roadmap for quantum computers

Online Courses and Tutorials


Elias F. Combarro, Introductory Lectures on Quantum Computing, Universidad de Oviedo / CERN openlab.

Chris Ferrie, Introduction to Quantum Computing, UTS Centre for Quantum Software and Information.


Dan Boneh and Will Zeng, CS269: Quantum Computer Programming, Stanford Computer Science.

Workshop: Quantum Processing of Big Data: from Quantum Computing to Earth Observation, Phi Lab, ESA.

Access to Computing Resources

Leap D-Wave Systems

Leap is the quantum cloud service from D-Wave Systems Inc. It is possible to get a free minute of direct Quantum Computing (QC) access time, which is enough to run between 400 and 4000 problems. Alternatively, users can obtain 20 minutes of free access to Leap’s quantum-classical hybrid solvers, which exploit the complementary strengths of both best-in-class classical algorithms and quantum resources.

IBM Quantum Experience

The IBM Quantum Experience is an online platform that gives users in the general public access to a set of IBM’s prototype quantum processors via the cloud. It also includes a set of tutorials on quantum computation, and access to an interactive textbook.

Podcasts and Videos


Lex Fridman Podcast #72, Scott Aaronson: Quantum Computing.

Applications in Remote Sensing

List of publications that participate to the competition organized by HDRCS. The winners provide also a video.

The list of recommended



B. Zygelman, ”A First Introduction to Quantum Computing and Information”, Springer, 2018.

M. Schuld and F. Petruccione, ”Supervised Learning with Quantum Computers”, Springer, 2018.

M. A. Nielsen and I. L. Chuang, ”Quantum Computation and Quantum Information: 10th Anniversary Edition”, Cambridge University Press, 2011.

N. S. Yanofsky and M. A. Mannucci, ”Quantum Computing for Computer Scientists”, Quantum Computing for Computer Scientists, 2008.

M. A. Nielsen and I. L. Chuang, ”Quantum Computation and Quantum Information”, Quantum computation and quantum information, 2000.

A. Yu. Kitaev, A. H. Shen and M. N. Vyalyi, ‘‘Classical and Quantum Computation”, American Mathematical Society, 2002.

Journal papers

S. Otgonbaatar and M. Datcu, ”Quantum Annealer for Subset Feature Selection and the Classification of Hyperspectral Images”, IEEE JSTARS, 2021.

N. Liu, T. Huang, J. Gao, Z. Xu, D. Wang and F. Li, “Quantum-Enhanced Deep Learning-Based Lithology Interpretation From Well Logs,” IEEE TGRS, 2021

Conference papers

D. Zaidenberg, A. Sebastianelli, D. Spiller, B. Le Saux, S. L. Ullo, Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing, IEEE IGARSS, 2021 (press).

A. Delilbasic,  G. Cavallaro, M. Willsch, F. Melgani, M. Riedel, K. Michielsen, Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification, IEEE IGARSS, 2021 (press).

M. Riedel, G. Cavallaro, J. A. Benediktsson,  Practice and Experience in using Parallel and Scalable Machine Learning in Remote Sensing from HPC over Cloud to Quantum Computing, IEEE IGARSS 2021, (press).

F. V. Pepe, C. Abbattista, L. Amoruso, M. D’Angelo, Quantum Imaging for Remote Sensing and Earth Observation and Remote Sensing, IEEE IGARSS 2021 (press)

G. Cavallaro, D. Willsch, M. Willsch, K. Michielsen and M. Riedel, ”Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-WAVE Quantum Annealer” IEEE IGARSS, 2020.