078 Quantum Computing and QC-Assisted Communications

1. Fundamentals of Quantum Communications

Why Quantum Computing? The ever-reducing transistor size following Moore’s law is approaching the point, where the so-called quantum effects become prevalent in the transistors’ operation. This specific trend implies that quantum effects become unavoidable, hence rendering the research of quantum computation (QC) systems an urgent necessity. In fact, a quantum annealing chipset is already commercially available from D-Wave1.

Apart from the quantum annealing architecture, the so-called gate-based architecture, which relies on building computational blocks using quantum gates in a similar fashion to classical logic gates, is attracting increasing attention due to the recent advances in quantum stabilizer codes, which are capable of mitigating the de-coherence effects encountered by quantum circuits. In terms of implementation, D-Wave’s most recent model, namely D-Wave 2000Q, has a total of 2000 qubits, while IBM Q Experience, which relies on the gated-based architecture, has currently only 20 qubits in total. However, IBM has recently announced their plans for delivering a 50 qubit gate-based quantum computer by 2020. Once quantum computing becomes a commercial reality, it may be used in wireless communications systems in order to speed up specific processes due to its inherent parallelization capabilities.
          While a classical bit may adopt either the values 0 or 1, a quantum bit, or qubit, may have the values |0>, |1>, or any superposition of the two, where the notation |·> is the column vector of a quantum state. If two qubits are used, then the composite quantum state may have the values |00>, |01>, |10> and |11> simultaneously. In general, by employing b bits in a classical register, one out of 2b combinations is represented at any time. By contrast, in a quantum register associated with b qubits, the composite quantum state may be found in a superposition of all 2b values simultaneously. Therefore, by applying a quantum operation to the quantum register would result in altering all 2b values at the same time. This represents the parallel processing capability of quantum computing.
             In addition to superior computing capabilities, multiple quantum algorithms have been proposed, which are capable of outperforming their classical counterparts in the same categories of problems, by either requiring fewer computational steps, or by finding a better solution to the specific problem. In this course, we will focus our attention on the employment of quantum algorithms in classical communication systems, which is nowadays referred to as quantum-assisted communications.

Qubit system
Geometrical (2-D, 3D) representation of the Quantum States
Discrete Variable (DV)/Continuous Variable (CV) Systems
Quantum Gates, Tensor Computing
Quantum Algorithms
Grover's Quantum Search Algorithm (QSA),
Dürr-Høyer QSA
Boyer-Brassard-Hoyer-Tapp QSA
Shor's Algorithm.

Applications of Quantum Communications
Quantum Channel Capacity.
Potential Enablers For Quantum Communications
                      Quantum Entanglement
                      Quantum-Dot Cellular Automata
                      Quantum Hardware Capacity
                      Quantum Key Distribution
                      Quantum Decision Theory
                      Quantum Game Theory
                      Quantum-Proof Randomness Extractors.
QC-Assisted Communications
                      Quantum-Assisted Multi-User Detection
                      Quantum-Aided Multi-User Transmission
                      Quantum-Assisted Indoor Localization for mmWave and VLC
                      Quantum-Assisted Joint Routing and Load Balancing
                      Quantum-Assisted Channel Estimation and Detection.

2. Fundamentals of Quantum Machine Learning
                   This section revisits the ML methods in the context of quantum-assisted algorithms for ML and the QML framework. Quantum principles based on emerging computing technologies will bring entirely new modes of information processing. An overview of supervised, unsupervised and reinforcement learning methods for QML is discussed in this segment of the course. Special attention will be paid to Spiking Neural Networks motivated by the research results in neuroscience, promising several orders of magnitude energy savings and their Q-equivalent. Life Long Learning concept will be also covered, solving the problem opf algorithm reinitialization for every new learning task.

1) Overview of Quantum Learning Methods
                      a: Supervised And Unsupervised QML
                      b: Quantum Reinforcement Learning
2) Generative and Discriminative QML Models
3) Quantum SVMs and ANNs
     (support vector machine and artificial neural networks)
4) Perceptron
5) Spiking Neural Networks
6) Life Long Learning

3. Quantum Networks (and 6G/7G)

Quantum Network Protocols
Summary of the analytical tools
Quantum Link Layer Protocol
Reinforcement Learning Based QN Protocols
QN STABILITY
Dynamic QN PROTOCOLS
Q-Internet
Q-Satellite networks
Elementary Link Generation with Satellites
Implementation Aspects of cv Satellite QN
CV QUANTUM KEY DISTRIBUTION
Fundamentals of CVQKD
Security of CVQKD protocols
Composable security proof for cv QKD
QKD OVER SUBOPTICAL BANDS:

QUANTUM vs POST QUANTUM SECURITY
POST‐QUANTUM CRYPTOGRAPHY
Overview of Post-Quantum Cryptosystems
QUANTUM CRYPTOGRAPHY
Discrete Variable Protocols
Continuous‐Variable QKD
One‐way CV‐QKD protocols
Two‐way CV‐QKD protocols
Theoretical Models of Security

IMPLEMENTATION EXAMPLES OF cv QKD
qubit PHYSICS
Superconducting Qubits
Qubit gates using the spin states of coupled single-electron quantum dots
Quantum Logic by Polarizing Beam Splitters
Quantum Gates Implemented by Trapped Ions