Optical quantum computing

The course is not on the list Without time-table

Code	Completion	Credits	Range	Language
QNI-OQC	Z,ZK	5	2P+1C	English

Course guarantor:

Aurél Gábor Gábris

Lecturer:

Aurél Gábor Gábris

Tutor:

Aurél Gábor Gábris

Supervisor:

Department of Applied Mathematics

Synopsis:

The course covers the basic theoretical methods and concepts for optical quantum computing, complemented by on hands-on exercise and applications using quantum programming libraries, Strawberry Fields and Piquasso. Theoretical concepts include measurement-based quantum computation, Gaussian Boson Sampling, and quantum supremacy. Applications feasible on current and near-term hardware include recent generative and discriminative machine-learning algorithms, as well as molecular vibration simulations.

Requirements:

Syllabus of lectures:

1. Quantum states of light: Fock states, Gaussian states (vacuum, coherent, squeezed, thermal); Representation of states in quantum optics: Fock state, Wigner quasi-probability distribution; Measurements: single photon (ideal, threshold), homodyne.

2. Quantum operations on light: Displacement, free evolution, squeezing, beam-splitter, cross-Kerr effect.

3.Encoding: single photon, dual-rail, coherent state, GottesmanKitaevPreskill qubits; Continuous variable (CV) quantum computing: qumodes.

4. Photonic quantum computing hardware: coherent states, spontaneous parametric down conversion, single photons; linear and non-linear operations.

5. Quantum operations with current technology: linear optics, effective non-linear operation (KnillLaflammeMilburn scheme), Gaussian and non-Gaussian states and operations.

6. Measurement-based quantum computing (MBQC): gate teleportation, adaptive measurements and feed forward.

7. Cluster state MBQC, GreenbergerHorneZeilinger states.

8. Boson Sampling: permanent, feasibility with current hardware; Gaussian Boson Sampling (GBS): Hafnian, quantum supremacy.

9. Gaussian Boson Sampling using threshold detectors, Torontonian, classical simulability.

10. Graph similarity: graph encoding, measurement coarse-graining, matching polynomial.

11. Universal quantum gates and quantum neural networks in CVQC.

12. Current hardware: time multiplexing architecture.

13.Simulations of molecular vibrations: vibrational dynamics, time evolution.

Syllabus of tutorials:

1. First program in Strawberry Fields: state creation, measurement, visualization

2. Quantum circuits: syntax and examples (e.g. quantum teleportation)

3. Grover search with GKP qubits

4. Realistic photon sources, post selection

5. Beam-splitter networks, resource states for KLM

6. Teleportation-based quantum computing: examples

7. Cluster state MBQC examples: Grover search

8. First program in Piquasso: Boson Sampling

9. Gaussian Boson Sampling: Torontonian computation on FPGA accelerated hardware

10. Graph similarity algorithm applications

11. CV variational circuit applications

12. Hardware-level implementation of GBS (time multiplexing)

13. Molecule vibration dynamics simulations

Study Objective:

Study materials:

1. Krovi, H.: Models of optical quantum computing,

Nanophotonics 2017, https://doi.org/10.1515/nanoph-2016-0136

2. Asavanant, W., Furusawa, A.: Optical Quantum Computers: A Route to Practical Continuous Variable Quantum Information Processing

AIP Publishing LLC 2022, ISBN 978-0-7354-2407-4

Note:

Information about the course and teaching materials can be found at https://courses.fit.cvut.cz/QNI-OQC

Further information:

https://courses.fit.cvut.cz/QNI-OQC

No time-table has been prepared for this course

The course is a part of the following study plans:

Quantum Informatics (compulsory elective course)