P R O J E C T   H I P H O P
HiPhoP QuantERA

Title: High dimensional quantum Photonic Platform
Duration: 01/04/2018 - 30/03/2021
Principal Investigator: Pascale Senellart (CNRS, France)

Project Annotation: Develop near-optimal single-photon sources based on semiconductor quantum dots, and couple them to highly reconfigurable 3D photonic glass chips to implement multi-photon multi-mode quantum walks. As a first benchmark, we will demonstrate quantum advantage (or supremacy) through high photon-number Boson sampling measurements. The platform will then be used to demonstrate secure quantum computation (homomorphic encryption) and quantum communication (quantum enigma machine) tasks on chip. A new advanced metrology task will be proposed and demonstrated, with simultaneous multi-parameter estimation.

Project Partners:
- Pascale Senellart (CNRS-Center For Nanoscience and Nanotechnology, Marcoussis, France)
- Philip Walther (Vienna University, Austria)
- Roberto Osellame (CNR, Italy) and Fabio Sciarrino (University of Roma)
- Ian Walmsley (University of Oxford, United Kingdom)
- Mario Ziman (Institute of Physics, Slovak Academy of Sciences, Slovakia)

Project objectives:
WP1: Quantum photonic toolbox (WP leader: CNRS)
- T1.1: High brightness quantum dot sources of indistinguishable single photons (CNRS)
- T1.2: Fiber-based timed encoding quantum walk platform (UOXF)
- T1.3: Temporal to spatial photon demultiplexing (CNRS-CNR)
- T1.4: Complex and reconfigurable glass photonic chips (CNR-UNIVIE)
- T1.5: Reproducible source technology: toward multiple source inputs (CNRS)
- T1.6: Fast on chip optical switches for on-chip photon routing (CNR)
WP2: Quantum advantage certified by machine learning (WP leader: CNR)
- T2.1: High dimensional time-encoded Boson sampling (UOXF, CNRS)
- T2.2: High dimensional path encoded Boson sampling (CNR, UOXF, CNRS)
- T2.3: Certification of Boson Sampling via machine learning (CNR, IPSAS)
- T2.4: Quantifying the state as a resource for quantum information (IPSAS, CNR)
WP3: Secure quantum computing on a chip (WP leader: UNIVIE)
- T3.1: Path- and polarization-encoding on a chip (CNR, UNIVIE)
- T3.2: Quantum walk based quantum enigma machine (CNR, CNRS)
- T3.3: Processing of encrypted data on a chip (CNR, UNIVIE, CNRS, IPSAS)
WP4: Quantum enhanced quantum metrology (WP leader: IPSAS)
- T4.1: Machine-learning based quantum phase estimation (CNR, CNRS, IPSAS)
- T4.2: Multiparameter estimation on a chip (CNR, IPSAS)

Involved Researchers: Daniel Nagaj, Michal Sedlák, Mário Ziman,