Project acronym QU-BOSS
Project QUantum advantage via non-linear BOSon Sampling
Researcher (PI) Fabio SCIARRINO
Host Institution (HI) UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA
Country Italy
Call Details Advanced Grant (AdG), PE2, ERC-2019-ADG
Summary After decades of progress in quantum information science, it is widely expected that in the next few years the field will start to yield practical applications in quantum chemistry, materials and pharmaceutical research, information security, and finance. For these applications to pan out, a crucial intermediate goal is to reach the quantum advantage regime, where quantum devices experimentally outperform classical computers in some computational task. The Boson Sampling problem is an example of a task that is computationally hard for classical computers, but which can be solved with a specialized quantum device using single photons interfering in a multimode linear interferometer. The aim of QU-BOSS is to experimentally push towards the quantum advantage regime with integrated photonic technology. The key innovative ingredient is the introduction of non-linearities acting at the single photon level embedded within the Boson Sampling interferometer. We plan to provide an experimental research breakthrough along three main directions, including both “hardware” and “software” components. First, we will use complementary approaches to map out how the addition of non-linearity boosts the device ́s complexity, making it harder to simulate classically. We will use different approaches to implement these devices with hybrid integrated quantum photonics, a versatile and flexible route to the manipulation of high-dimensional quantum photonic states. Finally, we will deploy the developed technology to implement two different architectures demonstrating quantum machine learning: a hybrid model of quantum computation and an optical quantum neural network. QU- BOSS aims to position integrated photonics into the NISQ (noisy, intermediate-scale quantum) era, opening up truly new scientific horizons at the frontier of quantum information, quantum control, machine learning and integrated photonics.
Summary
After decades of progress in quantum information science, it is widely expected that in the next few years the field will start to yield practical applications in quantum chemistry, materials and pharmaceutical research, information security, and finance. For these applications to pan out, a crucial intermediate goal is to reach the quantum advantage regime, where quantum devices experimentally outperform classical computers in some computational task. The Boson Sampling problem is an example of a task that is computationally hard for classical computers, but which can be solved with a specialized quantum device using single photons interfering in a multimode linear interferometer. The aim of QU-BOSS is to experimentally push towards the quantum advantage regime with integrated photonic technology. The key innovative ingredient is the introduction of non-linearities acting at the single photon level embedded within the Boson Sampling interferometer. We plan to provide an experimental research breakthrough along three main directions, including both “hardware” and “software” components. First, we will use complementary approaches to map out how the addition of non-linearity boosts the device ́s complexity, making it harder to simulate classically. We will use different approaches to implement these devices with hybrid integrated quantum photonics, a versatile and flexible route to the manipulation of high-dimensional quantum photonic states. Finally, we will deploy the developed technology to implement two different architectures demonstrating quantum machine learning: a hybrid model of quantum computation and an optical quantum neural network. QU- BOSS aims to position integrated photonics into the NISQ (noisy, intermediate-scale quantum) era, opening up truly new scientific horizons at the frontier of quantum information, quantum control, machine learning and integrated photonics.
Max ERC Funding
2 875 000 €
Duration
Start date: 2020-08-01, End date: 2025-07-31
Project acronym Smart-TURB
Project A Physics-Informed Machine-Learning Platform for Smart Lagrangian Harness and Control of TURBulence
Researcher (PI) LUCA BIFERALE
Host Institution (HI) UNIVERSITA DEGLI STUDI DI ROMA TOR VERGATA
Country Italy
Call Details Advanced Grant (AdG), PE8, ERC-2019-ADG
Summary Where is it difficult to control, predict and model a flowing system? to search and navigate inside it? to be prepared against extreme events? to tame them? It is in turbulent flows.
Turbulence is ubiquitous and unsolved from the point of view of out-of-equilibrium fundamental physics, uncontrollable from the engineering aspects, and a deadlock for brute-force numerical and experimental investigations. Indeed, progress by using conventional methods has been slow.
In this project, I propose to explore new avenues crossing the boundaries between Theoretical Engineering and Applied Physics using algorithms from Artificial Intelligence (AI) to study and control turbulence in an innovative way using smart Lagrangian objects in a vast array of flows. I am committed to: (i) develop original applications of AI algorithms to track and harness moving coherent structures and/or statistical turbulent fluctuations, (ii) optimise flow navigation of buoyant objects and active surface drifter, (iii) invent collective search protocols to locate emissions from fixed or floating sources, (iv) minimise turbulent dispersion of a swarm of autonomous underwater explorer and (v) perform new in-silico experiments for data-assimilation, to predict extreme-events, or to control turbulent fluctuations by novel Lagrangian injection/adsorption mechanisms.
The unifying fil-rouge of my project is to gain a Deep Understanding of turbulence by performing cutting-edge Lagrangian numerical studies. The project is both methodology oriented, with the grand challenge of developing fully unconventional applications of (Deep) Reinforcement Learning for fluid dynamics, and problem driven, delivering a series of specific optimal control strategies for important realistic flow set-ups and applications to the geophysical fields. With my experience and the impact of my contributions in the discipline, I am confident that I offer the highest chances to carry out this ambitious project with success.
Summary
Where is it difficult to control, predict and model a flowing system? to search and navigate inside it? to be prepared against extreme events? to tame them? It is in turbulent flows.
Turbulence is ubiquitous and unsolved from the point of view of out-of-equilibrium fundamental physics, uncontrollable from the engineering aspects, and a deadlock for brute-force numerical and experimental investigations. Indeed, progress by using conventional methods has been slow.
In this project, I propose to explore new avenues crossing the boundaries between Theoretical Engineering and Applied Physics using algorithms from Artificial Intelligence (AI) to study and control turbulence in an innovative way using smart Lagrangian objects in a vast array of flows. I am committed to: (i) develop original applications of AI algorithms to track and harness moving coherent structures and/or statistical turbulent fluctuations, (ii) optimise flow navigation of buoyant objects and active surface drifter, (iii) invent collective search protocols to locate emissions from fixed or floating sources, (iv) minimise turbulent dispersion of a swarm of autonomous underwater explorer and (v) perform new in-silico experiments for data-assimilation, to predict extreme-events, or to control turbulent fluctuations by novel Lagrangian injection/adsorption mechanisms.
The unifying fil-rouge of my project is to gain a Deep Understanding of turbulence by performing cutting-edge Lagrangian numerical studies. The project is both methodology oriented, with the grand challenge of developing fully unconventional applications of (Deep) Reinforcement Learning for fluid dynamics, and problem driven, delivering a series of specific optimal control strategies for important realistic flow set-ups and applications to the geophysical fields. With my experience and the impact of my contributions in the discipline, I am confident that I offer the highest chances to carry out this ambitious project with success.
Max ERC Funding
2 248 875 €
Duration
Start date: 2021-05-01, End date: 2026-04-30
