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Integrated photonic platformfor quantum machine learning
Nicolò Spagnolo,Dipartimento di Fisica,
Sapienza Università di Roma
www.quantumlab.it
Sapienza
Alessia SupranoDavide PoderiniMauro ValeriEmanuele PolinoIris AgrestiTaira GiordaniGonzalo CarvachoFulvio FlaminiNicolò SpagnoloFabio Sciarrino
IFN-CNR
Simone AtzeniGiacomo CorrielliAndrea CrespiRoberto Osellame
The team
Quantum Information: exploiting the laws of Quantum Mechanics to boostmanipulation of information
QuantumComputation
QuantumInformation
QuantumCommunication
Quantum Sensing
Quantum Simulation
Foundations of Quantum
Mechanics
Quantum Information and Machine Learning
Novel approach: to combine computational boost provided by quantummechanics with machine learning
Different degrees of freedom: Polarization
Optical path
Orbital Angular Momentum
.......
Quantum correlationsMain ingredients:
Quantum interference
Properties of photonic systems
Long propagation distances
Quantum coherence can be protected for long times
Low interaction with the environment
Quantum Information with photons
single-particle
multiparticle
Bulk Optics
Hybrid systems and different degrees of freedom
Integrated photonics
Photonic platforms
Integrated photonics
Photonic platforms
Permanent and localized index of refraction increase in
transparent media
translation of the sample at constant velocity with respect
to the laser beam
Integrated circuits Stable, controllable and repeatable operations
3D-capabilities
Polarizationinsensitive devices
+Manipulatingpolarization
Reconfigurable circuits
Femtosecond laser writing
Implementation of arbitrary linear unitaries
M. Reck et al., Phys. Rev. Lett. 73, 58 (1994), A. Crespi, et al., Nature Photonics 7, 545 (2013), L. Sansoni et al., Phys. Rev. Lett. 108, 010502 (2012); G. Corrielli et al., Nature Communications 5, 4249 (2014).
Polarization-insensitive circuits Waveplates for polarization manipulation
Arbitrary circuits with Femtosecond laser writing
Integrated tritter On chip quantum maze
Fast Fourier transform Sylvester interferometers
F. Caruso et al., Nat. Comm. 7, 11682 (2016)N. Spagnolo, et al., Nat. Comm. 4, 1606 (2013);
A. Crespi, et al., Nat. Comm. 7, 10469 (2016) N. Viggianiello et al., New J. Phys. 20, 033017 (2018)
3D-devices
Reconfigurable interferometersF. Flamini, et al. Light Sci. Appl. 4, e354 (2015)
Reconfigurableinterferometers
Dynamical optical phases
Thermo-optic effect
Change of phase due to heat dissipation in a resistor
Reconfigurable interferometers
Programmable simulator Integrated source of entangled pairsI. Pitsios, et al., Nat. Comm. 8, 1569 (2017)
F. Flamini, et al. Light Sci. Appl. 4, e354 (2015)
S. Atzeni et al., Optica 5, 311(2018)
Reconfigurableinterferometers
On chip quantum contextuality
A. Crespi et al., ACS photonics 4, 2807 (2017)
E. Polino et al. Optica 6, 288 (2019)
Advanced tool for optimization, manipulation and analysis of quantum optical systems
MACHINE LEARNING FOR CHARACTERIZATION OF QUANTUM DEVICES AND QUANTUM CERTIFICATION
Find hidden pattern in complex data
General tool for certification of a complex multi-mode, multi-photon interferometer
Pattern recognition
t-distributed stochasticneighbor embedding
Machine learningfor quantum physics
N. Spagnolo, et al., Sci. Rep. 7, 14316 (2017)
Characterization of large m unitarytransformations via a genetic algorithm
I. Agresti, et al., Phys. Rev. X 9, 011013 (2019).F. Flamini, et al., Quantum Sci. Technol. 4, 024008 (2019).
QUANTUM STATE ENGINEERINGVIA QUANTUM WALKS
Machine learningfor quantum physics
Online Bayesian
MACHINE LEARNING FOR ADAPTIVE PHASE ESTIMATION
Optimal information extraction
Particle Swarmoptimization
multiparameterscenario
ProbeInterferometer
Detection
LEARNING OF QUANTUM STATES
A. Lumino, et al., Phys. Rev. Appl. 10, 044033 (2018)E. Polino, et al., Optica 6, 288 (2019).
L. Innocenti, et al., Phys. Rev. A 96, 062326 (2017)T. Giordani, et al., Phys. Rev. Lett. 122, 020503 (2019).
A. Rocchetto, et al., Sci. Adv. 5, eaau1946 (2019).
Towards Quantum Machine Learning
From Machine Learning for Quantum to Quantum Machine Learning
Two different approaches
ARCHITECTURES FOR QMLQUANTUM BLOCKS
Quantum subroutines in hybrid algorithmsto tackle hard part of the computation
quantum
Data
quantum
Machine learning
classical
Data
quantum
Machine learning
classical classical quantum
input output
Quantum subroutines for complex algorithms
Optimization problems
Building block in hybrid system for numerical optimization requiringsampling from hard distributions
J. M. Arrazola, et al., Phys. Rev. A 98, 012322 (2018)
Uniformly drawnn bosons,m modes
Example: Boson Sampling
Hard to solve with classical hardware
Can be tackled with a dedicated quantum device
Connection to graph theory
Calculation of NP-hard quantities in graph theory
J. M. Arrazola, et al., Phys. Rev. Lett. 121, 030503 (2018)
classical classical quantum
input output
A. Crespi, et al., Nature Photonics 7, 545 (2013); N. Spagnolo, et al., Nature Photonics 8, 615 (2014);M. Bentivegna, et al., Sci. Adv. 1, e1400255 (2015).
Quantum Reservoir computing
Network
Fixedlinks
Loopfeedback
Source
ReservoirDetection
SLM
Integrated Photonics Orbital angular momentum
engineering of high-dimensionalquantum states
Network with fixed links
System acting as a reservoir
Detection and feedback to act on the reservoir
Involved people
Sapienza
Alessia SupranoDavide PoderiniMauro ValeriEmanuele PolinoIris AgrestiTaira GiordaniGonzalo CarvachoFulvio FlaminiNicolò SpagnoloFabio Sciarrino
IFN-CNR
Simone AtzeniGiacomo CorrielliAndrea CrespiRoberto Osellame
Queen’s University Belfast
Helena MajuryLuca InnocentiAlessandro FerraroMauro Paternostro
Microsoft Research
Nathan Wiebe
Università di Napoli
Lorenzo Marrucci
UCL and Oxford
Andrea RocchettoSimone Severini
University of Texas
Scott Aaronson
Capable
www.quantumlab.it
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