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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