Research


Encoding information in the time–frequency domain offers a versatile and efficient platform for quantum information processing. It provides a novel scheme for communications with large alphabets, computing with large quantum systems, and new approaches to metrology. It is hence crucial to secure full control on the generation of time–frequency quantum states and their properties.

Biphoton states generated for instance through SPDC sources can exhibit correlations in time and frequency, making it necessary to reconstruct their joint spectral profile and phase. Characterizing the spectral phase in particular poses a great challenge, one that has similarly been taken up by classical ultrafast metrology to control ultrashort pulses in the femtosecond and attosecond timescales.


Related Publications

I. Gianani - “Robust spectral phase reconstruction of time-frequency entangled bi-photon states” Phys. Rev. Research, 1, 033165, 2019





The importance of networks as a model to describe complex systems cannot be overstated. They are used to describe social interactions, biological processes, transport phenomena, and physical systems alike. In the context of quantum technologies, networks constitute the underlying structure of communication and computation protocols. Understanding how quantum information can be reliably transmitted between distant nodes, or routed among different computational units is a key step in development of secure communication schemes and fast computation protocols.
QWs are the natural paradigm to study how quantum information is distributed across these networks. Walkers can be used as quantum probes for the estimation of parameters, allowing to take advantage of estimation theory stemming from quantum metrology. In this framework, the amount of extractable information from a system is upper bounded by the Quantum Fisher Information. This allows to cast the information distribution problem using a quantifiable figure of merit, leading to a reliable characterization of the network properties.
In order to explore the realm of large complex networks from an experimental perspective, it is necessary to identify a platform enabling quantum walks with great tailorability and scalability, allowing access to long-time dynamics on the network, hence involving a great number of nodes. In this regard, realising simulated quantum walks that can be controlled and easily modified to explore different configurations, is an ideal solution.
Networks in the spectral domains answer all the above-mentioned requirements. Furthermore, the way in which the walk is simulated on these networks opens up intriguing possibilities. Indeed, while it is possible to control the available experimental parameters to simulate linear quantum walks, they also allow to simulate more involved evolutions on the network, that are nontrivially recognizable. This poses a further question compared to the mere characterization of the network: can we trace back and identify what evolution the network has incurred, starting from its state at different times? This question naturally leads to incorporating a machine learning approach in our research, with the development of Hamiltonian learning algorithms capable of providing a rapid answer to this problem.
The generation of photons through spontaneous parametric downconversion (SDPC) can, if properly tailored, allow to obtain anticorrelations between the generated photons resulting from energy conservation. At the same time it grants access to a large two-dimensional network where each node is identified by the frequencies of the two photons, whose space is conventionally discretized when performing a measurement. Accessing these features however requires a thorough characterization and control of the photon spectral properties, i.e. the joint spectral amplitude and phase, a task akin to the spectral characterization of classical ultrafast pulses. Full characterization of the spectral state allows to shape the initial state of the network with specific control on its entanglement, hence opening up a vast playground for exploring the effects this has on its evolution on the frequency network.

Related Publications

C. Benedetti, I. Gianani, "Identifying network topologies via quantum walk distributions”, AVS Quantum Science 6, 014412, 2024

I. Gianani, C. Benedetti, “Multiparameter estimation of continuous-time Quantum Walk Hamiltonians through Machine Learning”, AVS Quantum Science 5 (1), 014405, 2023





Quantum spectroscopy leverages the unique properties of quantum light, such as entanglement, to achieve superior spectral resolution compared to classical light sources and to address technical requirements such as that of performing the measurements remotely.

Related Publications

A Chiuri, M Barbieri, I Venditti, F Angelini, C Battocchio, MGA Paris, I. Gianani, "Fast remote spectral discrimination through ghost spectrometry”, Physical Review A 109 (4), 042617, 2024

M. Barbieri, I. Venditti, C. Battocchio, V. Berardi, F. Bruni, I. Gianani, “Observing thermal lensing with quantum light”, Optics Letters, 49,5, 1257-1260 (2024)

A. Chiuri, F. Angelini, S. Santoro, M. Barbieri, I. Gianani, “Quantum Ghost Imaging Spectrometer” ACS Photonics, 10, 12, 4299-4304 (2023)

I. Gianani, LLS Soto, AZ Goldberg, M Barbieri "Efficient lineshape estimation by ghost spectroscopy”, Optics Letters 48, 3299-3302 (2023)

I. Gianani, M. Barbieri, F. Albarelli, A. Verna, V. Cimini, R. Demkowicz-Dobrzanski, “Kramers-Kronig relations and precision limits in quantum phase estimation”, Optica,8,12, 2021





Machine learning has emerged as a powerful tool in quantum optics, where it is used to optimize complex experiments and improve data processing. In this research area, machine learning algorithms are applied to tasks such as calibrating quantum sensors and estimating parameters in quantum systems. By combining quantum technologies with machine learning, one can enhance the performance of quantum devices and develop new ways to analyze quantum information. Indeed, all the previous research lines presented in this page do greatly benefit from the use of ML techniques, may that be as unbiased estimators for recovering the parameters of a network, as a calibration and spectral analysis tool for spectroscopy sensors or as a way to retrieve the spectral phase of classical and quantum states.

Related Publications

I Gianani , IA Walmsley, M Barbieri - SPIDERweb: a neural network approach to spectral phase interferometry, Optics Letters 49 (19), 5415-5418, 2024.

V.Cimini, I. Gianani, N. Spagnolo, F. Leccese, F. Sciarrino, M. Barbieri “Calibration of quantum sensors by neural networks” Phys. Rev. Letters, 123, 230502, 2019

I. Gianani, I. Mastroserio, L. Buffoni, N. Bruno, L. Donati, V. Cimini, M. Barbieri, F. S. Cataliotti, F. Caruso, “Experimental Quantum Embedding for Machine Learning”, Advanced quantum technologies, 2100140, 2022.

Ilaria Gianani's teaching activity

A.A 2024/2025

Fotonica Quantistica

Laurea Magistrale in Fisica
Dipartimento di Matematica e Fisica, Università degli studi Roma Tre
Teams: Link

How to Journal Club - public speaking course

Dottorato SciMaNo
Dipartimento di Scienze, Università degli studi Roma Tre
Teams:

Elementi di Fisica dei Materiali

Laurea Triennale in Ottica ed Optometria
Dipartimento di Scienze, Università degli studi Roma Tre
Teams:

Elementi di Fisica generale (Esercitazioni)

Laurea Triennale in Ottica ed Optometria
Dipartimento di Scienze, Università degli studi Roma Tre
Teams:



2nd COLLOQUIUM GDR TEQ QUANTUM TECHNOLOGIES

November 13-15th 2024 - Sorbonne Université (FR)

I. Gianani - invited talk - "Ultrafast quantum metrology"






Quantum Women's Day

15h April 2025 - Università degli Studi dell’Insubria




TIME-FREQUENCY QUEST PAGE - SAVING PHOTONLAND GAME

Collaboration with QPlayLearn and Kuuasema (Game Dev)

Saving Photonland! is a game in which you have to solve some experimental puzzles. It allows you to build an intuition about time-frequency modes and how to deconstruct quantum pulses. To win, you have to reconstruct pairs of photons by adjusting their amplitudes and reconstructing their modes.




A conversation with women in quantum

14th May 2024 - Department of Physics and Chemistry University of Palermo





STORMYTUNE CONFERENCE

19th-21st June 2024, Gaeta (IT)
Organizing Committee: I. Gianani, M. Barbieri, B. Brecht, C. Silberhorn


ERSHE - Workshop with ERC winners

31st May 2023 - University of Roma Tre




Meet the team


CURRENT POSITION

2023 – date: Tenure-track Assistant professor (RTDb), Università degli Studi Roma Tre (IT)

Abilitazione Scientifica Nazionale 02/B1 Seconda Fascia valid from 30/05/2022 to 30/05/2033


SHORT BIO

Ilaria Gianani is currently a tenure-track Assistant Professor (RTDb) at Università degli Studi Roma Tre, having previously held postdoctoral research positions at both Università degli Studi Roma Tre (PI: Prof. M. Barbieri) and Sapienza Università di Roma (PI: Prof F. Sciarrino), and a fixed-term assistant professor (RTDa) position at Università degli Studi Roma Tre.
Dr. Gianani holds a DPhil in Atomic and Laser Physics from the University of Oxford obtained in 2018 under the supervision of Prof I. A. Walmsley, where her thesis focused on the characterization of ultrashort pulses. She also earned a Master’s and Bachelor’s degree in Physics, both from Sapienza Università di Roma, graduating with full honors (Supervisors: L. Guidoni (BSc), P. Mataloni (MSc)). Her academic journey has provided a strong foundation in quantum optics, metrology, and fundamental physics.
She has broad expertise in quantum optics, including the generation and manipulation of multiple degrees of freedom of quantum light, photonic implementation of quantum communications, quantum metrology, quantum simulators, foundational questions, and the integration of machine learning techniques in quantum technologies. She has a solid background in ultrafast pulse characterisation. She has published 70+ publications in peer-reviewed journals (including Nature group, Optica, Physical Review Letters, PRX Quantum, Advanced Photonics), which have received 1200+ citations, with an H-index of 20 (Google Scholar).In addition to her scientific endeavors, Dr. Gianani is dedicated to mentoring students and researchers. She actively participates in initiatives promoting diversity in science, and she has founded the Women in STEM Roma Tre initiative.



Mylenne Manrique is a PhD student in Roma Tre. She obtained her Bsc degree in Applied Physics and Msc degree in Materials Science and Engineering, both from University of the Philippines Diliman. Before starting her PhD, she worked on semiconductors, mainly low-cost fabrication and optical characterization techniques.




Main collaborators

Marco Barbieri, Università degli studi Roma Tre (IT)
Claudia Benedetti, Università degli Studi di Milano (IT)
Andrea Chiuri, ENEA (IT)
Luis Lorenzo Sanchez Soto, Universidad Complutense Madrid (ES)
Aephraim Steinberg, University of Toronto (CA)
Ian A. Walmsley, Imperial College (UK)
M. Paris, Università degli Studi di Milano (IT)
Iole Venditti and Chiara Battocchio, Università degli studi Roma Tre (IT)

Outreach collaborators

Caterina Foti, QPlayLearn (FI)

Grants

Active Grants

EQWALITY: Entangled Quantum walks on spectrAl modes: machine LearnIng and meTrologY

Funding programme: PRIN 2022 - MUR (IT)
Starting Date: TBA
Funding RM3: 123k EUR
Partners: Università degli studi di Milano (PI: C. Benedetti)
Università degli studi Roma Tre (PI: I. Gianani)

Previous Grants

HADES

Funding Programme: NATO-SPS
Period: 2021-2023
Funding RM3: 107k EUR
Partners: ENEA (PI: A. Chiuri)
University of Geneva (PI: J.P. Wolf)
Università degli studi Roma Tre (PI: Marco Barbieri, Co-investigator: I. Gianani)

Other