QRC2024

This Course is hosted at the

ETTORE MAJORANA FOUNDATION AND CENTRE FOR SCIENTIFIC CULTURE

FOUNDER: A. ZICHICHI

as part of the

INTERNATIONAL SCHOOL ON NONEQUILIBRIUM PHENOMENA

Directors of the School: A. Lanzara, G. M. Palma, B. Spagnolo

Working Party on Quantum Reservoir Computing

Directors of the Course: S. Lorenzo, G. M. Palma, M. Paternostro

Dates: 15 - 18 October 2024

The aim of the course is to discuss the current understanding of quantum reservoir computing and to develop characterization tools for tum computation and process identification. The features of specific platforms currently being explored for these goals will be considered, as well as impact of noise and thermodynamic constraints.
The working party will discuss the range of tasks that can be efficiently solved by QRC and, bly, the combination of analytical and numerical approaches, including the exploitation of quantum simulation on quantum computing platforms.

If you want to join us, please REGISTER.

Program

10:30 - 12:30

Arrivals, opening and registration

12:30 - 15:00

Lunch

15:00 - 15:50

Tomasz Paterek

Quantum Reservoir Processing

15:50 - 16:40

Oliver Neill

Photon number resolved quantum reservoir computing

16:40 - 17:10

Coffee break

17:10 - 18:00

Enrico Prati

Quantum machine learning and the exploitation of noise
as a computational resource

9:00 - 9:50

Valeria Cimini

Experimental realization of
photonic quantum extreme learning machines

9:50 - 10:40

Andrea Smirne

Quantumness in sequential measurements:
coherences, correlations and quantum combs

10:40 - 11:10

Coffee break

11:10 - 12:00

Marco Vetrano

QELM response to scrambling dynamics and
applications to atmospheric retrieval

12:00 - 12:50

Marco Miceli

An astrophysical problem:
can we study stellar explosions through QRC?

12:50 - 15:00

Lunch

15:00 - 15:50

Antonio Sannia

Dissipative Quantum Reservoir Computers

15:50 - 16:20

Coffee break

16:20 - 17:10

Gabriele Lo Monaco

QELM for quantum chemistry and biological tasks

17:10 - 18:00

Alessandro Ferraro

Quantum resources and classical simulation
in continuous-variable circuits.

9:00 - 9:50

Sofia Sgroi

Designing fully connected quantum networks
for optimal excitation-transfer

9:50 - 10:40

Dario Tamascelli

Spectral Density Modulation and Universal
Markovian Closure of Structured Environments

10:40 - 11:10

Coffee break

11:10 - 12:00

Davide Rattacaso

Quantum circuit compilation with quantum computers

12:00 - 12:50

Francesco Monzani

Universality conditions of unified classical and
quantum reservoir computing

12:50 - 15:00

Lunch

15:00 - 15:50

Jacopo Settino

Memory-Augmented Hybrid Quantum Reservoir Computing

15:50 - 16:20

Coffee break

16:20 - 17:10

Iris Agresti

Experimental quantum machine learning on photonic platforms

17:10 - 18:00

Alessandro Romancino

Entanglement Percolation in Random State Quantum Networks

9:00 - 9:50

Alessandra Colla

Thermodynamic role of general environments:
from heat bath to work reservoir

9:50 - 10:40

Luca Innocenti

Connections Between QELM, QRC, and Shadow Tomography

10:40 - 11:10

Coffee break

11:10 - 12:00

Marco Pezzutto

Non-positive energy quasidistributions
in coherent collisional models

12:00 - 15:00

Closing & Lunch

Title: Quantum extreme learning machine for quantum chemistry and biological tasks

Abstract: Among the paradigms of quantum supervised machine learning, quantum reservoir computing, and specifically the quantum extreme learning machine (QELM), has emerged as some of the most promising approaches. The notable advantages of QELM lie in the simplicity of its optimization routine which require minimal resources. Classical data are encoded in the quantum state of input qubits, with the choice of encoding determining model’s expressivity. The information is distributed across a reservoir interacting with the encoding qubits through a fixed scrambling dynamics that does not require any optimization during training. The reservoir is then measured and the outcomes are collected on a classical computer for post-processing, the optimization step, involving a simple linear regression to fit the target data.
We present the first implementation of QELM on NISQ devices with applications to quantum chemistry and biological tasks of practical interest. We use QELM to reconstruct the potential energy surface of various molecules, mapping the geometry of a molecular species to its energy. Our setup outperforms classical neural networks and other quantum routines such as VQE, while minimizing quantum resource costs. It is scalable and has controlled depth, making it suitable for practical implementation on real hardware. The implementation performed on IBM Quantum platforms yielded encouraging results, with the average error close to chemical accuracy and no need for error mitigation.
In the biological domain, we employ QELM as a support vector machine for protein classification on large experimental datasets. A practical example is classifying proteins based on their interaction with angiotensin enzyme, predicting peptides with anti-hypertensive effects that could contribute to cardiovascular health. The proteins are described in terms of 40 descriptors of biological relevance, such as aromaticity or hydrophobicity, a large number of input features presenting a challenge and benchmark for the current status of QELM. This second application confirms the feasibility of QELM and manifests an interesting resistance to statistical noise and decoherence, suggesting its potential for imminent applications in industrial pharmacology.

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