Quantum Computing
The list of articles published on quantum computing
Simulating static and dynamic properties of magnetic molecules with prototype quantum computers - 2021
Crippa L, Tacchino F, Chizzini M, Aita A, Grossi M, Chiesa A, Santini P, Tavernelli I, Carretta S.
Magnetic molecules are
prototypical systems to investigate peculiar quantum mechanical phenomena. As such, simulating their static
and dynamical behavior is intrinsically difficult for a classical computer, due to the exponential increase
of required resources with the system size. Quantum computers solve this issue by providing an inherently
quantum platform, suited to describe these magnetic systems. Here, we show that both the ground state properties
and the spin dynamics of magnetic molecules can be simulated on prototype quantum computers, based on superconducting
qubits. In particular, we study small-size anti-ferromagnetic spin chains and rings, which are ideal test-beds for
these pioneering devices. We use the variational quantum eigensolver algorithm to determine the ground state wave-function
with targeted ansatzes fulfilling the spin symmetries of the investigated models. The coherent spin dynamics are simulated
by computing dynamical correlation functions, an essential ingredient to extract many experimentally accessible properties,
such as the inelastic neutron cross-section.
A serverless cloud integration for quantum computing - 2021
Grossi M, Crippa L, Aita A, Bartoli G, Sammarco V, Picca E, Said N, Tramonto F, Mattei F
Starting from the idea of Quantum Computing which is a concept that dates back to 80s, we come to
the present day where we can perform calculations on real quantum computers. This sudden development of technology opens up new scenarios that quickly lead to the desire and the real possibility of
integrating this technology into current software architectures. The usage of frameworks that allow
computation to be performed directly on quantum hardware poses a series of challenges.
This document describes a an architectural framework that addresses the problems of integrating an
API exposed Quantum provider in an existing Enterprise architecture and it provides a minimum viable product (MVP) solution that really merges classical quantum computers on a basic scenario with
reusable code on GitHub repository. The solution leverages a web-based frontend where user can
build and select applications/use cases and simply execute it without any further complication. Every
triggered run leverages on multiple backend options, that include a scheduler managing the queuing
mechanism to correctly schedule jobs and final results retrieval. The proposed solution uses the upto-date cloud native technologies (e.g. Cloud Functions, Containers, Microservices) and serves as a
general framework to develop multiple applications on the same infrastructure
Towards practical Quantum Credit Risk Analysis - 2022
Dri E, Giusto E, Aita A, Montrucchio B
In recent years a CRA (Credit Risk Analysis) quantum algorithm with a quadratic
speedup over classical analogous methods has been introduced [1]. We propose a new variant of
this quantum algorithm with the intent of overcoming some of the most significant limitations
(according to business domain experts) of this approach. In particular, we describe a method to
implement a more realistic and complex risk model for the default probability of each portfolio’s
asset, capable of taking into account multiple systemic risk factors. In addition, we present a
solution to increase the flexibility of one of the model’s inputs, the Loss Given Default, removing
the constraint to use integer values. This specific improvement addresses the need to use real
data coming from the financial sector in order to establish fair benchmarking protocols.
Although these enhancements come at a cost in terms of circuit depth and width, they
nevertheless show a path towards a more realistic software solution. Recent progress in quantum
technology shows that eventually, the increase in the number and reliability of qubits will allow
for useful results and meaningful scales for the financial sector, also on real quantum hardware,
paving the way for a concrete quantum advantage in the field.
The paper also describes experiments conducted on simulators to test the circuit proposed
and contains an assessment of the scalability of the approach presented.
A More General Quantum Credit Risk Analysis Framework - 2023
Dri E, Aita A, Giusto E, Ricossa D, Corbelletto D, Montrucchio B, Ugoccioni R
Credit risk analysis (CRA)
quantum algorithms aim at providing a quadratic speedup over classical analogous methods. Despite this, experts
in the business domain have identified significant limitations in the existing approaches. Thus, we proposed a new
variant of the CRA quantum algorithm to address these limitations. In particular, we improved the risk model for
each asset in a portfolio by enabling it to consider multiple systemic risk factors, resulting in a more realistic
and complex model for each asset’s default probability. Additionally, we increased the flexibility of the loss-given-default
input by removing the constraint of using only integer values, enabling the use of real data from the financial sector
to establish fair benchmarking protocols. Furthermore, all proposed enhancements were tested both through classical
simulation of quantum hardware and, for this new version of our work, also using QPUs from IBM Quantum Experience in
order to provide a baseline for future research. Our proposed variant of the CRA quantum algorithm addresses the significant
limitations of the current approach and highlights an increased cost in terms of circuit depth and width. In addition,
it provides a path to a substantially more realistic software solution. Indeed, as quantum technology progresses, the
proposed improvements will enable meaningful scales and useful results for the financial sector.
Towards an end-to-end approach for quantum principal component analysis - 2023
Dri E, Aita A, Fioravanti T, Franco G, Giusto E, Ranieri G, Corbelletto D, Montrucchio B
Quantum Machine Learning has gained significant
attention in recent years as a way to leverage the relationship between quantum information and machine learning. Principal
Component Analysis (PCA) is a fundamental technique in machine learning, and the potential for its quantum acceleration has been
extensively studied. However, an algorithmic end-to-end implementation remains challenging. This paper covers quantum PCA
implementation up to extracting the principal components. We extend existing processes for quantum state tomography to extract
the eigenvectors from the output state, addressing the challenges of dealing with complex amplitudes in the case of non-integer
eigenvalues. Finally, we apply our implementation to a practical quantum finance use case related to interest rate risk, and
present the results of our experiments.