2021-08-03T11:46:25Z
https://zenodo.org/oai2d
oai:zenodo.org:4889813
2021-06-02T01:48:18Z
openaire
user-neasqc
Dunjko, Vedran
Villalpando, Antonio
Nogueiras, Maria
Ordóñez Sanz, Gustavo
Vázquez Cendón, Carlos
Leitao Rodríguez, Álvaro
Manzano Herrero, Alberto
Musso, Daniele
Gómez, Andrés
2021-06-01
Quantum Computing commenced in 1980’s with the pioneering work of Paul Benioff (Benioff, 1980) who proposed a quantum mechanical Turing machine. These ideas were also explored by the likes of Richard Feynman (Feynman, 1982) and Yuri Manin (Manin, 1980) who suggested that quantum computers could provide advantage over classical computers in certain tasks, such as the simulation of physical systems. In 1994, Peter Shor (Shor, 1994) published a groundbreaking paper demonstrating that a quantum algorithm could be used to for large integer factorisation in polynomial time, that is, exponentially faster than the best known classical algorithms. This was followed by Lov K. Grover who proposed a quantum computing algorithm that promised a quadratic speed up over database searches (L. K. Grover, 1996).
Advances in Quantum Computing hardware technology in recent years have been accompanied by the acceleration on the development of quantum computing algorithms with applications across many different use cases in different industry sectors: Automotive, Energy, Logistics, Pharma, Chemical/Manufacturing and the Financial Services Industry. One of the use cases in Finance comes from the application of Quantum Computing for Derivative Pricing and Derivative Risk Management. The purpose of this document is toprovide a summary of the “state of the art” for these applications.
However, it is important to note that the currently available quantum computers have limited number of qubits and these suffer from high levels of “noise” which limits the depth and length of the quantum circuits that can be implemented in real hardware. Therefore, near-term applications focus on implementations of quantum algorithms in Noisy Intermediate-Scale Quantum (NISQ) computers (Preskill, 2018).
Derivatives contract form one of the fundamental pillars of modern financial markets and are routinely traded by both financial institutions and traders with a variety of objectives, such as financial risk hedging. A simple example of financial derivative is a European stock option. This contract provides the derivative holder with the right to purchase or sell the stock at some time in the future for a fixed price agreed today. Hence, providing with potential upside (should the stock increase in price at maturity) while limiting the investor’s downside.
Derivatives pricing theory is the branch of financial mathematics that covers the fair valuation of financial derivatives such as options. This framework assumes that the underlying security (e.g. a single stock) follows some random (stochastic) process. The price of the derivative hence depends on the particular realisation of such process at a given point in time (e.g. the option maturity). The best known example of option pricing model is the Black-Scholes model (Black, 1976). This model proves that a fair value of an
option can be derived under certain assumptions (e.g. absence of arbitrage, continuous and unlimited long and short trading).
However, in general the Black-Sholes model is too simplistic to fit actual quoted prices in the market and other more complex models are used instead, at the cost of requiring numerical approximations to find the fair price of the derivative. Two main numerical approaches are currently used in the industry, Monte Carlo-based simulation techniques and partial differential equation (PDEs) approaches. The key advantage of the former is that it is easy to implement, very general and scales well with the dimension of the problem. On the other hand, Monte Carlo simulation tends to converge slowly to the required solution. It is also difficult to obtain risk sensitivities (i.e. how the derivative price depends on changes to the price underlying) using Monte Carlo. PDEs approaches are generally faster and permit the easy calculation of risk sensitives, however it is usually a difficult problem to solve PDEs for more complex derivatives, specially those that depend on several risk factors (curse of dimensionality). Monte Carlo simulation is therefore the tool of choice for financial risk management where risk metrics need to be estimated at the portfolio level where thousands of derivatives need to be covered.
Quantum computing, and in particular the Quantum Amplitude Estimation (QAE) algorithm promises a potential quadratic speed up over classical Monte Carlo approaches but maintaining its main advantages: easy of implementation and linear scaling to higher dimensions. This document covers these topics in more detail and presents some new results, such as the application of the “Quantum Coin” algorithms as an alternative to QAE.
Despite the highly promising advantages of quantum computing for derivative pricing and risk management, huge challenges remain open for real-world applications. Some of them are technological, such as the relative small number of qubits currently available and the fact that these are “noisy” (i.e. not always reliable).
Others are more theoretical, such as the lack of understanding on how to load classical information (such as probability distributions) to quantum registers, or how to represent relatively complex pay-off functions with quantum circuits that are as small as possible.
https://zenodo.org/record/4889813
10.5281/zenodo.4889813
oai:zenodo.org:4889813
eng
info:eu-repo/grantAgreement/EC/H2020/951821/
doi:10.5281/zenodo.4889812
url:https://zenodo.org/communities/neasqc
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Quantum Finance
Finance
Quantum Amplitude Estimation
QCoin
Derivatives Pricing
Risk Measures
VaR
CVaR
D5.1: Review of state-of-the-art for Pricing and Computation of VaR
info:eu-repo/semantics/report
publication-deliverable
oai:zenodo.org:4707556
2021-05-10T11:27:15Z
openaire
user-neasqc
Moussa, Charles
Wang, Hao
Calandra, Henri
Bäck, Thomas
Dunjko, Vedran
2021-04-09
Combinatorial optimization is an important application targeted by quantum computing. However, near-term hardware constraints make quantum algorithms unlikely to be competitive when compared to high-performing classical heuristics on large practical problems. One option to achieve advantages with near-term devices is to use them in combination with classical heuristics. In particular, we propose using quantum methods to sample from classically intractable distributions – which is the most probable approach to attain a true provable quantum separation in the near-term – which are used to solve optimization problems faster. We numerically study this enhancement by an adaptation of Tabu Search using the Quantum Approximate Optimization Algorithm (QAOA) as a neighborhood sampler. We show that QAOA provides a flexible tool for exploration-exploitation in such hybrid settings and can provide evidence that it can help in solving problems faster by saving many tabu iterations and achieving better solutions.
CM, TB and VD acknowledge support from Total. This work was supported by the Dutch Research Council (NWO/OCW), as part of the Quantum Software Consortium programme (project number 024.003.037).
https://zenodo.org/record/4707556
10.1007/978-3-030-72904-2_7
oai:zenodo.org:4707556
eng
Springer
info:eu-repo/grantAgreement/EC/H2020/951821/
doi:10.1007/978-3-030-72904-2
isbn:978-3-030-72903-5
url:https://zenodo.org/communities/neasqc
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Quantum computing
Combinatorial optimization
Tabu search
Tabu-Driven Quantum Neighborhood Samplers
info:eu-repo/semantics/conferencePaper
publication-conferencepaper
oai:zenodo.org:4745508
2021-05-10T13:48:08Z
openaire
user-neasqc
Hibti, Mohamed
Laarman, Alfons
Moret-Bonillo, Vicente
Mosqueira-Rey, Eduardo
Magaz-Romero, Samuel
Gómez-Tato, Andrés
2021-04-15
This report is the first deliverable of UDC-CESGA, related to task 6.2 of Work Package 6 of the NEASQC project, UC6. The document incorporates information on the approach to the work carried out so far, from the project start date to the deadline established for this first deliverable.
The report includes a brief description of invasive ductal carcinoma of the breast (IDC), the methodology followed for the modeling of a rule-based system for the diagnosis and treatment of IDC, a preliminary analysis to evaluate the suitability of quantum computing in this domain, a proposal about the quantum approximation that we want to use, and that we will later have to evaluate, and the analysis about the formal requirements of the application that we intend to carry out. We also include a quantum proposal on the uncertainty associated with reasoning in medicine.
A brief summary of the IDC is necessary to place the use case in the context of the project. The description will range from the initial symptoms that allow the clinician to consider the possibility of IDC, the diagnostic process, the degree of severity of the IDC, and the possible associated treatments.
The methodological description of the knowledge engineering used is necessary to understand the architecture of a classical rule-based system, and to be able to formalize the problem in terms of declarative knowledge, procedural knowledge and inferential circuits.
Next, a qualitative analysis of the problem in terms of quantum logical operators is presented to illustrate the possibility of converting a conventional rule-based system into a quantum rule-based system.
Finally, the formal requirements of the quantum rule-based system will be mentioned. Also, we will pay special attention to the imprecision of the information and the uncertainty associated with clinical practice.
https://zenodo.org/record/4745508
10.5281/zenodo.4745508
oai:zenodo.org:4745508
eng
info:eu-repo/grantAgreement/EC/H2020/951821/
doi:10.5281/zenodo.4745507
url:https://zenodo.org/communities/neasqc
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Models
Architecture
Formal specification
D6.2 Quantum Rule-Based Systems (QRBS) Models, Architecture and Formal Specification
info:eu-repo/semantics/report
publication-deliverable
oai:zenodo.org:4565185
2021-05-10T09:55:57Z
openaire
user-neasqc
Moret-Bonillo, Vicente
Fernández-Varela, Isaac
Álvarez-Estévez, Diego
2021-01-12
This article deals with the problem of the uncertainty in rule-based systems (RBS), but from the perspective of quantum computing (QC). In this work we first remember the characteristics of Quantum Rule-Based Systems (QRBS), a concept defined in a previous article by one of the authors of this paper, and we introduce the problem of quantum uncertainty. We assume that the subjective uncertainty that affects the facts of classical RBSs can be treated as a direct consequence of the probabilistic nature of quantum mechanics (QM), and we also assume that the uncertainty associated with a given hypothesis is a consequence of the propagation of the imprecision through the inferential circuits of RBSs. This article does not intend to contribute anything new to the QM field: it is a work of artificial intelligence (AI) that uses QC techniques to solve the problem of uncertainty in RBSs. Bearing the above
arguments in mind a quantum model is proposed.
This model has been applied to a problem already defined by one of the authors of this work in a previous publication and which is briefly described in this article. Then the model is generalized, and it is thoroughly evaluated. The results obtained show that QC is a valid, effective and efficient method to deal with the inherent uncertainty of RBSs.
https://zenodo.org/record/4565185
10.26502/acbr.50170149
oai:zenodo.org:4565185
eng
info:eu-repo/grantAgreement/EC/H2020/951821/
url:https://zenodo.org/communities/neasqc
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Archives of Clinical and Biomedical Research 5(1) 42-60
Quantum Artificial Intelligence; Quantum Rule-Based Systems; Quantum Computing; Uncertainty
Uncertainty in Quantum Rule-Based Systems
info:eu-repo/semantics/article
publication-article
oai:zenodo.org:4745443
2021-05-10T13:48:08Z
openaire
user-neasqc
Dunjko, Vedran
Gómez, Andrés
Villalpando, Antonio
O'Riordan, Lee J.
Balodis, Kaspars
Krišlauks, Rihards
2021-02-24
Understanding the applicability of NISQ-era devices for a variety of problems is of the utmost importance to better develop and utilise these devices for real-world use-cases. In this document we motivate the use of quantum computing models for natural-language processing tasks, focussing on comparison with existing methods in the classical natural language processing (NLP) community. We define the current state of these NISQ devices, and define methods of interest that will allow us to exploit the resources to implement NLP tasks, by encoding and processing data in a hybrid classical-quantum workflow. For this, we outline the high-level architecture of the solution, and provide a modular design for ease of implementation and extension.
https://zenodo.org/record/4745443
10.5281/zenodo.4745443
oai:zenodo.org:4745443
eng
info:eu-repo/grantAgreement/EC/H2020/951821/
doi:10.5281/zenodo.4745442
url:https://zenodo.org/communities/neasqc
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Natural Language Processing
Quantum Computing
Tensor Networks
D6.1 QNLP design and specification
info:eu-repo/semantics/report
publication-deliverable