ABOUT THE PROJECT

The NEASQC (NExt ApplicationS of Quantum Computing) project brings together academic experts and industrial end-users to investigate and develop a new breed of Quantum-enabled applications that can take advantage of NISQ (Noise Intermediate-Scale Quantum) systems in the near future. NEASQC is use-case driven, addressing practical problems such as drug discovery, CO2 capture, smart energy management, natural language processing, breast cancer detection, probabilistic risk assessment for energy infrastructures or inventory management.

NEASQC has the ambition to initiate an active European Community around NISQ Quantum Computing by providing a common toolset that will attract new industrial users.

Missions and Objectives

NEASQC aims at demonstrating that, though the millions of qubits that will guarantee fully fault-tolerant quantum computing are still far away, there are practical use cases for the NISQ (Noise Intermediate-Scale Quantum) devices that will be available in the near future. NISQ computing can deliver significant advantages when running certain applications, thus bringing game-changing benefits to users, and particularly industrial users.

The NEASQC consortium has chosen a wide selection of NISQ-compatible industrial and Financial use-cases, and will develop new quantum software techniques to solve those use-cases with a practical quantum advantage. To achieve this, the project brings together an unprecedented multidisciplinary consortium of academic and industry experts in Quantum Computing, High Performance Computing, Artificial Intelligence, chemistry…

The ultimate ambition of NEASQC is to encourage European user communities to investigate NISQ quantum computing. For this purpose, the project consortium will define and make available a complete and common toolset that new industrial actors can use to start their own practical investigation and share their results.

NEASQC OBJECTIVES

1.

Develop 9 industrial and financial use cases
with a practical quantum advantage
for NISQ machines.

2.

Develop open source NISQ programming libraries for industrial use cases, with a view to facilitate quantum computing experimentation for new users.

3.

Build a strong user community
dedicated to industrial
NISQ applications.

4.

Develop software stacks and benchmarks
for the Quantum Technology Flagship
hardware platforms.

Consortium

NEASQC brings together highly skilled and motivated academic experts and industrial end users, who collaborate on very relevant and representative Quantum Computing applications, and will share their learnings with their communities. To maximise industry/academia collaboration, each use case is investigated by an integrated team of a least one industrial partner and one academic partner.

Associated end-users

NEASQC will propose a status of “Associated end-user” to organisations interested in quantum computing and with uses cases of interest. Associated end-users will benefit from a priviledged access to NEASQC information and return of experience, with special sessions organised for them during our project meetings to exchange with project members.

The objective of our Associated end-user scheme will be to help organisations beyond the NEASQC consortium to kickstart their own quantum computing experiments. More information will be provided later.

Deliverables

  • Symbolic AI and graph algorithmics
  • Machine Learning and Optimisation
  • Chemistry

D6.1 QNLP design and specification

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.

D6.2 Quantum Rule-Based Systems (QRBS) Models, Architecture and Formal Specification

This report is the first deliverable related to the use case QRBS. It incorporates information on the approach to the work carried out so far. 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.

D6.3 QNLP Pre-Alpha prototype

The NEASQC project aims at demonstrating and advancing the capabilities of NISQ-era devices through the development of practically-relevant use-cases. Under the category of Symbolic AI and Graph Algorith-mic algorithms, one of the use-cases that is being developed is for Quantum-enabled Natural Language Processing (QNLP). The objective of the QNLP use-case in NEASQC is for the investigation, development and comparison of existing methods in classical NLP with a QNLP approach for encoding and processing sentences in a hybrid classical-quantum workflow.

For this, deliverable D6.1 “QNLP design and specification” was presented in M6 with an overview of the background and existing approaches for classical NLP and quantum NLP along with a detailed illustration of the proposed QNLP software architecture solution and methods for testing and benchmarking the QNLP implementation.

Deliverable D6.3 “QNLP pre-alpha prototype” is the first version of the QNLP software (pre-alpha prototype) which is primarily aimed at assessment within the NEASQC project. D6.3 implements a first version of the modules for generating training datasets (composed of sentences of specific grammatical structures), quantum circuits for training using single sentences and whole datasets, and classical NLP approaches for evaluation. This report accompanying D6.3 provides an overview of the currently implemented modules along with access and usage details of the QNLP pre-alpha prototype.

D6.5 Quantum Rule-Based System (QRBS) Requirement Analysis

This report is the second deliverable of Task 6.2-Quantum Rule-Based Systems (QRBS) for breast cancer detection of the NEASQC project. The document presents the work carried out so far, and is complementary to the other deliverables of this task.

The report begins with an introduction into the requirement analysis process, explaining the necessary concepts to understand the development of the work. Along with said concepts,the applied procedure for the analysis of requirements used in this work is also presented, to put the reader in context and to justify the following sections.

Once the necessary concepts have been presented, the report illustrates the different phases of the work carried out, starting with the needs and features (vision). This phase serves to detect several factors that are crucial to the requirement analysis, such as the actors related to the system or the needs that the system must cover.

The document continues with next step of the process, related to the use cases, which are decompositions of the previous needs according to their functionality and logical structuring. Each use case is studied, explained and detailed in depth. This decomposition allows for a more robust and traceable development across the next phases of the work.

Finally, following the previous decomposition, test cases are defined. This test cases conform a test plan that will help on later stages of the development,when it arises the need to check whether the system meets the requirements specified.

D6.6: Divide and quantum open source software

Fault trees are a type of model which captures how small failures in probabilistic systems can propagate and ultimately lead to a critical system failure. An important component of fault tree analysis is finding small subsets of events which can cause a critical failure (often called “cut sets”). Finding these small cut sets is important because they often correspond to the most likely way a system will fail.

In this deliverable we provide an open source implementation of a procedure which computes minimal cut sets from fault trees by translating the problem to a satisfiability (SAT) problem. This SAT formula can then either be solved with a classical SAT solver, or with a quantum algorithm: specifically Grover’s algorithm for amplitude amplification. The solutions found by both methods are the same, but the quantum algorithm allows for a quadradic speedup in time complexity. Additionally, for quantum computers which have too few qubits to handle the entire problem instance, a divide and conquer approach can theoretically be used to split the problem up and obtain smaller quantum speedups (Rennela, Brand, Laarman, & Dunjko, 2021).

The main component of this deliverable is the open source library itself, which can be found online here: https://github.com/NEASQC/ft-2-quantum-sat. In section 2 of this document we give an overview of problem of finding cut sets, and how the library solves this problem.

D6.7 QNLP Alpha Prototype

Being a software deliverable, D6.7 “QNLP alpha prototype”, is an intermediate version composed of the approaches that are being explored and evaluated for the targeted QNLP tasks – parallel data extraction and intent detection.

The deliverable D6.3 “QNLP pre-alpha prototype” (Villalpando et al., 2021a) developed an early implementation of the DisCoCat-based model using parameterised quantum circuits to encode a pre-defined dataset of sentences, and trained the parameterised circuits which were evaluated using a test dataset for true/false conditions. Further description of the DisCoCat model is available at (Coecke et al., 2010).

This deliverable D6.7 is a software prototype that adds an additional approach based on Dressed Quantum Circuits (Mari et al., 2020) in which pre-trained classical models are used as pre-processing layers in a transfer learning fashion. Neural Networks are implemented to transform pre-trained word vectors into usable lower dimensional vectors acting as the parameters for the rotations of variational quantum circuits.

The software implementation of this approach is available for internal evaluation at the NEASQC QNLP GitHub repository under a dedicated releases branch tagged v0.2-alpha-d0.9.

An outline about the Dressed Quantum Circuits approach is summarised at the README.md file of the alpha prototype, and the dedicated Jupyter notebook.

An outline of instructions for downloading and running the associated Jupyter notebooks for the pre-alpha and alpha prototypes are provided in README.md file of the NEASQC QNLP GitHub repository, and listed in Section 3.

It is to be noted that both the pre-alpha and alpha prototypes of the QNLP software are works in progress and intended for internal evaluation and testing of the theoretical/algorithmic approaches.

D5.1 Review of state-of-the-art for Pricing and Computation of VaR

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 to provide a summary of the “state of the art” for these applications.

D4.1: VA Beta and BBO Beta

Here we present the quantum computing computational package for quantum chemistry. It contains the implementation of the variational Hamiltonian ansatz state preparation for chemical systems. Equipped with the variational algorithms for imaginary- and real-time evolution, the package can optimize and propagate in time wave functions for chemical systems. As the Pre-Born-Oppenheimer molecular structure is implemented, one can describe nuclear quantum effects. We show the use cases for the developed methods on small chemical systems such as lithium hydride and hydrogen molecules. Specifically, the real- and imaginary-time evolutions have been proven to work correctly and efficiently. The Pre-Born-Oppenheimer scheme delivers results in agreement with the reference. Furthermore, it is shown that the variational Hamiltonian ansatz may approximate wave functions more efficiently than traditional variational ansatzes.

D4.2 QCCC alpha

The code presented in this document allows for the calculation of the ground state energy of benzene under spatial de-formations by using a state-of-the-art quantum computing methodology – the variational quantum eigensolver (VQE). Two types of quantum computing ansatze are implemented (the hardware efficient one and the qUCC). The code supports noisy simulations and three types of spatial deformations of the benzene molecule. The code is available on Github : https://github.com/NEASQC/D4.2.

Project factsheet

Project NameNExt ApplicationS of Quantum Computing
AcronymNEASQC
Project TypeResearch and Innovation Action (RIA)
Time span01/09/2020-31/08/2024
CallH2020-FETFLAG-2020-01
Grant Agreement951821
CoordinatorAtos (Bull SAS)
EU Contribution€ 4 671 332,50

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