The QPath Blog

Quantum Pharmacogenomics with QuantumPath® on Amazon Braket


José Luis Hevia
aQuantum CTO

Guido Peterssen Nodarse
aQuantum COO


Quantum computing has the most varied applications in the field of medicine and health: drug discovery, reconstruction of DNA sequences, solving genome assembly tasks, classification of medical images, detection of COVID-19 patients with X-rays, etc.

One of the most interesting applications is to predict the interaction and the effect of a drug on a given organism, which is impossible to obtain in practical response time with classical computers due to the large number of variables to be dealt with.. Therefore, only quantum computing will allow us to adequately predict diseases and patient outcomes and make precision medicine a reality. Precision medicine (finding the precise drug for the most specific target) has evolved at an exponential pace over the last few years and fuelled the development of person-centred medicine [1]. One of the key elements in this regard has been the scientific knowledge derived from panomics, he combined analysis of multi-dimensional genomic, transcriptomic, proteomic, and metabolomic data [2]. In fact, there is no personalized medicine without pharmacogenetics or pharmacogenomics, it does not make any sense [3].


QHealth: Quantum pharmacogenomics applied to ageing

Genomics is one of the most significant precision medicine drivers and is considered by many experts as the one with most disruptive potential in healthcare [1]. In our case, we are interested in improving elderly drugs treatment, and we have been working in the QHealth project: “Quantum pharmacogenomics applied to aging”. The overall objective of the project is, first and foremost, human: To increase the longevity and quality of life of the elderly, something to which QHealth will contribute thanks to research into the relationships between genetic determinants and other variables in the health trajectory of the elderly throughout their lives, including the reaction that drugs can trigger in the elderly, in such a way as to predict the possible adverse effects that a given drug may have on the health of an elderly person based on their history of taking drugs, the effects of the drugs and their physiological and genetic determinants.

As stated by Dr. Adrián Llerena, President of the Spanish Society of Pharmacogenetics and Pharmacogenomics, member of the Pharmacogenomics Working Party of the European Medicines Agency (EMA) and Scientific leader of the QHealth project, “If we want a more efficient system that adjusts the medication to people and not to averages, we will have to include biomarkers in the portfolio of services” [4].

For this reason, the quantum treatment of biomarkers occupies a strategic place in the research and development of the QHealth project, and is a determining factor among the indicators to measure the results of this project:

·       Genetic factors (biomarkers) included in the analysis

·       Pharmacological interactions between the different drugs included in the analysis.

·       Potential beneficiaries: the elderly population.

To achieve this goal, QHealth has designed the scientific, methodological and technological models necessary for the conception of the scientific and technical foundations of a hybrid classical/quantum system, capable of performing optimizations and simulations impossible to perform in reasonable execution times in classical computers, which, thanks to the integration with classical health applications, provides the results of the system to health professionals in charge of prescribing drugs to the elderly. The hybrid system will not only have a human impact, but it will also have an economic impact because it will make it possible to optimize the investments that health systems make in financing drugs and in dealing with the adverse effects that drugs often cause in the elderly.

The project has a total budget of 5,160,477.00 euros and has received a grant of 3,671,281.69 euros from the CDTI (Center for the Development of Industrial Technology of Spain). The project has been supported by the Spanish Ministry of Science and Innovation and by the ERDF (European Regional Development Fund). It involves a multidisciplinary team of researchers and technologists from the aQuantum (by Alhambra IT), Gloin, and Madrija companies and the University Institute of Biosanitary Research of Extremadura in collaboration with the Pharmacogenetics and Personalized Medicine Unit, the University of Extremadura, and the University of Castilla-La Mancha.

Therefore (Figure 1) QHealth builds a hybrid quantum system combining health-care applications and data analytics with quantum computing. Quantum technologies already allow, at the present time, to perform optimizations and simulations whose realization in classical hardware is not possible within acceptable deadlines. This hybrid system, in combination with classical health applications, will give its outputs to medical professionals involved in prescribing drugs to elderly adults. In a further extension, we also envisage application in the case of younger persons with difficult drug treatment and health conditions, trying to reduce the negative impacts of drugs due to their correlation and mutual side-effects when used in combination. Using the case histories and the socioeconomic and genetic variables of the persons being analysed, we can then also make recommendations for suitable drug treatments and provide risk assessment before they are prescribed.

Figure 1. QHealth project overview


QuantumPath®: the platform for the development of quality quantum software applications 

Some platforms allow users to design and run quantum applications in an integrated development environment but only a few offer a complete ecosystem for quantum software development [5]. QuantumPath® [6] provides an ecosystem of tools, services and processes that simplify the development of quantum algorithms in the context of hybrid information systems (see Figure 2).

Figure 2. QuantumPath® Overview


Figure 2 visually summarizes the most relevant and differentiating elements that QuantumPath® contributes to the development of quantum algorithms and software:

·      The architecture of the platform, made up of two large modules:

o      QPath Core, which provide a powerful platform of tools, services, and APIs for the creation of technology-agnostic quantum solutions.

o      QPath Apps to develop high-quality quantum algorithms & apps that support Software Engineering and Programming best practices adoption.

·      The most remarkable capabilities of QuantumPath® today, the result of seamlessly integrating the most advanced in quantum computing and Quantum Software Engineering into a platform designed to accelerate practical quantum computing.

·      The great current advantages that QuantumPath® brings to those who want to develop high-quality quantum software

This ecosystem allows to accelerate quantum software development and manage its lifecycle, from quantum algorithm creation through development, testing and deployment, to deployment and reuse; it is a platform completely designed to provide a path for faster adoption of quantum technologies. The quantum software developer visually designs the quantum algorithms with Q Assets Compositor® (both quantum circuits and annealing model definitions, see Figure 3) leaving the underlying details of the system (from the model to the results), as the lifecycle is fully automatic [7].

Figure 3. Graphical interfaces of QuantumPath®


QuantumPath® is agnostic with respect to quantum programming languages and technologies, allowing to choose as execution target quantum (annealing and gate-based) computers and simulators, guided by fully automatic processes, supported by all the stored telemetry. In this way, quantum development teams can focus on the knowledge and leave the details to the platform.

QuantumPath® embraces and promotes the coexistence of classical and quantum computing, through its qSOA® architecture, via ConnectionPoints and its open standards-based protocol. Moreover, the modular design of the platform supports third-party solutions as plugins, through its control pipelines. This means that future quantum technologies can be effortlessly added as new layers, enhancing your quantum solutions.

Moreover, QuantumPath® is the only platform specifically conceived from a quantum software engineering perspective [8], incorporating a series of components (platform apps) that support the management of quantum software development projects, support quantum software evolution (software re-engineering/modernization processes, include different techniques for quantum software testing, ensure quantum software quality, promote quantum software reuse, and facilitate quantum software governance and management.


QHealth Architecture: QuantumPath® & Amazon Braket

 One of the most critical challenges of the QHealth project is focused on the way in which the system must solve the problem that arises: The need to carry out a medical analysis on a huge number of variables to be analyzed, and how to develop the quantum computation necessary to tacklethe complexities associated with that analysis. The solution to this problem of QHealth is approached, fundamentally, through the quantum annealing approach.

·       But this same challenge opens up new scopes in the design of the system itself. The first architectural decision was: A hybrid architecture where a classical part performs data management tasks and a quantum part performs raw processing tasks for the combinatorics.  QuantumPath provides the appropriate tools to simplify the construction of hybrid systems independent of the quantum provider technology: reducing to a minimum the work required to create the hybrid connector system that makes possible the exploitation of quantum computing in almost all its aspects.

The second important architectural decision was related to another fundamental challenge: the way in which the classical environment is integrated with the quantum one. The QHealth project (Figure 4) will provide:

·       a graph composition interface, through which the classical system will be able to define the necessary structures of knowledge

·       an interaction engine with the QuantumPath API qSOA® that will allow the dynamic generation of agnostic circuits to launch against the quantum provider that best suits the needs of the system. In this case, AWS Braket resources and its D-Wave connectors have been preselected. Thanks to AWS Braket it is really feasible to be able to launch experiments against the D-Wave solver so that an additional layer of reinforcement is available to QuantumPath. But it is not limited to Braket. QuantumPath® agnostic capability supports direct connection to annealing computers, regardless of manufacturer. If for some reason it is necessary to access another annealing computer, for example, QuantumPath can continue to connect directly against the D-WAVE hardware, or with any other compatible technology provider. Without affecting the project and the developed algorithm at all.

·       provides monetization information based on pay-per-use slots in case the quantum hardware service is paid for.

* Provides detailed log information of jobs and executions with which to corroborate those generated by the platform.


Figure 4. QuantumPath® on AWS and other Annealing providers


Figures 5 – 8 provides some screens showing the QuantumPath® lifecycle processes for the development of the QHealth Project with AWS.

Figure 5. QuantumPath® solutions creation

Figure 6. Agnostic executions connected to Amazon Braket

Figure 7. Formulation of annealing using Q Assets Compositor®


As we know, the 4th industrial revolution, which comes from nanotechnology, biotechnology, genomics, and quantum computing, has already begun. In fact, one of the sectors where the potential of quantum technologies can be most exploited is the medical and health sector.

We are concerned that software engineering best practices should not be forgotten, and that they should be incorporated into development platforms to ensure the quality of quantum information systems [9]. These issues are even more relevant for applications related to health and medicine.

This blog shows how the combination of Amazon Braket and QuantumPath represents an excellent environment for institutions, companies, and professionals to accelerate the adoption of real-world quality quantum algorithm and software development. Also, for already existing quantum software development teams, thanks to the hybrid quantum software engineering and lifecycle application functionalities, allowing the optimization of their software development processes, the increasing in productivity, and securing their investments in quantum software development, being more competitive in the emerging quantum software business for practical quantum computing.

QuantumPath® can be accessed through four types of subscriptions online, including a free on QPath® Free Developer subscription.

The content and opinions in this post are those of the authors and AWS is not responsible for the content or accuracy of this post.

[1] The Disruptive Power of Quantum Computing in Precision Medicine, I. Vasiliu-Feltes -July 21, 2020, Medicine, 

[2] Panomics for Precision Medicine. C. Sandhu, A. Qureshi, and A. Emili. Trends Mol Med. 2018 24(1): 85–101, 

[3] Para un sistema más eficiente hay que incluir biomarcadores en la cartera de servicios. A. Llerena.

[4] Interview with Dr. Adrian Llerena.

[5] Quantum Computing. J. L. Hevia, G. Peterssen, C. Ebert, M. Piattini. IEEE Software. 38(5): 7-15 (2021).

[6] QuantumPath: A quantum software development platform, J. L. Hevia, G. Peterssen and M. Piattini Journal of Software: Practice and Experience.

[7] A New Path to Create Solutions for Quantum Annealing Problems. J.L. Hevia, E. Murina, G. Peterssen and M. Piattini. Journal of Quantum Information Science, 11 (3), 112-125. September 2021.

[8] The Talavera Manifesto for Quantum Software Engineering and Programming.

[9] Towards a quantum software engineering. M., Piattini, M. Serrano, R. Pérez-Castillo, G. Peterssen, and J. L. Hevia. IT Professional, vol. 23, no. 1, pp. 62–66, Jan.-Feb. 2021. doi: 10.1109/MITP.2020.3019522.