Category: Blog

QCentroid and OpenQuantum Partner to Democratise Access to Quantum Computing Solutions

Norwich, United Kingdom – 15 May – QCentroid, a leading Quantum-as-a-Service company, and OpenQuantum, a pioneer in open-source quantum computing hardware and software solutions, today announced a strategic partnership to make quantum computing more accessible, affordable, and faster to develop applications for.

This collaboration seeks to break down the barriers to entry that have traditionally hindered the progress needed for widespread adoption of quantum computing. By combining QCentroid’s user-friendly platform with OpenQuantum’s commitment to open-source technology, the partnership will provide an end-to-end, fully integrated experience for users of all levels.

“We are excited to partner with OpenQuantum to further our mission of democratising access to quantum computing,” said Carlos Kuchkovsky, CEO of QCentroid. “Their dedication to open-source technology aligns perfectly with our vision of a future where quantum solutions are readily available to everyone. This partnership will empower businesses and researchers to explore the potential of quantum computing without the burden of complex infrastructure or exorbitant costs.”

The partnership will deliver several key benefits:

  • Simplified Access:  QCentroid’s SaaS offering will seamlessly integrate with OpenQuantum’s hardware and software, offering users a streamlined experience to access and utilise quantum resources.
  • Open-Source Advantage: The utilisation of open-source tools fosters transparency, collaboration, and rapid innovation within the quantum ecosystem.
  • Accelerated Application Development: Using existing code and APIs with standardized open-source hardware and firmware can accelerate application development and help translate theoretical concepts into real-world solutions faster.
  • Cost-Effectiveness:  By leveraging OpenQuantum’s affordable foundational platform, the partnership will significantly reduce the cost barrier associated with quantum computing, making it more accessible to a wider audience.
  • End-to-End Solution: From accessing quantum computers to developing and deploying quantum algorithms, the collaboration provides a comprehensive solution for all quantum computing needs.

“This is an exciting development for the quantum computing industry,” said Simon Muskett, Co-Founder of OpenQuantum. “By partnering with QCentroid, we are bringing the power of the standardised, open quantum computing stack to a much wider audience of organisations providing them with the resources they need to harness the power of quantum and develop groundbreaking applications faster.”

This partnership marks a significant step forward in the evolution of quantum computing, paving the way for wider adoption and unlocking its transformative potential across various industries. 

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About QCentroid

QCentroid Labs is a leading Quantum Computing company offering Quantum-as-a-Service solutions, optimising for business efficiency and net zero goals at the same time. By hiding the quantum complexity and empowering users with intuitive AI tools and clear workflow, QCentroid is transforming the way quantum experimentation is done, from months to weeks, enabling companies to get to utility and their net zero objectives faster.

About OpenQuantum 

OpenQuantum is bringing the power of open source to quantum computing. With the support of a vibrant community of innovators and users, OpenQuantum is driving the development of open-source tools and customised quantum computing solutions that make this crucial technology more accessible and speed up scientific research.

Quantum Computing: A Game-Changer for Environmental Solutions and Reducing Carbon Footprint

Harnessing Quantum Innovation for Sustainable Earth

Quantum computing has emerged as a breakthrough technology with the potential to revolutionize various fields and environmental science is one of them. This post is all about how quantum computing can aid in tackling complex environmental challenges and assesses its overall carbon footprint, offering a glimpse into a more sustainable future.

The potential of quantum computing has been well documented [cite some source]allowing it to. This capability may be particularly beneficial in environmental research, where complex systems and complex datasets are common. Here are some ways quantum computing has the potential to make a difference:

  • Climate Modeling: Traditional computers struggle with the vast complexity of climate systems. Quantum computers may be able to model these systems more accurately, predicting weather patterns and climate changes with greater precision. This may lead to better-informed decisions in environmental policy and disaster management.
  • Biodiversity Analysis: Quantum computing can aid in analyzing complex ecological data, helping scientists understand species distribution, genetic diversity, and ecosystem dynamics. This understanding is crucial for conservation efforts and for maintaining the balance of our ecosystems.
  • Pollution Control: By efficiently analyzing data on pollutants, quantum computers may be able to assist in developing more effective pollution control strategies. This includes identifying sources of pollution, predicting dispersion patterns, and devising optimal mitigation measures.

While quantum computing can provide significant value  for environmental science, it is also important to consider its carbon footprint. Additionally, the efficiency gains in various sectors due to quantum computing could far outweigh its own carbon footprint. For instance, optimizing renewable energy grids or developing new materials for better carbon capture could have a significant positive environmental impact.

Quantum computing holds great promise for addressing complex environmental problems. Its ability to analyze large datasets and model intricate systems can lead to more effective and sustainable solutions. While the technology itself is energy-intensive, ongoing improvements and its potential to optimize other sectors present a hopeful scenario for its carbon footprint. Embracing quantum computing could be a significant step forward in our quest for environmental sustainability.

Connecting Quantum computing to Web3. A tech approach.

No-code quantum algorithms execution with the QCentroid Platform

In this article we’re going to go through the requirements, the challenges and the solutions adopted to connect the Ethereum blockchain to the QCentroid Quantum Solutions Platform.

Due to the nature of blockchain ecosystems, accessing off-chain data from on-chain smart contracts is not natively possible. However, oracles like Chainlink provide this bridge between on-chain and off-chain data. Oracles enable smart contracts to retrieve data from the outside world.

Architecture

The QCentroid Platform is accessible through an authenticated REST API, just like almost any other platform out there.

To access our platform from the blockchain, we decided to use ChainLink oracle platform.

Due to the nature of quantum computing, the QCentroid platform works in an asynchronous mode, which means that the result of a computation request is not returned right away in the response. Instead, a Job Id is returned to be used later to check the job status and to obtain the result.

This asynchronous behavior adds a layer of complexity to the whole workflow from the smart contract, through the oracle, to the Quantum Platform and back.

But, before we enter in detail into this workflow, let’s see the architecture that we have deployed and all the components that we need.

This diagram shows all the components involved:

  • The user’s smart contract
  • The QCentroid Quantum provider smart contract
  • ChainLink Oracle
  • A ChainLink node
  • The External Adapters
  • The QCentroid Platform
  • ChainLink keepers

Depending on your application, you will not always need all these components. In our case, I would like to highlight four of these components and why we decided to deploy, run and operate them ourselves.

The QCentroid provider smart contract simplifies the access to the oracle to the final users. A final user smart contract only needs a call to this provider contract and a callback function  instead of extending the Chainlink client contract and the use of LINK tokens, oracle address and job ids. This provider smart contract manages all this.

If you need to use authentication to access an API you are going to need an External Adapter. This may already exist if you’re accessing a well known API, such as GitHub or Twitter, but if you are trying to connect to your own API or to a not so common API, you’ll have to build your own adapter.

Becoming a ChainLink node operator is not a trivial task, it’s a job itself. In our case, our own ChainLink node is needed to control the access to the Platform API. As the credentials to access the API are managed at the External Adapter, now we need a mechanism to control the access to this external adapter. The way to do this in the ChainLink platform is through address whitelisting at node level. This is why we decided to run and operate our own ChainLink node to be able to manage the list of approved addresses.

ChainLink Keepers provide decentralized and highly reliable smart contract automation. Due to the asynchronous nature of the QCentroid Quantum Platform we make use of ChainLink Keepers to poll the status of the ongoing jobs and fetch the result whenever it is ready.

These four components may or may not be needed by your application, but now you know the role they play in the ecosystem and you can decide whether you need them or not. 

For most of these components you’ll find tons of information and tutorials on how to build and deploy them. Here are the resources that we used at QCentroid to build our ecosystem.

To start building our own External Adapter, we used the Chainlink NodeJS External Adapter Template by Thomas as a starting point. This adapter is written in NodeJS and allows you to run it locally, as a Docker service or serverless (using AWS Lambda for example).
The API-specific values are configured as environment variables, so they are not hardcoded and they can be easily filled by your CI/CD workflow.

Building and running your own ChainLink Node for development purposes is also a relatively simple task relying on the resources shared with us by ChainLink, like the ChainLink SmartContract Kit. There you’ll find step-by-step instructions on how to build and run a ChainLink node. Setting up the node for operational purposes is where things start to get complicated and would be subject for another article. 

Workflow

Now that we know what all these elements are, what they are for and how to build and run them, let’s have a look at a full workflow, from the initial request from a user’s smart contract to the reception of the solution from the Platform.

This workflow shows the different components described earlier and how and when they are involved in the process and they’re role.

It’s important to mention that our main objective when designing this architecture was to simplify as much as possible the work needed by our smart contract end users. The diagram clearly shows how few “arrows” are needed on the left and how our users only need to focus on their business. As the workflow moves to the right, we can see how the components involved in the architecture handle the tedious tasks such as authentication or polling for the results.

For our end users, using Quantum computing from a smart contract is as easy as: “here is my data, here is your result”.

With this article, we wanted to show our specific use case of a hybrid on-chain/off-chain architecture and how we’ve approached the connection between these two worlds using ChainLink’s breakthrough oracle technology.

Effortlessly integrate Quantum Hardware, execute Quantum Algorithms, and compare results with our advanced tools.

  • Easily upload and execute quantum algorithms on a wide range of quantum hardware.
  • Seamless integration with existing IT systems through APIs, SDKs and Smart contracts.
  • Comprehensive monitoring and analysis tools to optimize algorithm performance and cost.
  • Pay-as-you-go pricing model for flexibility and cost-effectiveness.
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