Q-Ctrl and Classiq collaborate to help developers create faster and more efficient quantum algorithms

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Much of the excitement surrounding quantum computers comes from the ability to use quantum algorithms to solve problems that are retractable with standard or “classical” computers. Several notable examples of quantum speedups have been discovered, particularly with applications to cryptography and chemistry.

Despite these exciting applications, quantum computers are no panacea for computing; for some problems, quantum algorithms provide no or only a minor benefit. In its current state of development, quantum computing is extremely sensitive to noise and disruptive factors in the environment. This makes quantum computing “noisy,” as quantum bits (or qubits) lose information when they go out of sync, a process known as decoherence.

Q-Ctrl error suppression for better quantum algorithms

To provide a means to support the development of quantum technology, Sydney-based Q-Ctrl offers quantum control infrastructure software, dubbed Fire Opal, for R&D professionals and quantum computing end-users. Fire Opal links intermediate representations, such as QASM, to hardware-specific instructions with built-in deterministic error suppression techniques designed to minimize noise and decoherence and maximize algorithmic success when executed in hardware. This foundational technology also applies to a new generation of quantum sensors and enables Q-Ctrl to shape and underpin every application of quantum technology.

Since 2018, Q-Ctrl has been an inaugural member of IBM Quantum Startup and is currently supported by Square Peg Capital, Sierra Ventures, Sequoia Capital China, Data Collective, Horizons Ventures, Main Sequence Ventures, In-Q-Tel, Airbus Venture and Ridgeline partners.

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Automate and simplify quantum algorithms with Classiq

Most recently, Q-Ctrl announced a partnership with Tel Aviv-based quantum algorithm development software startup Classiq to provide an end-to-end platform for designing, running, and analyzing quantum algorithms. The Classiq platform automatically creates optimized quantum circuits in an intermediate representation from high-level functional models. Specifically, the Classiq solution enables any algorithm developer to work with high-level functional descriptions of algorithms and produce complete quantum circuits, automating and simplifying the difficult process of creating quantum algorithms.

The new partnership will integrate Classiq’s Quantum Algorithm Design platform with Q-Ctrl quantum control techniques designed to improve hardware performance. The integration will provide a single interface where developers can use both solutions to create algorithms and run them with high-performance error reduction techniques, streamlining the end-to-end process for obtaining useful information from quantum computers. The technologies being developed by both companies aim to reduce the need for users to have deep quantum computing expertise, allowing developers to focus on the domain-specific applications that matter most to them.

“The goal of this integration is to provide the utility and benefits of quantum computing to an expanded base of algorithm developers in business and academia. Classiq will be responsible for its integration with its software tools, leveraging the support of Q-Ctrl,” Q-Ctrl Chief Strategy Officer Aravind Ratnam told VentureBeat.

Bringing it all together to help developers without quantum skills

Users will be able to develop on the Classiq platform by incorporating Q-Ctrl’s hardware enhancement tools “under the hood”. Classiq’s Quantum Algorithm Design platform can take high-level functional models and transform them into quantum circuits, which will be automatically optimized by Q-Ctrl’s infrastructure software. These tools are both hardware agnostic, meaning they can run on a wide range of quantum hardware vendors.

An add-on to the Q-Ctrl offering is its education technology platform, dubbed Black Opal. As an optional extension of the solution integration, Black Opal will be offered to customers for their education and training via the Classiq portal. Future extensions could include the joint development of a Black Opal Skills Path explicitly focused on education for the Classiq platform.

“Classiq and Q-Ctrl provided a natural fit as we quickly saw how our solutions addressed different parts of a developer’s workflow,” said Ratnam. “There are many developers who see algorithm design as a major barrier to using our tools, so our combined toolset makes it more accessible and easier to get started. Putting our solutions together made natural sense.

At present, the integrated offering will be available to customers directly through the Classiq platform. Initially Classiq and Q-Ctrl will jointly engage with enterprise customers (regardless of which side the customer takes), to learn about use cases and get feedback. Classiq and Q-Ctrl will evaluate solution components (IP, time, and material) based on the nature of the use case, the complexity of the integration, and the customer context.

Customers can use these ready-to-use tools to get more out of today’s hardware and build quantum algorithms to solve some of the world’s toughest computational problems, such as finance, quantum machine learning for drug discovery, and optimization problems for logistics .

“Classiq’s algorithm design tools lower the barriers for developers to create quantum circuits, and Q-Ctrl’s error correction tools will help them get the best algorithm performance out of quantum hardware, which is often subject to noise and errors,” Ratnam said. “Together, we aim to expand access to quantum algorithm design, error correction and optimization tools to a broader base of developers, with or without deep quantum expertise.”

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