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Much of the excitement surrounding quantum computers stems from the possibility of using quantum algorithms to solve problems that are intractable 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 not a panacea for computation; for some problems, quantum algorithms provide no advantage or only a minor one. 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 become out of sync, a process known as decoherence. 

Error suppression from Q-Ctrl for better quantum algorithms

To provide a medium to bolster quantum technology development, Sydney-based Q-Ctrl offers quantum control infrastructure software, dubbed Fire Opal, for R&D professionals and quantum computing end users. Fire Opal connects intermediate representations, such as QASM, to hardware-specific instructions with embedded deterministic error suppression techniques designed to minimize noise and decoherence and maximize algorithmic success when executed on 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 the IBM Quantum Startup, and is currently backed by Square Peg Capital, Sierra Ventures, Sequoia Capital China, Data Collective, Horizons Ventures, Main Sequence Ventures, In-Q-Tel, Airbus Ventures and Ridgeline Partners. 

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Automating and simplifying 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, executing 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 output 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’s quantum control techniques designed to boost 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, simplifying the end-to-end process of getting useful insights from quantum computers. The technologies developed by both companies aim to reduce the need for users to possess deep quantum computing expertise, allowing developers to focus on domain-specific applications that matter most to them. 

“The objective of this integration is to deliver the utility and benefits of quantum computing to an expanded base of algorithm developers in enterprise and academia.  Classiq will be responsible for the relevant integration with its software tools, leveraging support from Q-Ctrl,” Aravind Ratnam, chief strategy officer at Q-Ctrl, told VentureBeat. 

Bringing it all together to help developers without quantum expertise

Users will be able to develop in Classiq’s platform while incorporating Q-Ctrl’s hardware-enhancing 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 providers. 

An additional component of the Q-Ctrl offering is its education technology platform, dubbed Black Opal. As an optional extension to the solution integration, Black Opal will be offered to clients for their own education and training via the Classiq portal. Future extensions may include the co-development of a Black Opal skill pathway focused explicitly on educating for the Classiq platform. 

“Classiq and Q-Ctrl provided a natural fit as we immediately 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 that more accessible and easy to get started. Stitching our solutions together made natural sense.” 

As things stand, the integrated offering will be available to customers directly through the Classiq platform. Initially Classiq and Q-Ctrl will jointly engage with enterprise customers (irrespective of which party brings in the customer), to learn about use cases and obtain feedback. Classiq and Q-Ctrl will price their solution components (IP, time and material) based on the nature of the use case, integration complexity and customer context.  

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

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

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