Helsinki, September 16, 2024 -- Algorithmiq, a team pioneering the integration of quantum computing, artificial intelligence (AI) and Network Science to solve complex problems in the Healthcare and Life Sciences, has today announced the commercial availability of its revolutionary Tensor Network Error Mitigation (TEM) solution, via IBM’s newly launched Qiskit Functions Catalog.

Leveraging the properties of tensor networks, Algorithmiq’s TEM is now available to the 250+ Fortune 500 companies, universities, laboratories, and startups in the IBM Quantum Network. TEM allows developers, researchers, and computational and data scientists to manage noise in software post-processing while lowering quantum processing unit (QPU) usage. This combination allows for the programming of quantum computers with greater ease and simplicity, ultimately reducing the time needed to build and execute new workflows.

Noise mitigation strategies are crucial for improving the utility of near-term quantum devices. While these algorithms can course-correct for a certain period, they’re prone to diminishing returns, particularly as the problem size and number of qubits increase. Algorithmiq’s Tensor Network Error Mitigation (TEM) method is a hybrid quantum-classical algorithm designed for performing noise mitigation entirely at the classical post-processing stage. TEM is also designed to integrate with error correction techniques to help extend the scale and accuracy of quantum simulations, the combination of which will become increasingly relevant, as quantum processors increasingly become more sophisticated.

Since its establishment in 2020 by co-founders Sabrina Maniscalco, Guillermo García-Pérez, Matteo Rossi, and Boris Sokolov, Algorithmiq has aimed to push the boundaries of what is possible in error mitigation for quantum computing. The company’s approach to scalable error mitigation continues to pave the way toward true quantum value.

With the application of tensor networks in the post-processing stage of quantum computing execution, Algorithmiq’s Tensor Network Error Mitigation (TEM) has achieved levels of error mitigation never before witnessed without the need for additional quantum circuits. This optimized error cancellation with minimal use of the quantum hardware enables access to utility-scale quantum experiments.

Late last year, in collaboration with Trinity College Dublin and IBM, Algorithmiq demonstrated the utility of TEM, by successfully running one of the largest scale error mitigation experiments to date on IBM’s hardware, achieving 2,402 entangling gates. Six months later, detailed in a paper soon to be published on the arXiv, the Algorithmiq team has now pushed the boundaries even further, successfully conducting an experiment that sees the use of 91 active qubits x 91 layers of entangling gates, resulting in a total of 4,095 entangling gates.