The collaboration between Algorithmiq and Microsoft will integrate Algorithmiq’s advanced simulation and measurement methods with Microsoft’s Quantum platform and Quantum Development Kit (QDK), enabling the next generation of cloud-deliverable quantum solutions for chemical sciences from drug discovery to materials design.
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Algorithmiq, in collaboration with IBM, Flatiron Institute, EPFL, and the University of Ljubljana, runs an experiment on an IBM Heron processor modeling operator dynamics in heterogeneous quantum materials, revealing information-flow patterns beyond the reach of classical computation.
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Milestone experiment will appear in the first public tracker of quantum advantage, evolving quantum progress from unsubstantiated claims to an open benchmark system overseen by the academic community.
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Quantum AI is an emerging field at the intersection of quantum computing and artificial intelligence (AI)—two of the most transformative technologies of our time. Bringing the two together is not just theoretical curiosity; it opens new possibilities for solving complex problems at the edge of what classical computers can handle.
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In the last few years, quantum computing has emerged as a powerful tool with the potential to significantly change numerous industries, with life sciences and healthcare at the forefront [1]. But what are the applications of quantum computing in life sciences? In this post, we will explore the most commonly asked questions about quantum computing for life sciences and provide some answers on the most recent developments in the field.
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What is quantum chemistry? Quantum chemistry is a field in which quantum mechanics is used to understand chemical systems. A central problem is solving the Electronic Structure Problem, defined as finding stationary states of the non-relativistic time-independent electronic Schrödinger equation. In the so-called Born-Oppenheimer approximation electron-electron as well as electron-nuclei…
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Algorithmiq announces a venture to develop NVIDIA-accelerated supercomputing-enabled error mitigation techniques for near-term quantum devices, aiming to accelerate the achievement of quantum advantage.
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Algorithmiq’s breakthrough error mitigation algorithm, TEM, is now commercially available on IBM’s Qiskit Functions Catalog.
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Algorithmiq’s Tensor-network Error Mitigation (TEM) is a hybrid quantum-classical method for reducing noise on near-term quantum devices. By post-processing informationally complete measurements with tensor networks, TEM improves accuracy without additional quantum circuits. Its favorable scaling and reduced classical cost make it a practical path toward more reliable quantum computations.
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