Quantum-Si to Develop Acceleration Platform and Advance Core Technologies in Collaboration with NVIDIA
This collaboration aims to significantly enhance data processing speeds to handle the increased data volume from Proteus. By leveraging NVIDIA accelerated computing and Quantum-Si’s advanced single-molecule protein sequencing and detection technologies, the collaboration aims to develop a robust data processing system. This system will keep pace with Quantum-Si’s rapidly evolving technology advancements, generating single-molecule protein insights to deepen our understanding of the proteome and develop innovative solutions through research, drug discovery, and healthcare AI.
“We are thrilled to collaborate with NVIDIA to make single-molecule proteomics more accessible to researchers,” said John Vieceli, Ph.D., Chief Product Officer of Quantum-Si. We have been leveraging AI protein structure prediction tools with, both in the cloud and on-premises to design new and improved biomolecules. Now, we are excited to apply NVIDIA technology for downstream data processing and interpretation applications for Proteus.
“Sequencing proteins to derive insights requires advanced processing capabilities that can handle vast volumes of data,” said George Vacek, Global Head of Genomics Alliances at NVIDIA. “Applying NVIDIA technology for accelerated computing and AI, Quantum-Si’s platform for proteomics and multi-omics can make a significant impact on healthcare and life sciences AI and research.”
Infineon and Quantinuum Announce Partnership to Accelerate Quantum Computing
Infineon innovates with a dedicated team to make their trapped-ion quantum processing units (QPUs) the heart of the leading quantum computers. The company has invested in this field since 2017, applying its expertise in high-volume processing technologies and developing technologies, like integrated photonics and control electronics, to enable their partners to scale the qubit count of their machines.
In Quantinuum’s hardware approach, charged atoms are trapped with electromagnetic fields so they can be manipulated and encoded with information using microwave signals and lasers. This design has distinct advantages over other quantum hardware, including higher fidelities and longer coherence times.
This collaboration builds on today’s leading performance of Quantinuum’s trapped-ion quantum computers, which currently hold the world records in key performance benchmarks such as 2-qubit gate fidelity, quantum volume and cross-entropy benchmark fidelity. To deliver even better fidelity at greater scale and achieve commercial advantage, larger and more sophisticated ion traps are needed. Engineers from the two companies have been working together for more than a year and will intensify their efforts under the current partnership to develop powerful ion traps for Quantinuum’s next-generation quantum computers.
Quantum Circuits Accelerates Momentum Toward Commercial Quantum Computing With New Aqumen Seeker Quantum Processing Unit
The highly efficient 8-qubit quantum processor, called Aqumen Seeker, follows the company’s software announcement three months ago of its quantum cloud service, software development kit, and simulator to build and test quantum applications before they run on production hardware. That announcement foreshadowed Quantum Circuits’ forthcoming hardware, which is now being used by enterprise customers with the software as a full-stack system.
Quantum Circuits’ error-detecting dual-rail qubits follow the company’s innovation philosophy that errors must be corrected first to avoid disrupting performance at scale. They evolve beyond single-qubit approaches that attempt to scale first, then correct along the way, overcoming inherent challenges of brute-force performance requirements, inefficiency, and limited scale. Quantum Circuits ensures that qubit performance is carefully analyzed and understood before they are released, resulting in more scalability with less qubits required.
Quantum Circuits’ dual-rail qubits incorporate the industry’s only combination of quantum error detection (QED), error detection handling (EDH), and real-time control flow (RTCF). RTCF and EDH enhance tools that programmers use to explore and create algorithms with Quantum Circuits’ dual-rail qubits. Together, these features significantly enhance the performance of the quantum system, enabling algorithms to run efficiently with greater scale, fidelity, and reliability.
Alice & Bob Supercharge Quantum Simulations with Dynamiqs by Integrating with Accelerated Computing
Alice & Bob, a global leader in the race for fault-tolerant quantum computing, has announced the release of Dynamiqs, Alice & Bob’s pioneering quantum simulation library. Dynamiqs integrates NVIDIA accelerated computing, driving quantum simulations beyond state-of-the-art levels of performance and setting the stage for major breakthroughs in quantum research.
The Alice & Bob research team recognized the need for a disruption in the quantum simulation space, leading to the creation of Dynamiqs. From the beginning of development, it was clear that GPUs could provide huge benefits and accelerate simulations at scale. Based on this observation, the team developed Dynamiqs based on the latest machine learning library developed by Google AI, JAX, and on Diffrax, a state-of-the-art library for differential equations. The NVIDIA-powered GPU acceleration increases the efficiency of matrix operations—a critical factor in quantum simulations—by up to 60x, allowing Dynamiqs to simulate large, complex systems such as Quantum Processing Units (QPUs) that involve multiple qubits and physical hardware components like resonators. Dynamiqs underscores Alice & Bob’s core business as QPU developers and cements the company’s role in the quantum computing industry.
Dynamiqs will enable rapid and efficient quantum simulations, which are notoriously complex, particularly when dealing with large Hilbert spaces, open systems interacting with their environments, and fast time-dependent dynamics. These challenges often push conventional computing resources to their limits, resulting in slow performance and inefficient scaling. Dynamiqs is an open-source Python library sponsored by Alice & Bob, with collaboration from Inria and the University of Sherbrooke, that enables high-speed simulation of open and closed quantum systems leveraging NVIDIA accelerated computing.
Algorithmiq Leverages NVIDIA Supercomputing to Advance Quantum Computing
Early tests suggest that NVIDIA-accelerated supercomputing could enhance the performance of Algorithmiq’s Tensor Network Error Mitigation (TEM) by up to 300x compared to its initial implementation. By leveraging NVIDIA’s computing capabilities, Algorithmiq aims to improve the performance and reliability of near-term quantum devices, advancing the industry’s efforts toward practical quantum applications.
Algorithmiq’s integration of NVIDIA-accelerated supercomputing is expected to yield initial results by Q2 2025. The company plans to showcase its powerful applications of GPU-enabled quantum computing solutions at upcoming industry events. These advancements are anticipated to pave the way for applications across multiple industries, from finance and healthcare to materials science and cryptography.
