TangleLab Pairs Quantum Processors With Existing Supercomputers

A $5 million grant from the U.S. National Science Foundation is funding the creation of TangleLab, a new national testbed for hybrid quantum-classical computing led by the Pittsburgh Supercomputing Center. Rigetti Computing and Hewlett Packard Enterprise are partnering in the project, which will give researchers and students hands-on access to an integrated system designed to explore the future of scientific computing. “Carnegie Mellon has long been a leader in advances in computing, and through the Pittsburgh Supercomputing Center we continue to invest in the infrastructure that enables the next generation of discovery,” said Theresa Mayer, vice president for research. TangleLab aims to define how quantum and classical computers will work together, while also preparing a workforce equipped to utilize these emerging technologies.

TangleLab: Integrating Quantum Technologies with High-Performance Computing

A $5 million grant from the U.S. National Science Foundation is establishing TangleLab, a national testbed designed to explore the integration of quantum computing with existing high-performance computing infrastructure. This investment signals a clear federal commitment to hybrid quantum-classical systems. Led by the Pittsburgh Supercomputing Center, TangleLab will not deploy a standalone quantum computer, but instead focus on creating an open platform for researchers and educators to investigate how quantum processors can function as specialized resources within broader computing environments. This approach acknowledges that quantum computers are not intended to replace classical systems entirely, but rather to augment them for specific, computationally intensive tasks.

The platform, developed in partnership with Hewlett Packard Enterprise and Rigetti Computing, aims to address fundamental questions about the design, programming, management, and optimization of these hybrid systems. “With TangleLab, we are really looking into integrating quantum technologies into classical high-performance computing environments to enable hybrid quantum-classical workflows that can demonstrate potential quantum utility,” said Bruno Abreu, deputy scientific director at PSC. Unlike classical computers utilizing bits, quantum processors employ qubits capable of existing in multiple states simultaneously, a property called superposition, potentially offering advantages for certain problem types. TangleLab builds upon the Pittsburgh Supercomputing Center’s decades-long experience in heterogeneous computing, having combined multiple processor types since 1991. Carnegie Mellon University views the project as a key investment in the future of scientific discovery.

The center anticipates beginning construction on September 1, 2026, with full operations projected for 2027, and will offer access through a competitive proposal process alongside training and consulting services to ensure broad usability. James Barr von Oehsen, PSC’s executive director, emphasized that TangleLab will.

Carnegie Mellon has long been at the forefront of advances in computing, and through the Pittsburgh Supercomputing Center we continue to invest in the infrastructure that enables the next generation of discovery.

Theresa Mayer, vice president for research

The convergence of quantum and classical computing is no longer a distant prospect; researchers are actively building systems to integrate these disparate technologies, recognizing that quantum processors will likely function as specialized co-processors rather than outright replacements for existing infrastructure. This collaboration brings together expertise in high-performance computing infrastructure and quantum hardware development, essential for constructing a functional and accessible testbed. The platform’s design acknowledges the current limitations of quantum technology; scientists are still determining where quantum computers offer a meaningful advantage over classical systems. Researchers will access TangleLab through a competitive proposal process, with the system supporting both dedicated research and educational use, and PSC will offer training and consulting to users of all experience levels. Construction is slated to begin September 1, 2026, with full operations anticipated in 2027.

With TangleLab, we are really looking into integrating quantum technologies into classical high-performance computing environments to enable hybrid quantum-classical workflows that can display flavors of quantum utility.

Bruno Abreu, deputy scientific director at PSC

The Pittsburgh Supercomputing Center (PSC) is expanding its role as a national hub for advanced computing with the forthcoming TangleLab, a $5 million initiative funded by the U.S. National Science Foundation. TangleLab’s development also complements a broader ecosystem of quantum research already thriving at Carnegie Mellon. The Pittsburgh Quantum Institute, a collaborative effort involving Carnegie Mellon, the University of Pittsburgh, and Duquesne University, supports research, education, and training in quantum information science. The university’s NSF-supported Center for Quantum Computing and Information Technologies fosters collaboration between researchers, students, and industry partners to explore practical quantum applications.

TangleLab continues PSC’s long tradition of making next-generation computing technologies accessible to the broader research and education community.

Barr von Oehsen, PSC’s executive director and principal investigator for TangleLab
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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