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SISSA launches JQuant, a journal for quantum information and physics

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October 28, 2026 by Ivy Delaney

SISSA has launched JQuant, a new open-access journal entering the field of quantum science with an initial issue of five papers. The journal focuses on the intersection of quantum information and fundamental physics, aiming to encourage interdisciplinary research. The inaugural issue immediately establishes JQuant’s credibility by featuring contributions from J. Ignacio Cirac, Barbara Kraus, John Preskill and Nobel laureate Giorgio Parisi, all internationally recognized researchers. SISSA Medialab will manage the journal’s editorial processes, building on its existing portfolio of six other scientific publications.

SISSA Launches JQuant: An Open-Access Quantum Science Journal

The new open-access publication aims to encourage interdisciplinary research directions within quantum science, addressing a need for dedicated forums in the rapidly growing field. SISSA Medialab manages the journal’s editorial processes, using its experience with six other scientific journals already in production. These internationally recognized researchers have all received prestigious scientific awards, lending immediate credibility to the new venture.

JQuant’s scope encompasses theoretical research in areas including quantum computation, sensing and many-body physics, with the journal positioned as a non-profit initiative. SISSA stated that this is the journal for quantum science, highlighting its intention to become a leading voice in the field.

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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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