Open Access
Journal of Physics Process
Submit your manuscript and receive a structured peer review from a panel of AI reviewers. Final editorial decisions are made by the Editor-in-Chief.
Submit
Upload your PDF or Word manuscript and provide metadata.
AI peer review
A scientific rigor reviewer plus a format & language editor assess each submission.
Editorial decision
The Editor-in-Chief reviews the reports and makes the final call.
Publish
Accepted articles appear in a volume & issue, with a DOI and a PDF.
Reviewer reports are generated by AI and are advisory; editorial decisions remain with the human Editor-in-Chief.
Latest & trending research
Newest preprints across physics — arXiv
An entangling Hubbard quasiparticle collider on a digital quantum processor
Chan Bin Bark, Hanbyul Kim, Yoonil Koo, SeungBeom Hong
Scattering of quasiparticles provides direct access to the interactions that govern strongly correlated systems. In this work, we realize the quasiparticle scattering experiment of…
A Warped Born Rule Turns an Observer into a Maxwell's Demon
Olumakinde Ogunnaike
Quantum mechanics seems to be an incredibly delicate theory. Slight alterations to nearly any part of its structure can lead to huge qualitative differences and wildly unphysical…
A scalable sample-based quantum diagonalization solver for general Pauli-sum Hamiltonians
Yutaro Iiyama
A scalable ground-state solver to be used in sample-based quantum diagonalization (SQD) is presented. The solver is built on JAX API, and is intended to be run on GPU platforms and…
Quantum Query Complexity of Maximum Degree and its Generalizations
Amin Shiraz Gilani, Nikhil S. Mande, Subhasree Patro, Nithish Raja
We study the quantum query complexity of several basic graph problems. 1) We prove a tight $Θ(n^{3/2})$ bound for finding a maximum-out-degree vertex in an $n$-vertex tournament,…
Maximally antiflat fixed-particle-number fermionic states
Rahul Gupta, Arya Haghighian, James D. Whitfield
Fermionic Gaussian states are completely characterized by their two-point correlations, making the covariance matrix a natural reference for quantifying non-Gaussian structure.…
Necessary conditions for benchmarking next-generation AI in materials discovery
Daniel E Widdowson, Tatiana V Kurlina, Olga D Anosova, Vitaliy A Kurlin
Current AI predicts materials by speeding up energy minimisation and training on large databases. However, scaling data generation prevents manual analysis by domain experts. If…
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