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Know Before You Ask—Quantum Search Redefines Predictive Analysis

Stephen Balzac
April 1, 2025
Quantum Search

Quantum AI search technology anticipates your questions and is ready with helpful answers.  

SWIRL’s new Quantum AI Search feature takes advantage of emergent properties of AI to dramatically expand the range of data sources that it can simultaneously search in response to a user query. Two pieces of research out of the Massachusetts Institute of Technology made the breakthrough possible. First, AI experts created an image generator AI that is more powerful than the state-of-the-art models, uses less energy, and can run on a laptop or smartphone. Second, physics researchers developed a photon-shuttling interconnect that enables direct communication between quantum processors, leading to remote entanglement between processors that are not physically connected.  

Quantum entanglement, long a staple of science fiction stories, posits that two entangled particles can influence one another’s behavior across arbitrarily long distances without being limited by the speed of light. The MIT research moves beyond theory toward a real-world application of quantum entanglement and the development of working quantum computers. Quantum entangled processors can solve problems far beyond the capabilities of even the fastest modern supercomputer.  

The true breakthrough came when researchers realized that photon-shuttling and quantum entanglement could be combined with the compact image generating AI models. Extending the quantum entanglement approach more broadly enabled researchers to entangle any arbitrary collection of AI models, dramatically enhancing processing capabilities, information availability, and speed. Unexpected results, initially dismissed as hallucinations, led to the further discovery that model entanglement transcends time as well as space. This discovery opens a new dimension in search, specifically a fourth dimension. 

SWIRL’s new Quantum AI Search feature takes full advantage of advanced AI models being linked to future models so that your queries simultaneously search all the data sources you have access to, or ever will have access to, including sources that don’t yet exist.  

By combining AI search with quantum entanglement, SWIRL can run your future queries and send you the results today. If you look at the results and never execute the search, the information—and your memory of it—simply disappears, thus avoiding a dangerous time paradox. SWIRL uses Paradox Avoidance Generation (PAGE) to make sure you don’t accidentally destroy the universe.  

Results are organized using our advanced PAGE ranking, with the most relevant and least likely to destroy the universe presented first. The threshold is user adjustable. 

You’ll know that you’re using SWIRL Quantum AI Search when answers suddenly appear to questions you haven’t yet asked—indeed, you will get answers to questions you haven’t even thought about asking! That may have already happened, although if you decided not to execute the search once you saw the answers, you won’t remember. SWIRL Quantum AI Search truly saves you time. 

SWIRL Quantum AI Search will be available on April Fools Day, 2045. 

SWIRL delivers secure, federated AI search across all your systems, re-ranked with AI and kept within your tenant’s security boundary.
Connect Your Systems
Link iManage, NetDocuments, M365, SharePoint, email, research tools, regulatory sources, SQL databases, and other systems. No data lake required. No second index to secure.
Search Everywhere at Once
SWIRL runs a federated search across all connected systems simultaneously. Native permissions ensure lawyers only see documents they're authorized to access.
Re-Rank with Your LLM
Results are re-ranked by your firm's chosen large language model to surface the most relevant items first. Provenance, citations, and source systems preserved.
Feed Your Assistant or UI
Results flow via APIs and connectors to M365 Copilot, ChatGPT, or other assistants. Or lawyers use SWIRL's own legal search interface directly.
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