The Graphical Language of a 12-State Tensor Network Provides Hyperfast Processing of Financial Information

Abstract

Double-entries are the LEGO® bricks of financial information as the financial history of any business can be reconstructed from this inflow/outflow base-pair unit or bit (Orús, 2014). We show how a finite double-entry alphabet or financial wave-function (Schrödinger, 1935) encodes the superpositioned and time-reversible set of financial events capable of describing all basic forms of financial information in a purely graphical language. The topology of this ground-state network can be used as a minimal-complexity computational resource to process financial information in a hyperfast way (Biamonte, 2016). From the collective double-entry interactions, an emergent macroscopic structure heralds the financial condition of a business for a given period of time. The new map condenses thousands of pages of financial information into a single diagram; allowing people to learn in less than 24 hours what would normally take at least 1 year (see Fig. 1). This may help alleviate the problem of financial literacy (GFLEC, 2014). Results are based on solid empirical evidence.

Keywords: tensor network algorithm, quantum information, double-entry information, isomorphism, financial education

JEL Classification: M41