© 2013-2019 Qbit Solutions Research Team. All Rights Reserved.

SCIENCE BEHIND FINANCIAL QBITS

Deep Learning

Tensor Networks

Financial Qbits

© E. Miles Stoudenmire, 2018.

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Flow into Profits in the languages of Deep Learning and Tensor Network Theory

 

Deep learning

 

Deep learning architectures provide a multi-layered framework that contain different levels of abstraction, encoding the hierarchy of concepts that describe the players and dynamics of a given discipline. In the case of Flow into Profits, the top layer provides a macroscopic perspective of financial players and the lower layer gives us the more detailed view of specific players or accounts.

Quantum tensor networks theory (TNT)

Double-entries may be thought of as 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). 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.

The possibility of building a quantum business model was predicted by past president of the American Accounting Association (AAA) Dr. Joel S. Demski and Oberlin College physicist Dr. Stephen A. Fitzgerald, in the paper: “Quantum information and accounting information, their salient features and conceptual applicatios" (Demski et al. 2006, p. 35), where they point out the absence of fundamentals of the current scheme and express their excitement for the prospect of an updated quantum mechanical version.