Paper/Quantum Information and Accounting Information

Demski, Joel; Fitzgerald, S.; Ijiri, Yuji; Ijiri, Yumi; Lin, Haijin (2009). "Quantum information and accounting information: Exploring conceptual applications of topology". 28. local page: Journal of Accounting and Public Policy: 133–147. 

Introduction

This present paper is a sequel to the paper prepared last summer by the same five authors, entitled "Quantum information and accounting information: Their salient features and conceptual applications." This first paper has been published in the July-August issue of the Journal of Accounting and Public Policy and is available online at sciencedirect.com by Elsevier. The purpose of this earlier paper was to explore the latest developments in quantum information and to see whether some conceptual applications can be made that will improve our understanding of accounting information. This present paper will focus on a revolutionary trend in quantum information and accounting information. The word, "trend," is used to emphasize that not just one but many revolutionary changes are occurring. On quantum information, we will focus on one particularly salient recent development, namely the use of topology in quantum computation. On accounting information, we discuss the changes brought on by the Sarbanes-Oxley Act of 2002 which made accounting information more legalistic and blurred the boundary of financial and managerial accounting. We wish to explore topology’s potential applications to accounting as we believe that this development will most likely aid an important segment of current accounting information, especially internal controls that are mandated by Sarbanes-Oxley. To preserve continuity, we shall first present a synopsis of the last year's paper before we discuss the present paper. We started the paper with Part I entitled "Understanding quantum information." Here, we examined such concepts as quantum superposition and quantum parallel processing, by which a huge multiplicity of states can be transformed almost instantly. In addition to speed, we learned that quantum information must deal with irreducibly random phenomena and inexhaustible uncertainty, meaning that there are no complete events that are under complete certainty. Furthermore, there have been many quantum phenomena that just seem to be impossible to explain, such as quantum entanglement and quantum transportation where particles can travel long distance "instantly." Finally, we discussed quantum cryptography and its use of the Heisenberg uncertainty principle to create unbreakable keys. The way physicists are dealing with quantum experiments is also interesting. They can now by means of Bose-Einstein condensate, see and manipulate particles which they could only imagine in theory. Quantum algorithms have also been developed, allowing us to cope with errors inherent in the processing of qubits. Physicists are exploring these applications while still thinking about the fundamental laws in physics. The best progress seems to occur when stretching the classical description without damaging it, thus uncovering something profound about Nature. In Part II, we dealt with quantum information and double-entry bookkeeping. Arthur Cayley (1821-95), the founder of modern British school of pure mathematics, developed matrix algebra, later used by Werner Heisenberg in 1925, and it became indispensable in quantum mechanics. Interestingly, Cayley wrote a short book on double-entry bookkeeping. Furthermore, 2 he stated in its preface that double-entry bookkeeping is "Like Euclid's theory of ratios, an absolutely perfect one." We considered the basis for his glorious praise and concluded that Cayley saw the isomorphism between ‘ratio matrix’ and ‘double-entry matrix,’ thus transferring his praise for theory of ratios to the framework of double-entry bookkeeping. It was useful to consider why there are differences in quantum information and accounting information and to trace the reasons. Thus in Part III, we analyzed seven perspectives from accounting information, namely 1) recognition and aggregation issues, 2) measurement vs. information content, 3) physical vs. social sciences perspective, 4) uncertainty and probability assessment, 5) endogenous expectation, 6) error-correction mechanism, and 7) environmental issues.