The speed and accuracy of quantum computing make it an ideal tool for collecting and analyzing data, modeling the impact of proposed tax legislation, and predicting and detecting tax evasion. Computers using quantum technology can instantly solve computational problems that could take decades for an ordinary PC. These same qualities, however, threaten data security.
Quantum computing is based on quantum mechanics, or the study of the physical properties of atomic and subatomic particles. A quantum is the smallest physical unit of a system. For example, a quantum of light is a photon and a quantum of electricity is an electron. The word quantum comes from Latin and means “an amount” or “how much?”
A quantum state in a subatomic system is a set of physical properties that can be measured simultaneously and provides a probability distribution for the system outcomes of each possible measurement. The knowledge of the quantum state of the system and of the rules for the evolution of the system over time covers everything that can be predicted about the behavior of the system.
A quantum computer differs from a classical computer in its basic unit of information. The classic computer operates on bits of 1s and 0s, known as a dual processing system. Quantum computers rely on quantum bits, or “qubits,” that work with 1, 0, or a combination of the two. Qubits can be designed as photons, electrons or atomic nuclei.
Qubits allow all combinations of information to exist in multiple places simultaneously, in a phenomenon known as superposition. This allows quantum computers to simultaneously calculate a multitude of equations or possibilities, a step-by-step task performed by ordinary computers.
Qubits, like most subatomic particles, can become connected so that action on one qubit can influence another qubit, in a phenomenon known as entanglement. If two qubits are entangled in the same quantum state, changing the state of one qubit will change the state of the other, even if the qubits are separated by huge distances.
This dramatically increases the speed and accuracy of quantum computers, making them ideal for collecting, sharing, and modeling data. Quantum computers can also facilitate the equalization, monitoring, review and correction of tax systems.
Speed and precision
Since the EU introduced Mutual Administrative Assistance in 2011, the exchange of tax information has expanded to include cross-border advance tax rulings, advance pricing agreements, country-by-country reporting, information on beneficial owners, cross-border tax arrangements and sales on electronic platforms. Data collection and sharing is facilitated by systems that require country-by-country public reporting, OECD Pillars 1 and 2, and the Common Reporting Standard.
These systems will only succeed if the technology allows taxpayers to provide the data without breaking the system. Data sets that are too large or complex to be handled by traditional data processing software are called big data. Big data can be collected and analyzed by combining quantum processors with artificial intelligence and enhanced machine learning.
Machine learning is mainly used for data classification. Quantum computers can create new classifiers that generate more sophisticated maps of data, allowing researchers to develop more efficient AI that can identify patterns invisible to classical computers and classify data more accurately.
Data identification and analysis is the purpose of the OECD Analytical Database of Individual Multinationals and Affiliates (ADIMA). The ADIMA project examines the operations of 500 multinational companies to determine where their value chains are, how they operate and where they pay taxes.
The OECD plans to expand the database and include information not typically found in company reports by assessing open data sources such as news outlets and websites. The first published reports only looked at the top 100 ADIMA companies and found that 85 of them had active operations, while the official financial statements showed active operations for only 75.
Quantum technology can also provide robust fiscal impact analyzes to governments before they enact legislation. Tax impact theory analyzes how changes to tax systems affect taxpayer response and is useful for examining cross-border models such as the Common Consolidated Corporate Tax Base. A quantum computer could simulate the possible effects of proposed legislation on the distribution of the tax base.
Fraud and evasion
Most financial institutions invest in fraud detection systems that use advanced algorithms, but these systems can produce a high number of false positives, causing organizations to be too risk averse. Investigating fake alerts takes time and can block legitimate transactions.
The data modeling capabilities of quantum computers are superior for finding patterns, performing classifications, and making predictions. A support vector machine sorts data into classes within a set of decision boundaries (called a hyperplane). Its algorithm learns, for example, to assign labels to objects. Once machines are trained, they can assign new data to the appropriate category.
Machine learning techniques used by quantum support vector machines use supervised learning models to classify and regress data and to detect outliers, which reduces false positives.
But there are downsides. Companies, banks and governments are aware of the data security risks of quantum computing. In 2015, the US National Security Agency had quantum computing in mind when it warned intelligence agencies to choose cryptographic algorithms. In April IBM
Quantum computing moves computers beyond binary logic to atomic-level randomness. Encryption that is mathematically complex enough for now could be decrypted by a quantum machine in the future. This would radically change the dynamics of fraud prevention.
While the average citizen lacks the means and knowledge to acquire and use the technology (a commercial quantum computer with 2,000 qubits currently costs $15 million), international cartels earning significant revenue from ransomware could afford it. .
Imagine that a classical computer is attacked by an adversary with quantum technologies. Security can be breached retrospectively because encrypted messages can be intercepted, stored and decrypted 10 years later.
This possibility has generated a demand for secure quantum cryptography. Post-quantum cryptography, or a type of cybersecurity that can be used by conventional computers, is being developed. Quantum strong cryptography could prevent data exposure and improve the protection of digital assets.
In 2016, the US Department of Commerce’s National Institute of Standards and Technology launched a competition calling on academics and industry cryptographers to design an algorithm that could withstand decryption from a quantum computer. On July 5, it chose four algorithms to include in its post-quantum crypto standard.
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