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Chapman & Hall/CRC eBooks

GAALOPWeb as a Qubit Calculator

Dietmar Hildenbrand

Published Aug 31, 2021• 0 citations
Publisher

Abstract

In this chapter, we use GAALOP (Geometric Algebra Algorithms Optimizer) to handle QBA (quantum bit algebra) as a Geometric Algebra for. We make all the calculations online via GAALOPWeb for Qubits. This enables us to make calculations for qubits without the need of installing a specific software. Since GAALOPWeb is webbrowser-based, it can be used on various devices such as PC, tablet or smart phone. We present how the NOT-operation can be applied on one as well as on two qubits.

DOI
10.1201/9781003139003-17

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In this chapter, we use GAALOP (Geometric Algebra Algorithms Optimizer) to handle QBA (quantum bit algebra) as a Geometric Algebra for. We make all the calculations online via GAALOPWeb for Qubits. This enables us to make calculations for qubits without the need of installing a specific software. Since GAALOPWeb is webbrowser-based, it can be used on various devices such as PC, tablet or smart phone. We present how the NOT-operation can be applied on one as well as on two qubits.
We present a real geometric algebra framework designed for the direct translation of the Dirac formalism into geometric algebra representations. Unlike previous approaches based on positive-definite signatures, QCA employs a split-signature construction that enables a natural realization of quantum states and operators while simplifying computational implementation. We further present an implementation of QCA using the \textit{GAALOP} software and show how quantum gates and multi-qubit systems can be efficiently represented and generated computationally. As an application, we demonstrate the use of QCA in quantum game theory, where the real-algebraic formulation provides computational advantages for modeling entangled strategies and quantum interactions. The proposed framework establishes a practical bridge between the abstract formalism of quantum computation and efficient geometric algebra implementations.
This chapter provides a basic overview of Quantum Computers (QCs) used in Quantum Computing (Quacomp) applications. Classical computers utilize silicon-based semiconductor devices. In recent years integrated circuit lateral feature sizes have been reduced to 10 nm, increasing chip density; however, as these sizes progress to be reduced to 5 nm and smaller, silicon-based circuits become impractical. QCs make use of quantum phenomena such as entanglement and superposition to increase and improve the computation speed as compared to classical computers, enabling them to solve some computationally complex problems in practical time windows. At this juncture QCs are not able to act as replacements for general- purpose computers, but they can outperform classical computers in a set of specific computational problems, such as optimization problems or for identifying the best solution from a set of feasible solutions. This chapter covers three QC approaches: the circuit model (or gate model), the adiabatic quantum computation model, and the categorical quantum model. Quacomp has experienced relatively rapid development in recent years, starting with the basic ideation in the early 1980s, then continuing with early proof of concepts for hardware in the next two decades, and now, in the 2020s, with QC hardware that can implement over one-thousand qubits (e.g., [ TIL22 ], [ FEY82 ], [ LEU00 ], [ NEG06 ], [ PLA07 ], [ MON11 ], [ MON16 ], [ CAS23 ]). Figure 5.1 (courtesy of IBM) depicts recent illustrative achievements in practical QC development at IBM, a key player in this arena (in the fall of 2023, IBM® announced the Condor prototype, the world’s first universal QC with 1,121 qubits 1 ). The generation of QCs at the time of this writing is known as Noisy-Intermediate Scale Quantum (NISQ) devices (some proponents cite this as being the third generation). These NISQ QCs are typically cooled to cryogenic temperatures (around −273° C) in order to operate efficiently; these environmental conditions are challenging to maintain in other than laboratory settings. In practical terms, QCs remain of somewhat limited and specialized functionality and are prone to computational errors. Considering these limitations, Hybrid QC (HQC) 170 171 172 arrangements and Hybrid Quantum Applications (HQApps) endeavor to combine classical and QC systems in order to undertake calculations directed at various (but specific) optimization or related use cases; tasks such as preparing inputs for quantum algorithms, storing data, or interacting with the user are typically still performed using classical computers. The number of operations in a QC typically equates with the number of quantum gates and the number of qubits supported; however, the larger the number of gates, the more likely it is that errors, including decoherence, are introduced into the quantum state. NISQ entails a somewhat limited number of logical qubits and associated physical qubits to support error correction schemes. Fortunately, a category of algorithms running on NISQ devices can proceed with only a small number of qubits and exhibit some degree of noise resilience; typically, they are hybrid algorithms, with a portion of the computational steps performed on a quantum device and with other computational steps running on a conventional computer. The bottom line is that with NISQ devices, the number of operations is typically moderate at this juncture, thus the scope and type of algorithms that can be implemented is relatively limited. However, there is research that shows that even these QCs can outperform conventional computers on a set of mathematical problems (e.g., [ PRE18 ], [ BRO19 ], [ MAD22 ]). The next step in the evolution is the interconnection of QCs over a distance network, as discussed in Chapter 6 . A classification of fundamental QC technologies has been proposed the European standards body CEN-CENELEC, specifically the Focus Group on Quantum Technologies (FGQT), which was established in June 2020 to coordinate and support the development of standards relevant for European industry and research [ OVA22 ]. See Table 5.1 .
To secure communications, this article presents a key extraction scheme for smart devices using ambient sounds. Specifically, it is designed for the scenario when smart devices do not have any pre-loaded secrets. Moreover, it can be implemented when smart devices have no access to the online trusted third party or the Network Time Protocol server. In our scheme, smart devices achieve synchronization by making use of ambient sounds. Then, they calculate and obtain the pairing distance and secure distance by applying a band-pass filter. Completing these operations, two smart devices can directly extract a communication key. We analyze the security of our scheme and evaluate the performance of it by implementing the scheme using a few off-the-shelf devices. The experimental results indicate that compared with related schemes, the bit generation rate of our scheme has significant improvement (increases at least 80%), and it can reach 312 bit/s.
Research in contemporary physics is emphasizing the development and evolution of computer systems to facilitate the calculations. Quantum computing is a branch of modern physics is believed promising results for the future, Thanks to the ability of qubits to store more information than a bit. The work of this paper focuses on the simulation of certain quantum algorithms such as the prisoner's dilemma in its quantum version using the MATHEMATICA® software and implementing stochastic version of the software MAPLE ® and the Grover search algorithm that simulates finding a needle in a haystack.
We start this chapter by reviewing how quantum states and spaces are described with Dirac notation. We then examine qubits, the quantum memory bits, and the states formed with multiple qubits. We next discuss quantum gates and their low-level processing of qubits. And, at last, we get on to quantum programming and QC applications. We explore the use of several software packages that simulate QC, or provide the programs to run on quantum computers. We explore an example of the latter using the IBM Quantum Platform. The assortment of examples and problems that follow includes simple circuits, Monte Carlo techniques, Schrödinger's cat, and quantum Fourier transforms.