Though still years away from becoming a commercially applicable reality, Quantum Computing is an interesting area that offers much promise in undertaking computations that are not at present feasible with the current generation of computers, the ‘classical computers’ (Rieffel & Polak, 2014). Quantum computers work in a way that is totally and radically different from classical computers because qubits (quantum bits) can exist in a state of both one and zero, simultaneously such that a single qubit can store, and process N qubits of information. Based on the superposition state of having both one and zero, the information stored in N qubits can be computed as N qubits raised to the power on N classical bits (Meglicki, 2010). Quantum computing makes use of the quantum phenomena of mechanics such as entanglement and superposition to undertake operations on data and so this makes quantum computers markedly different from the classical computer that is based on transistors (Raimond, Brune & Haroche, 2001).
Classical computers require data encoding in binary form where the possible states are either zero or one, quantum computing allows for dual state. But just like the classical computer, quantum computers will have little value without the right software to be run on them hence the need to develop practical ways that these computers can be programmed as their arrival is eagerly awaited (Schlosshauer, 2010). Despite the idea of Quantum computers as pioneered by Yuri Marin and Paul Benioff in the early 1980s, not much consideration was placed on how to program these computers until the 1990s (Sarma, 2015). There are several proposals that have been made on programming quantum computers; however, many suffer from various flaws that render them impractical or highly expensive for application in typical environment for developing commercial software (McCaskey et al., 2017). The flaws with many proposals include;
Lack of scalability: Some proposals work well for small code ‘snippets’ but becomes difficult to understand and manage them beyond this; examples include languages that require formal proofs and visual languages for programming.
Foreign techniques: Techniques that are proposed for use are foreign to many commercial software developers, examples are functional languages
Proprietary languages: There are many languages that can be used in quantum computing to date (almost 8500) yet very few can be used commercially and this poses a challenge since the majority of programming languages commercially used today have some unique features that enable their use commercially
Languages that are a challenge to integrate with existing software: Any methods for programming quantum computers must properly integrate with existing software
Unconventional design framework: Proposed libraries and languages have APIs that are difficult to use and do not subscribe to established conventions; they are likely prone to incorrect use
Can run on quantum computers only: The likely scenario is that quantum computers will be used by classical computers, much like the cloud configuration where the quantum computers are used as a computing power resource; as such techniques for programming must integrate and work well with classical computers (McCaskey et al., 2017)
As such, these challenges and flaws in existing programming languages for quantum computing are far from being a trivial undertaking as the working of quantum computers raises interesting challenges in designing languages for creating software to run on them. In this paper, a research is proposed on a quantum computing framework without the flaws discussed afore.
The objectives of this research (also the expected deliverables) are the following;
The target audience for this proposal is commercial programmers
A framework for practical computer programming for quantum computing has several foundations in physics and computer science. Quantum computing fulfills Moor’s law of technology marching on (Al-Rabadi, 2012), with estimation around the year 2023, give or take 3 years, quantum computers will appear (‘Quantum Computing Report’, 2017). Quantum computers are fundamentally different in their operation compared to the 1930s Turing machines that formed the basis of all modern computers. It is therefore important to fully understand quantum computation as a background to understanding the challenges of programming such computers (Bonsor & Strickland, 2016). Understanding quantum computing requires an understanding of quantum mechanics; at the quantum level, there is randomness in nature. If there is an arbitrary state, it is not possible to determine with an appreciable degree of accuracy, how the state will evolve in exact terms. This significantly departs from the Newtonian perspective that the forces acting on a system will determine the evolution of that system. Quantum thinking lacks deterministic properties such that the future position of something like the planets can be determined using available formula and algorithms. A photon, for instance, based on the quantum phenomenon, can be in two places at any one time. The only thing that is certain about the quantum level is uncertainty, but they have been proven experimentally (Blumel, 2010). The qubit is well represented by the Bloch sphere shown below;
The qubit is the basic unit that can be applied in quantum computing. The principle of quantum mechanics that can be applied in computing is illustrated below with a quantum source going through a plane having two slits with a photon detector at the other end as depicted below. When observed on the photon detector, there will be 12 particles, even if only one particle is emitted; further, the observed pattern if of smaller waves canceling (destructive interference) and reinforcing (constructive interference) each other. What is observed are no longer waves, but only where the photons hit. It is the destructive and constructive interference that enables computing using the quantum principle. The photons are detected when passing through one slit or the other, but not both. The photons are considered to have traveled two paths and when split (the photon stream) they will behave like waves (Blumel, 2010), (Greenstein & Zajonc, 2006). The quantum mechanics will be expressed using the Dirac notation in this proposal.
The smallest quantum information unit is the Qubit while in classical system, the smallest unit of information is the bit. Qubits differ from bits since qubit expression is complex within the matrix. Complex numbers take the form of a + bi where b and a are real numbers and I is an imaginary part where i2 equals -1. Using the Argand plane, complex numbers can be drawn as well with the imaginary part (bi) in the Y axis and (a), the real part in the X axis
This state can also be expanded to n qubits in which a complex number precedes every possibility is restricted to have all complex number absolute squared values adding up to 1. the complex numbers, however, cannot be extracted as this will collapse to either of the two possible values, with reference to the probabilities. Expressing qubits becomes complex when expressed in matrices when there are multiple qubits because the number of entries will be n2 entries that must be made in the matrix. Without all entries, the matrix is incomplete, so using the Dirac model in which the zeros can be left out making a concise notation possible (Chester, 2012).
Based on how nature at the quantum level behaves, the principle can be used for computing because the qubit can have a value of 1, 0, or 0 and 1 combined. A qubit with a combination state of either 1 or 0, it can have a 10% chance, when observed, of being in zero state meaning in 9 out of 10 times, it will be in the 1 state but be 0 in the next observation. As such, a quantum program can generate different results in different executions (Chester, 2012). In quantum computing, there will be multiple results generated although only a single one is selected and becomes the output, unlike in a classical computer program as illustrated below;
The probabilities in quantum computer destructively and constructively interfere with each other. A computing device should operate on the principles of quantum mechanics in order to efficiently simulate a quantum system. This computer can be programmed efficiently only when prepared in the physical state. If an attempt is made to simulate a quantum system on a classical computer, the result would be an exponential system slow down (Chester, 2012).
Shor proposed an algorithm for quantum computing that enable factor and also make it possible to solve discrete logarithmic problems a polynomial number of steps, basing on the size of the input. Another algorithm is the Grover algorithm, introduced by Lov Grover for fast searches in databases. These two algorithms are the most important and relevant algorithms for quantum computing. Grover algorithm enables fast task execution; for example, to search through an unordered objects lists requires O () time, while in a classical computer the time required would be O9n) time. The Deutsch’s algorithm allows the determination of F(0) xor f(1) with just a single query to f(x) using the interference concept (Chen, Kauffman & Lomonaco, 2008).
Most languages used for commercial use are written in object oriented and imperative languages, such as C#, Java, Visual Basic, Python, and Cobol, among others (Hiscott, 2014). To commercially use a programming language in quantum computing, it must be easy for users to learn and use. This can be achieved through piggy backing on languages such as Python they are already familiar with. The quantum programming languages structure is different from the existing languages used in classical computers. Quantum languages generally include statements to initialize the system’s quantum state, manipulate the system through unary operations, and then measurement being the final task (Castor & Liu, 2016). Pseudo code conventions have been introduced by Knill based on imperative programming techniques utilizing flow control statements and variables; the method uses the QRAM (quantum random access machine) model. However, the use of pseudo code has little relevance for practical writing of quantum applications (Wang, Knill, Glick & Défago, 2000). Another approach is the qGCL language developed by Zuliani and Sanders for correctness of proof, program deviation, and teaching. In this language, computation is controlled by classical computers making use of quantum sub systems with actions being initialization, evolution, and then finalization. This language also provides implementation for many quantum algorithms, including the Gover’s and Shor algorithms. However, qGCL is too dated and limited for present and future use in quantum programming (Gay & Mackie, 2010).
A quantum programming language that is an extension of for the classical computing C++ language has been developed by Bettelli as a library with many classes. The classes enable the library workings to be encapsulated and hiding them from users; the method allows for better enforcing of rules while valid states will also be maintained using classes. The Bettelli implementation generates quantum operations with byte codes that be piped into real quantum subsystems or simulators. Bettelli’s method most important feature is that a language for quantum programming should be an extension of the classical programming languages (Gay & Mackie, 2010). The QCL (Quantum Computer Language) developed by Omer is the most complete language for quantum programming. Its structure is similar to C which many programmers can easily learn; however, its limitation is an extension of the C language limitation which is used for low level programming and not cutting edge software used in the commercial sector because it lacks modern libraries and power.
It is also proprietary, meaning its adoption will be difficult and slow, as opposed to modern programs that are open source (Gay & Mackie, 2010). A method that simulates quantum computing through the use of Fortran has been proposed by Markus and shows how such libraries can work in quantum computing through simulation; however, it’s not a framework or language in itself. Qubits can be simulated using the Ruby programming language, as proposed by Carini; this is also not a language by itself by has an implementation technique entailing qubits state simulation on separate threads. This model has problems with scheduling but is a pointer to future quantum computing languages that can take advantage of multiple processor cores. Most proposed languages have limitations or would not be practical in quantum computing as programming languages. The main challenge for developing a an effective quantum computer programming language revolves around quantum computer programming, general quantum computing, and designing the framework (Gay & Mackie, 2010).
A practical quantum computing framework can be developed for existing present classical object oriented languages that can be proven to fulfill a myriad of usability and functional properties for quantum programming. The framework will be proven to be practical is it lacks flaws including foreign techniques, lack of scalability, be a proprietary language, difficult to integrate with software currently in use, has unconventional design and can only run on quantum computer.
The overriding research approach for this proposed research is a semi-experimental design in which an empirical approach is used where the treatment condition will be controlled by the researcher without random assignment, using confirmatory research where the priori hypothesis will be tested through a controlled experiment and the concept proved (Shadish, Cook & Campbell, 2011). This research proposes to use an iterative approach to undertaking research and will commence with the gathering of examples, followed by developing usability and functional properties, and interface design that satisfies the properties. The interfaces will then be implemented to test and utilize the proposal using classical simulation; the testing will be done with cognizance of time constraints. The research will then prove that the interfaces that have been proposed and tested satisfy the properties for a practical quantum programming language, before the iteration is analyzed.
The nature of the research is unique, hence justifying the use of the semi experimental confirmatory design because quantum computers still do not actually exist. As such, this research will continuously search for literature as the research proceeds to get alternate ideas and evaluate similar proposals. This experiment will involve the use of dedicated infrastructure to carry out experiments and also act as a repository. A server will be used and all automated tests, along with UML designs documented. The server will have an SCM (source code system) installed and configured, a web server for redirects for requests, a system for tracking bugs, and configured in the RAID architecture to ensure redundancy. Daily automated builds will be used for testing. The properties and functionality of the system will be developed and interfaces developed for implementation through classical simulation. Implementation will be through a primary library and four secondary libraries as depicted in the image below;
Conclusion
The concept of quantum computing operating using the principles of quantum mechanics has been discussed in this paper. Quantum computers will exponentially increase computing power; but they are still theoretical machines, which according to Moor’s law will be available between 2023 and 2015 plus or minus 3 years. It is envisaged that quantum computers will use the cloud computing model in initial stages where their computing power is accessed using classical computers. Quantum computing is very different from classical computers, as one thing like a photon can be in two places at once, a concept that quantum computing will exploit. Proposed programming languages like QRAM, qGCL, and QCL have challenges including lack of scalability, foreign techniques, and unconventional frameworks, proprietary, are a challenge to integrate with existing software and can run on quantum computers only. This research proposal propose a practical programming language that will overcome these challenges and will be usable through classical computing but harness quantum computing power and will be proven through simulation tests
References
Al-Rabadi, A. N. (2012). Reversible logic synthesis: From fundamentals to quantum computing. Berlin: Springer.
Blumel, R. (2010). Foundations of quantum mechanics: From photons to quantum computers. Sudbury, Mass: Jones and Bartlett Publishers.
Bonsor, K., & Strickland, J. (2017). How Quantum Computers Work. Defining the Quantum
Computer – Qubits and Defining the Quantum Computer | HowStuffWorks. Retrieved 12 October 2017, from https://computer.howstuffworks.com/quantum-computer1.htm
Chen, G., Kauffman, L. H., & Lomonaco, S. J. (2008). Mathematics of quantum computation and quantum technology. Boca Raton, FL: Chapman & Hall/CRC.
Chester, M. (2012). Primer of Quantum Mechanics. Dover Publications.
Gay, S., & Mackie, I. (2010). Semantic techniques in quantum computation. Cambridge Cambridge University Press
Greenstein, G., & Zajonc, A. (2006). The quantum challenge: Modern research on the foundations of quantum mechanics. Sudbury, Mass: Jones and Bartlett Publishers.
Hiscott, R. (2014). 10 Programming Languages You Should Learn Right Now. Mashable. Retrieved 6 October 2017, from https://mashable.com/2014/01/21/learn-programming- languages/#3gzxjL0R8aqj
Meglicki, Z. (2010). Quantum Computing Without Magic: Devices. Cambridge: MIT Press.
Raimond, J. M., Brune, M., & Haroche, S. (August 28, 2001). Manipulating quantum entanglement with atoms and photons in a cavity. Reviews of Modern Physics, 73, 3, 565-582.
Rieffel, E., & Polak, W. (2014). Quantum computing: A gentle introduction. Cambridge, Mass. : MIT Press
Sarma, K., J. (2015). Understanding Quantum Computing. International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume-1, Issue-6, 370-384. https://ijseas.com/volume1/v1i6/ijseas20150641.pdf
Schlosshauer, M. (2010). Decoherence and the quantum-to-classical transition. Berlin: Springer.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2011). Experimental and quasi-experimental designs for generalized causal inference. Belmont: Wadsworth Cengage Learning.
Wang, C., Knill, E., Glick, B. R., & Défago, G. (January 01, 2000). Effect of transferring 1- aminocyclopropane-1-carboxylic acid (ACC) deaminase genes into <i>Pseudomonas fluorescens</i> strain CHA0 and its <i>gac</i>A derivative CHA96 on their growth- promoting and disease-suppressive capacities. Canadian Journal of Microbiology, 46, 10, 898-907.
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