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Genetic and Evolutionary Computation Conference Companion (GECCO Companion)

Evolving Quantum Logic Gate Circuits in Qiskit

Thomas Newbold, Alberto Moraglio

Published Jul 14, 2024• 0 citations
Publisher

Abstract

The emergence of quantum computers represents a crucial leap forward in practical computability, when compared to classical architectures. Harnessing that power effectively is an exercise of increasing importance. Despite research in this field expanding rapidly, little headway has been made towards new quantum algorithms. The complexity of quantum systems makes them conceptually inaccessible to non-experts; therefore, programs are hard to design, hindering advancement. This paper presents a method for genetically encoding and designing arbitrary circuits in Qiskit, a software library developed by IBM for simulating quantum logic gates. The effectiveness of the system is verified by evolving solutions to the Toffoli gate and Quantum Fourier Transform problems.

DOI
10.1145/3638530.3664108

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The emergence of quantum computers represents a crucial leap forward in practical computability, when compared to classical architectures. Harnessing that power effectively is an exercise of increasing importance. Despite research in this field expanding rapidly, little headway has been made towards new quantum algorithms. The complexity of quantum systems makes them conceptually inaccessible to non-experts; therefore, programs are hard to design, hindering advancement. This paper presents a method for genetically encoding and designing arbitrary circuits in Qiskit, a software library developed by IBM for simulating quantum logic gates. The effectiveness of the system is verified by evolving solutions to the Toffoli gate and Quantum Fourier Transform problems.
Quantum computing is an emerging field that depends upon the basic properties of quantum physics and principles of classical systems. This leads a way to develop systems to solve complex problems that a classical system cannot do. In this article, we present simple methods to implement logic circuits using quantum gates. Logic gates and circuits are defined with quantum gates using Qiskit in Python. Later, they are verified with quantum circuits created by using IBM Quantum. Moreover, we propose a way of instantiating the basic logic circuits to design high-end logic expressions. As per our knowledge, the proposed simple approach may be helpful to solve the complex logical problems in near future.
Automatic quantum circuit design is motivated by the difficulties in manual design, because quantum algorithms are highly non-intuitive and practical quantum computer hardware is not yet available. Thus, quantum computers have to be simulated on classical hardware which naturally entails an exponential growth of computational costs and allows only to simulate small quantum systems, i. e., with only few qubits. Huge search spaces render evolutionary approaches nearly unable to achieve breakthrough solutions in the development of new quantum algorithms. Consequently, at present we must be content to evolve essentially already existing (black-box) quantum algorithms. This thesis presents empirical results on the evolution of quantum circuits using genetic programming. For that purpose, a linear and a linear-tree GP system (allowing intermediate measurements) with integrated quantum computer simulator were implemented. Their practicality in evolving quantum circuits is shown in different experiments for 1-SAT (solutions act like Hogg's algorithm) and the Deutsch-Jozsa problem. These experiments confirm that the evolution of quantum circuits is practically feasible only for sufficiently small problem instances. In this context, scalability and the detection of scalability becomes very important. It is shown that scalable quantum circuits are evolvable to a certain degree: a general quantum circuit can be inferred manually from the evolved solutions for small instances of the given problem. Besides, further experiments indicate that 're-evolution' is effective for the evolution of scalable quantum circuits. With this method the start population of a problem instance is inoculated with evolved solutions for a smaller problem instance. Furthermore, investigations of fitness landscapes and selection strategies are made, with the aim of improving the efficiency of evolutionary search. A notable result is that using the crossover operator damages rather than benefits evolution of quantum circuits.
Quantum programs are difficult for humans to develop due to their complex semantics that are rooted in quantum physics. It is therefore preferable to write specifications and then use techniques such as genetic programming (GP) to generate quantum programs instead. We present a new genetic programming system for quantum circuits which can evolve solutions to the full-adder and quantum Fourier transform problems in fewer generations than previous work, despite using a general set of gates. This means that it is no longer required to have any previous knowledge of the solution and choose a specialised gate set based on it.
This paper presents the application of the improved Self-Organizing Migrating Algorithm (iSOMA) to the synthesis and optimization of quantum circuits. We develop a comprehensive method to evolve candidate quantum circuits with minimal cost by integrating iSOMA with circuit evaluators in Qiskit. Experimental evaluation across 100 independent runs demonstrates a 90% success rate in synthesizing Toffoli gates, with an 80% circuit uniqueness rate indicating diverse solution exploration. The algorithm achieves a median cost of 0.000 and determinism = 8/8 for successful runs, confirming its effectiveness for quantum circuit synthesis. This research is important for future 6G networks and beyond, as quantum computing has the potential to be more efficient, especially in the physical layer of the Radio Access Network (RAN), where quantum-supported optimization mechanisms are able to process a large number of tasks faster. The proposed synthesis of Toffoli gates controlled by iSOMA technology contributes to quantum computing by supporting the efficient design of quantum circuits, which is a prerequisite for the deployment of quantum-native functions, such as the Quantum Fourier Transform, in future communications and wireless infrastructures.
Recent advancements in computing speed and capacity of Artificial Intelligence (AI) algorithms have reached a saturation level in performance due to the continuous application of Moore's law which resulted in the memory wall phenomenon. Quantum computing (QC), based on the principles of quantum mechanics which include superposition and entanglement, has the potential to contribute to the advancements of AI by providing exponential speedup. Quantum computers use quantum bits, also known as qubits, as the metric to demonstrate the power of quantum technology. Qubits, the fundamental unit of quantum information can exist in two states, 0 and 1, simultaneously, leveraging the principle of superposition. Quantum gates are the building blocks of quantum circuits, operating on qubits to transform one quantum state into another, and are comparative to classical logic gates. Designing and simulating quantum circuits can be completed using the Qiskit library design by IBM. Qiskit is a cloud-based, open access platform to real quantum computers with 5 to 16 qubits. This paper offers a straightforward tutorial on implementing one, two and three-qubit quantum gates, as well as designing quantum circuits using the Qiskit library, illustrated with an example of a quantum half adder implementation.