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IEEE International Conference on Quantum Software (QSW)

Reverse Engineering of Hamiltonian Expressions from D-Wave programs

Ricardo Pérez‐Castillo, Luis Jiménez-Navajas, Mario G. Piattini

Published Jul 1, 2022• 3 citations
Publisher

Abstract

Quantum annealing software has achieved a certain market penetration since it has demonstrated a good performance for optimization problems. The problem definitions (Hamiltonians functions to be minimized) are usually built in combination with classical software that can evolve over time. Thus, it is difficult to comprehend the underlying Hamiltonian and represent it in an accurate way for maintaining such programs. Although some reverse engineering techniques have been proposed for gate-based software, there are no choices to accomplish reverse engineering in quantum annealing. This research proposes a dynamic analysis technique of D-Wave (python) programs for reversing Hamiltonians, which has been preliminarily evaluated with nine programs. The technique also represents those expressions by using the Knowledge Discovery Metamodel (ISO/IEC 19506). Due to the usage of this standard, the outgoing expressions can be represented in combination with other parts of classical-quantum software systems. Thus, the main implication is that this technique contributes to the software modernization of/towards hybrid information systems.

DOI
10.1109/qsw55613.2022.00018

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Quantum annealing software has achieved a certain market penetration since it has demonstrated a good performance for optimization problems. The problem definitions (Hamiltonians functions to be minimized) are usually built in combination with classical software that can evolve over time. Thus, it is difficult to comprehend the underlying Hamiltonian and represent it in an accurate way for maintaining such programs. Although some reverse engineering techniques have been proposed for gate-based software, there are no choices to accomplish reverse engineering in quantum annealing. This research proposes a dynamic analysis technique of D-Wave (python) programs for reversing Hamiltonians, which has been preliminarily evaluated with nine programs. The technique also represents those expressions by using the Knowledge Discovery Metamodel (ISO/IEC 19506). Due to the usage of this standard, the outgoing expressions can be represented in combination with other parts of classical-quantum software systems. Thus, the main implication is that this technique contributes to the software modernization of/towards hybrid information systems.
Abstract Quantum computing is now a reality, and its incomparable computational power has led companies to show a great interest in being able to work with quantum software in order to support part of their current and future business operations. However, the quantum computing paradigm differs significantly from its classical counterparts, which has brought about the need to revolutionise how the future software is designed, built, and operated in order to work with quantum computers. Since companies cannot discard all their current (and probably mission-critical) information systems, they must adapt their classical information systems to new specific quantum applications, thus evolving towards hybrid information systems. Unfortunately, there are no specific methods with which to deal with this challenge. We believe that reengineering, and more specifically, software modernisation using model-driven engineering principles, could be useful as regard migrating classical systems and existing quantum programs towards hybrid information systems. This paper, therefore, presents QRev, a reverse engineering tool that analyses quantum programs developed in Q# in order to identify its components and interrelationships, and then generates abstract models that can be used in software modernisation processes. The platform-independent models are generated according to the Knowledge Discovery Metamodel (KDM) standard. QRev is validated in a case study involving five quantum programs in order to demonstrate its effectiveness and scalability. The main implication of the study is that QRev can be used in order to attain KDM models, which can subsequently be employed to restructure or add new quantum functionality at a higher abstraction level, i.e. independently of the specific quantum technology.
A quantum annealer solves optimization problems by exploiting quantum effects. Problems are represented as Hamiltonian functions that define an energy landscape. The quantum-annealing hardware relaxes to a solution corresponding to the ground state of the energy landscape. Expressing arbitrary programming problems in terms of real-valued Hamiltonian-function coefficients is unintuitive and challenging. This paper addresses the difficulty of programming quantum annealers by presenting a compilation framework that compiles a subset of C code to a quantum machine instruction (QMI) to be executed on a quantum annealer. Our work is based on a modular software stack that facilitates programming D-Wave quantum annealers by successively lowering code from C to Verilog to a symbolic “quantum macro assembly language” and finally to a device-specific Hamiltonian function. We demonstrate the capabilities of our software stack on a set of problems written in C and executed on a D-Wave 2000Q quantum annealer.
Quantum annealing is a form of quantum computing that exploits quantum effects to probabilistically solve a specific, NP-hard problem: finding the ground state of a classical, Ising-model Hamiltonian. Because physical quantum annealers are already available, there exists the pressing question of how to program such systems. That is, how can one map a computational problem into the coefficients of an Ising-model Hamiltonian for solution by quantum-annealing hardware? In this article, we address that question primarily from a practical standpoint. We survey extant software tools intended for programming D-wave annealing-based quantum processors and examine the programming model and solution technique promoted by each tool in an attempt to showcase the variety of contemporary approaches to solving computationally challenging problems on an existing annealing-based quantum computer.
In this new era of Quantum Computing, a transformative paradigm is emerging for the enhancement of Cyber Security using the special-purpose systems like D-Wave's quantum annealers which helps the encryption system become more resilient. As quantum computing evolves, it provides new paradigms for breaking the traditional encryption methods, as the recent breakthroughs is accomplished by researchers from Shanghai University that uses D-Wave's quantum annealing capabilities in providing the researchers to successfully demonstrated the potential to compromise widely used encryption systems, including RSA and AES. The usage of quantum annealing (QA) that makes the D-Wave’s a specialized quantum system which enables an efficient computation in tackling cryptographic problems, demonstrates the implications for the data security and privacy by applying the quantum-classical hybrid architectures. This research aims to get attention on the urgent need for developing a new security measures in response to the vulnerabilities exposed by the quantum advancements which emphasizes the crucial role of D-Wave's in unlocking the complexities of quantum computing and its implications for the future of cybersecurity.
Quantum computing has come to stay in our lives. Companies are investing billions of dollars in it because of the potential benefits that it can achieve, providing promising applications in almost every business sector. Although quantum computing is evolving at an exponential rate, the development of tools, techniques, or frameworks for the evolution of current information systems towards quantum software systems is still proving to be a challenge. This research contributes to the evolution of current information systems towards hybrid information systems (combining the classical and quantum computing paradigm). We propose a software modernization process, by following model-driven engineering principles, adapted to the quantum paradigm, based on modified versions of standards for reverse engineering of classical, quantum software assets, and for the design of the target system. In particular, this paper focuses on the restructuring transformation from KDM to UML models, where KDM models have been generated from Q# code. This proposal has been validated through a case study involving 17 programmes. The results obtained show optimistic values regarding the complexity of the UML models generated, their expressiveness and scalability. The main implication of this research is that UML models can indeed help the software evolution of/toward hybrid information systems.
Quantum annealing is a specialized type of quantum computation that aims to use quantum fluctuations in order to obtain global minimum solutions of combinatorial optimization problems. Programmable D-Wave quantum annealers are available as cloud computing resources which allow users low level access to quantum annealing control features. In this paper, we are interested in improving the quality of the solutions returned by a quantum annealer by encoding an initial state into the annealing process. We explore two D-Wave features allowing one to encode such an initial state: the reverse annealing and the h-gain features. Reverse annealing (RA) aims to refine a known solution following an anneal path starting with a classical state representing a good solution, going backwards to a point where a transverse field is present, and then finishing the annealing process with a forward anneal. The h-gain (HG) feature allows one to put a time-dependent weighting scheme on linear (hhh) biases of the Hamiltonian, and we demonstrate that this feature likewise can be used to bias the annealing to start from an initial state. We also consider a hybrid method consisting of a backward phase resembling RA, and a forward phase using the HG initial state encoding. Importantly, we investigate the idea of iteratively applying RA and HG to a problem, with the goal of monotonically improving on an initial state that is not optimal. The HG encoding technique is evaluated on a variety of input problems including the edge-weighted Maximum Cut problem and the vertex-weighted Maximum Clique problem, demonstrating that the HG technique is a viable alternative to RA for some problems. We also investigate how the iterative procedures perform for both RA and HG initial state encoding on random whole-chip spin glasses with the native hardware connectivity of the D-Wave Chimera and Pegasus chips.