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BHT-QAOA: Generalizing Quantum Approximate Optimization Algorithm to Solve Boolean Problems as Hamiltonians v1

Ali Al-Bayaty, Marek A. Perkowski

Published Nov 29, 2024• 1 citation
Publisher

Abstract

The BHT-QAOA is a hybrid classical-quantum algorithm that solves arbitrary classical Boolean problems as Hamiltonians in the quantum domain, using the quantum approximate optimization algorithm (QAOA) [1]. The BHT-QAOA stands for the "Boolean-Hamiltonians Transform for QAOA" [2]. Research and studies are mainly focused on solving combinatorial optimization problems using QAOA, e.g., the MaxCut problem [1]. However, the BHT-QAOA adds an additional capability to QAOA to find all optimized approximated solutions for classical Boolean problems, as expressed in the following steps and demonstrated in the figure below. Design classical Boolean problems into quantum Boolean oracles in different logical structures, such as POS, SOP, ESOP, CSP-SAT, XOR-SAT, just to name a few. Convert these quantum Boolean oracles into quantum Phase oracles. Transform these quantum Phase oracles into the Hamiltonians (HC and HM) of QAOA. Please observe that, from the aforementioned steps, the total utilized numbers of qubits and quantum gates are significantly reduced for the final generated Hamiltonians (HC and HM) of QAOA. Accordingly, the BHT-QAOA will provide broad opportunities to solve many classical Boolean-based problems as Hamiltonians, for the practical engineering applications of several algorithms, digital synthesizers, robotics, machine learning, just to name a few, in the hybrid classical-quantum domain.

DOI
10.17504/protocols.io.dm6gp9bd8vzp/v1

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The BHT-QAOA is a hybrid classical-quantum algorithm that solves arbitrary classical Boolean problems as Hamiltonians in the quantum domain, using the quantum approximate optimization algorithm (QAOA) [1]. The BHT-QAOA stands for the "Boolean-Hamiltonians Transform for QAOA" [2]. Research and studies are mainly focused on solving combinatorial optimization problems using QAOA, e.g., the MaxCut problem [1]. However, the BHT-QAOA adds an additional capability to QAOA to find all optimized approximated solutions for classical Boolean problems, as expressed in the following steps and demonstrated in the figure below. Design classical Boolean problems into quantum Boolean oracles in different logical structures, such as POS, SOP, ESOP, CSP-SAT, XOR-SAT, just to name a few. Convert these quantum Boolean oracles into quantum Phase oracles. Transform these quantum Phase oracles into the Hamiltonians (HC and HM) of QAOA. Please observe that, from the aforementioned steps, the total utilized numbers of qubits and quantum gates are significantly reduced for the final generated Hamiltonians (HC and HM) of QAOA. Accordingly, the BHT-QAOA will provide broad opportunities to solve many classical Boolean-based problems as Hamiltonians, for the practical engineering applications of several algorithms, digital synthesizers, robotics, machine learning, just to name a few, in the hybrid classical-quantum domain.
A new methodology is introduced to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). This methodology is termed the "Boolean-Hamiltonians Transform for QAOA" (BHT-QAOA). Because a great deal of research and studies are mainly focused on solving combinatorial optimization problems using QAOA, the BHT-QAOA adds an additional capability to QAOA to find all optimized approximated solutions for Boolean problems, by transforming such problems from Boolean oracles (in different structures) into Phase oracles, and then into the Hamiltonians of QAOA. From such a transformation, we noticed that the total utilized numbers of qubits and quantum gates are dramatically minimized for the generated Hamiltonians of QAOA. In this article, arbitrary Boolean problems are examined by successfully solving them with our BHT-QAOA, using different structures based on various logic synthesis methods, an IBM quantum computer, and a classical optimization minimizer. Accordingly, the BHT-QAOA will provide broad opportunities to solve many classical Boolean-based problems as Hamiltonians, for the practical engineering applications of several algorithms, digital synthesizers, robotics, and machine learning, just to name a few, in the hybrid classical-quantum domain.
A new methodology is proposed to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). Our methodology successfully finds all optimized approximated solutions for Boolean problems, after converting them from Boolean oracles (in different structures) into Phase oracles, and then into the Hamiltonians of QAOA. From such a conversion, we noticed that the total utilized numbers of qubits and quantum gates are dramatically minimized for the final quantum circuits of Hamiltonians. In this paper, arbitrary classical Boolean problems are examined by successfully solving them with our proposed methodology, using structures based on various logic synthesis methods, an IBM quantum computer, and a classical optimization minimizer. Accordingly, this methodology will provide broad opportunities to solve many classical Boolean problems as Hamiltonians, for the practical engineering applications of several algorithms, robotics, machine learning, just to name a few, in the hybrid classical-quantum domain.
This article investigates the performance impact of five classical optimization approximation algorithms on our previously introduced quantum search algorithm, termed the Boolean–Hamiltonians Transform for Quantum Approximate Optimization Algorithm (BHT-QAOA), to effectively search for all best-approximated solutions for Boolean-based problems. These optimization approximation algorithms are BFGS, L-BFGS-B, SLSQP, COBYLA, and COBYQA. Their performance impact is evaluated and compared using two proposed performance metrics—(i) the final number of function evaluations (the lower numbers denote the best optimization approximation algorithms) and (ii) the final quality of qubit measurements (the higher values indicate all best-approximated solutions were found for a problem). Arbitrary classical Boolean problems in various logical structures were examined and evaluated using the BHT-QAOA, these five optimization approximation algorithms, and a simulated noisy model of an IBM quantum computer. Broadly, the BHT-QAOA, with these five algorithms, successfully finds all optimized approximated solutions for these problems. Specifically, both the BFGS and SLSQP algorithms successfully find all best-approximated solutions for these problems, in the context of fewer function evaluations and higher quality of qubit measurements.
The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum algorithm described as ansatzes that represent both the problem and the mixer Hamiltonians. Both are parameterizable unitary transformations executed on a quantum machine/simulator and whose parameters are iteratively optimized using a classical device to optimize the problem's expectation value. To do so, in each QAOA iteration, most of the literature uses a quantum machine/simulator to measure the QAOA outcomes. However, this poses a severe bottleneck considering that quantum machines are hardly constrained (e.g. long queuing, limited qubits, etc.), likewise, quantum simulation also induces exponentially-increasing memory usage when dealing with large problems requiring more qubits. These limitations make today's QAOA implementation impractical since it is hard to obtain good solutions with a reasonably-acceptable time/resources. Considering these facts, this work presents a new approach with two main contributions, including (I) removing the need for accessing quantum devices or large-sized classical machines during the QAOA optimization phase, and (II) ensuring that when dealing with some k-bounded pseudo-Boolean problems, optimizing the exact problem's expectation value can be done in polynomial time using a classical computer.
Quantum Approximate Optimization Algorithm (QAOA) provides approximate solution to combinatorial optimization problems. It encodes the cost function using appp-level quantum circuit where each level consists a problem Hamiltonian followed by a mixing Hamiltonian. Despite the promises, few real-world applications (besides the pedagogical MaxCut problem) have exploited QAOA. The success of QAOA relies on the classical optimizer, variational parameter setting, and quantum circuit design and compilation. In this study, we implement QAOA and analyze its performance for a broader Quadratic Unconstrained Binary Optimization (QUBO) formulation to solve real-word applications such as, partially occluded object detection problem. Furthermore, we analyze the effects of above influential factors on QAOA performance. We propose a 3-level improvement of hybrid quantum-classical optimization for object detection. We achieve more than 13X execution speedup by choosing L-BFGS-B as classical optimizer at the first level and 5.50X additional speedup by exploiting parameter symmetry and more than 1.23X acceleration using parameter regression at the second level. We empirically show that the circuit will achieve better fidelity by optimally rescheduling gate operations (especially for deeper circuits) at the third level.
One of the most prominent application areas for quantum computers is solving hard constraint satisfaction and optimization problems. However, detailed analyses of the complexity of standard quantum algorithms have suggested that outperforming classical methods for these problems would require extremely large and powerful quantum computers. The quantum approximate optimization algorithm (QAOA) is designed for near-term quantum computers, yet previous work has shown strong limitations on the ability of QAOA to outperform classical algorithms for optimization problems. Here we instead apply QAOA to hard constraint satisfaction problems, where both classical and quantum algorithms are expected to require exponential time. We analytically characterize the average success probability of QAOA on a constraint satisfaction problem commonly studied using statistical physics methods: random k -SAT at the threshold for satisfiability, as the number of variables n goes to infinity. We complement these theoretical results with numerical experiments on the performance of QAOA for small n , which match the limiting theoretical bounds closely. We then compare QAOA with leading classical solvers. For random 8-SAT, we find that for more than 14 quantum circuit layers, QAOA achieves more efficient scaling than the highest-performance classical solver we tested, WalkSATlm. Our results suggest that near-term quantum algorithms for solving constraint satisfaction problems may outperform their classical counterparts. Published by the American Physical Society 2024