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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

Optimizing Quantum Reversible Circuits Using Reinforcement Learning

Sheng Yang, Guan‐Ju Peng

Published Nov 1, 2023• 0 citations
Publisher

Abstract

In light of the rapid advancements in quantum computing, the synthesis of quantum circuits has drawn significant attention. Quantum circuits are required to be reversible owing to their inherent physical characteristics. However, synthesizing such circuits and optimizing their performance present substantial challenges. In this paper, we proposed to adopt reinforcement learning for synthesizing the reversible circuits involving NOT, CNOT, Toffoli, and other relevant gates and, in doing so, minimize the quantum computational costs of the resulting circuits. The experimental results demonstrated that the reversible circuits generated using the proposed method outperform the prior methods in both the gate counts and the total cost.

DOI
10.1109/trustcom60117.2023.00326

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In light of the rapid advancements in quantum computing, the synthesis of quantum circuits has drawn significant attention. Quantum circuits are required to be reversible owing to their inherent physical characteristics. However, synthesizing such circuits and optimizing their performance present substantial challenges. In this paper, we proposed to adopt reinforcement learning for synthesizing the reversible circuits involving NOT, CNOT, Toffoli, and other relevant gates and, in doing so, minimize the quantum computational costs of the resulting circuits. The experimental results demonstrated that the reversible circuits generated using the proposed method outperform the prior methods in both the gate counts and the total cost.
The optimization of the limited resources of current quantum devices hinges on the implementation of quantum-compiling techniques. The process of transforming high-level quantum algorithms into executable quantum circuits has a significant impact on the performance and fidelity of computations. One of the most critical aspects of the compiling stack is the synthesis of quantum circuits, which aims to minimize the depth and number of gates, as well as the error rate. Nevertheless, the optimal synthesis algorithms are extremely computationally expensive. This is why synthesis algorithms based on reinforcement learning (RL) have recently been introduced. However, such algorithms are constrained by the topology of quantum processors, necessitating retraining of the RL agent for different topologies. To achieve a balance between adaptability, optimality, and computational cost in RL-based quantum circuit synthesis techniques, this work proposes an RL-based quantum circuit synthesis algorithm of Clifford operators that addresses different topologies simultaneously. Experimental tests demonstrate that our synthesizer outperforms state-of-the-art greedy techniques while being significantly faster than boolean satisfiability solvers (SAT).
With recent advancements in quantum computing technology, optimizing quantum circuits and ensuring reliable quantum state preparation have become increasingly vital. Traditional methods often demand extensive expertise and manual calculations, posing challenges as quantum circuits grow in qubit- and gate-count. Therefore, harnessing machine learning techniques to handle the growing variety of gate-to-qubit combinations is a promising approach. In this work, we introduce a comprehensive reinforcement learning environment for quantum circuit synthesis, where circuits are constructed utilizing gates from the the Clifford+T gate set to prepare specific target states. Our experiments focus on exploring the relationship between the depth of synthesized quantum circuits and the circuit depths used for target initialization, as well as qubit count. We organize the environment configurations into multiple evaluation levels and include a range of well-known quantum states for benchmarking purposes. We also lay baselines for evaluating the environment using Proximal Policy Optimization. By applying the trained agents to benchmark tests, we demonstrated their ability to reliably design minimal quantum circuits for a selection of 2-qubit Bell states.
In the Noisy Intermediate-Scale Quantum (NISQ) era, efficient quantum circuit synthesis is essential for optimizing limited qubit resources and mitigating noise effects. Quantum Linear Reversible Circuits (QLRCs) are a fundamental class of circuits with applications in quantum compilation or quantum error correction. They are composed exclusively of CX and SWAP gates, which are among the noisiest operations on current quantum hardware. Therefore, their efficient synthesis is pivotal for executing QLRCs on NISQ processors, which have restricted connectivity between qubits. In this direction, we introduce a Reinforcement Learning (RL)-based synthesis approach using Proximal Policy Optimization (PPO) to synthesize QLRCs on generic topologies of up to five qubits. This work extends previous work that did not handle different topologies of quantum processors. Our method dynamically adapts to hardware constraints, learning optimal or near-optimal gate decompositions. We compare our approach against Qiskit’s synthesis methods for QLRCs, demonstrating that our RL-based synthesizer consistently achieves lower CX gate depth. These results highlight the potential of RL-driven quantum circuit synthesis as a powerful alternative to traditional heuristic-based techniques, paving the way for more efficient AI-based quantum compilation strategies in the NISQ era.
This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a given unitary while minimizing circuit depth, total gate count, a specific gate count, or a combination of these factors. While past research has focused predominantly on continuous gate sets, synthesizing unitaries from the parameter-free Clifford+T gate set remains a challenge. Although the time complexity of this task will inevitably remain exponential in the number of qubits for general unitaries, reducing the runtime for simple problem instances still poses a significant challenge. In this study, we apply the tree-search method Gumbel AlphaZero to solve the problem for a subset of exactly synthesizable Clifford+T unitaries. Our method effectively synthesizes circuits for up to five qubits generated from randomized circuits with up to 60 gates, outperforming existing tools like QuantumCircuitOpt and MIN-T-SYNTH in terms of synthesis time for larger qubit counts. Furthermore, it surpasses Synthetiq in successfully synthesizing random, exactly synthesizable unitaries. These results establish a strong baseline for future unitary synthesis algorithms.
CNOT gates are fundamental to quantum computing, as they facilitate entanglement, a crucial resource for quantum algorithms. Certain classes of quantum circuits are constructed exclusively from CNOT gates. Given their widespread use, it is imperative to minimise the number of CNOT gates employed. This problem, known as CNOT minimization, remains an open challenge, with its computational complexity yet to be fully characterized. In this work, we introduce a novel reinforcement learning approach to address this task. Instead of training multiple reinforcement learning agents for different circuit sizes, we use a single agent up to a fixed sizemmm. Matrices of sizes different frommmmare preprocessed using either embedding or Gaussian striping. To assess the efficacy of our approach we trained an agent withm=8m=8m=8, and evaluated it on matrices of sizennnthat ranges from 3 to 15. The results we obtained show that our method overperforms the state of the art algorithm as the value ofnnnincreases.
This paper demonstrates the integration of Reinforcement Learning (RL) into quantum transpiling workflows, significantly enhancing the synthesis and routing of quantum circuits. By employing RL, we achieve near-optimal synthesis of Linear Function, Clifford, and Permutation circuits, up to 9, 11 and 65 qubits respectively, while being compatible with native device instruction sets and connectivity constraints, and orders of magnitude faster than optimization methods such as SAT solvers. We also achieve significant reductions in two-qubit gate depth and count for circuit routing up to 133 qubits with respect to other routing heuristics such as SABRE. We find the method to be efficient enough to be useful in practice in typical quantum transpiling pipelines. Our results set the stage for further AI-powered enhancements of quantum computing workflows.