We develop new approximate compilation schemes that significantly reduce the expense of compiling the Quantum Approximate Optimization Algorithm (QAOA) for solving the Max-Cut problem. Our main focus is on compilation with trapped-ion simulators using Pauli- X operations and all-to-all Ising Hamiltonian H Ising evolution generated by Molmer-Sorensen or optical dipole force interactions, though some of our results also apply to standard gate-based compilations. Our results are based on principles of graph sparsification and decomposition; the former reduces the number of edges in a graph while maintaining its cut structure, while the latter breaks a weighted graph into a small number of unweighted graphs. Though these techniques have been used as heuristics in various hybrid quantum algorithms, there have been no guarantees on their performance, to the best of our knowledge. This work provides the first provable guarantees using sparsification and decomposition to improve quantum noise resilience and reduce quantum circuit complexity. For quantum hardware that uses edge-by-edge QAOA compilations, sparsification leads to a direct reduction in circuit complexity. For trapped-ion quantum simulators implementing all-to-all H I s i n g pulses, we show that for a ( 1 − ϵ ) factor loss in the Max-Cut approximation ( ϵ > 0 ) , our compilations improve the (worst-case) number of H I s i n g pulses from O ( n 2 ) to O ( n log ( n / ϵ ) ) and the (worst-case) number of Pauli- X bit flips from O ( n 2 ) to O ( n log ( n / ϵ ) ϵ 2 ) for n -node graphs. This is an asymptotic improvement for any constant ϵ > 0 . We demonstrate that significant improvements to the approximation ratio are obtained using decomposition in simulated trapped-ion experiments with dephasing noise. We further present a generic argument showing that sparsification results in an exponentially improved circuit fidelity lower bound in digital computing schemes based on one- and two-qubit gates, which are relevant to a wide variety of hardwares such as superconducting qubits and certain neutral atom or trapped ion setups, and more sophisticated noise models. We anticipate these approximate compilation techniques will be useful tools in a variety of future quantum computing experiments.
We develop new approximate compilation schemes that significantly reduce the expense of compiling the Quantum Approximate Optimization Algorithm (QAOA) for solving the Max-Cut problem. Our main focus is on compilation with trapped-ion simulators using Pauli- X operations and all-to-all Ising Hamiltonian H Ising evolution generated by Molmer-Sorensen or optical dipole force interactions, though some of our results also apply to standard gate-based compilations. Our results are based on principles of graph sparsification and decomposition; the former reduces the number of edges in a graph while maintaining its cut structure, while the latter breaks a weighted graph into a small number of unweighted graphs. Though these techniques have been used as heuristics in various hybrid quantum algorithms, there have been no guarantees on their performance, to the best of our knowledge. This work provides the first provable guarantees using sparsification and decomposition to improve quantum noise resilience and reduce quantum circuit complexity. For quantum hardware that uses edge-by-edge QAOA compilations, sparsification leads to a direct reduction in circuit complexity. For trapped-ion quantum simulators implementing all-to-all H I s i n g pulses, we show that for a ( 1 − ϵ ) factor loss in the Max-Cut approximation ( ϵ > 0 ) , our compilations improve the (worst-case) number of H I s i n g pulses from O ( n 2 ) to O ( n log ( n / ϵ ) ) and the (worst-case) number of Pauli- X bit flips from O ( n 2 ) to O ( n log ( n / ϵ ) ϵ 2 ) for n -node graphs. This is an asymptotic improvement for any constant ϵ > 0 . We demonstrate that significant improvements to the approximation ratio are obtained using decomposition in simulated trapped-ion experiments with dephasing noise. We further present a generic argument showing that sparsification results in an exponentially improved circuit fidelity lower bound in digital computing schemes based on one- and two-qubit gates, which are relevant to a wide variety of hardwares such as superconducting qubits and certain neutral atom or trapped ion setups, and more sophisticated noise models. We anticipate these approximate compilation techniques will be useful tools in a variety of future quantum computing experiments.
Abstract Quantum simulation on noisy intermediate-scale quantum (NISQ) hardware is limited by short coherence time and gate errors. We present a universal variational compilation optimization method (VCOM) that directly leverages the native gate set of trapped-ion processors, namely single-qubit rotations and global Ising-type XX interactions, to construct shallow circuits for simulating molecular Hamiltonians. Specifically, VCOM employs a hybrid optimization strategy combining genetic algorithms with gradient-based refinement to compile Pauli string evolution operators e −i Pt , thereby substantially reducing circuit depth and multi-qubit gate overhead in Hamiltonian simulation. Applied to hydrogen chains, the compiled circuits achieve fidelities comparable to standard decompositions while reducing the proportion of two-qubit operations to only 1.9%–4.6% of the total native gates, a significant improvement over the standard second-order Trotter–Suzuki product formula. Under experimentally motivated laser-intensity and phase-noise models, the proposed scheme maintains high fidelity and exhibits significantly slower degradation than direct native gate decompositions of conventional circuits. These results establish native-gate-based variational compilation as a scalable and practical approach for digital quantum simulation on trapped-ion platforms, with potential extensions to strongly correlated systems and non-equilibrium dynamics.
We study a cost Hamiltonian compilation problem for the quantum approximate optimization algorithm (QAOA) applied to the Max-Cut problem, focusing on trapped-ion quantum computers. Instead of standard compilation with CNOT and Rz gates, we employ global coupling operations and single-qubit bit flips. Prior work by Rajakumar et al. established that such a compilation is always possible. Minimizing operational error requires short operation sequences. The problem reduces to a low-rank semi-discrete decomposition of the graph's adjacency matrix, where the minimum achievable rank, the graph coupling number gc(G), represents the number of global control layers. Rajakumar et al. introduced the Union of Stars construction, proving gc(G) <= 3n - 2 for unweighted graphs with n vertices, and gave an O(m)-rank construction for weighted graphs. We concentrate on unweighted graphs. We derive structural properties of the compilation problem and show the Union of Stars method is order-optimal by proving a lower bound of gc(G) >= n - 1 for a family of graphs. We also improve the general upper bound to 2.5n + 2. For particular graph families -- cliques, perfect matchings, paths, and cycles -- we provide sharper bounds. Further, we reveal a link between the problem and Hadamard matrix theory. Finally, we introduce a compact mixed-integer programming (MIP) formulation that outperforms the previously studied exponential-size MIP.
Abstract Graph cut, a combinatorial optimization, is explored in quantum computing, seeking optimal bi-partitions of a directed or undirected, weighted or unweighted graph. The cut models, such as max-cut and min-cut, are applied in domains including VLSI design, circuit partitioning, data clustering, and network analysis. This paper explores the Ising modeling of max-cut and min-cut in a Laplacian-based QAOA, including benchmarking, spectral analysis, and a detailed analysis of the results. It aims to discover how Ising cut modeling and QAOA perform on a structured graph class, such as a weighted chordal graph. Chordal graphs offer decomposition properties favorable to QUBO and Ising models. Experimental results show that the proposed QAOA framework yields optimal or near-optimal cut values with high and stable approximation ratios across circuit depths. Also, hardware-aware considerations are introduced through superconducting qubit layout design and eigenmode simulations in Qiskit Metal and Ansys EPR HFSS. Further analysis reveals challenges related to convergence and performance in denser topologies. The study gives the modeling and the comprehensive evaluation of QAOA for graph-cuts, including a conceptual hardware-aware analysis.
We study an operation compilation problem, associated with the hybrid quantum-classical algorithm, QAOA, for the max-cut problem on trapped-ion quantum computers. As opposed to the standard CNOT and Rz gate compilation of the cost Hamiltonian for QAOA, here the goal is to compile it to global coupling operations and individual qubit bit flips. Rajakumar et al have proposed this compilation and showed such a compilation exists for any graph. To mitigate operation error a short sequence of operations is desired. In essence, the problem is a low-rank semi-discrete matrix decomposition for the adjacency matrix of a given graph. The lowest possible rank for this decomposition is known as the graph coupling number, gc(G), where G is the input graph. They gave a combinatorial construction, named union of stars, with the rank of at most 3n−2 for any unweighted graph with n vertices. They gave an O(m) construction for weighted graphs as well. Here we focus solely on the unweighted graphs. We extract important theoretical properties of the problem. Utilizing these properties we prove the order-optimality of the union of stars algorithm for unweighted graphs by introducing a family of graphs with a lower bound of n - 1 on their gc number. Additionally, we decrease the theoretical upper bound from 3n−2 to 2.5n+2 for any unweighted graph with n vertices. For specific graph families like cliques, perfect matching graphs, paths, and cycles, we find tighter bounds. Additionally, we propose a compact mixed integer program (MIP) which is competitive against their MIP formulation which has exponential size in the input graph. We also discover a beautiful connection of the problem with the Hadamard matrices.
Quantum Approximate Optimization Algorithm (QAOA) is studied primarily to find approximate solutions to combinatorial optimization problems. For a graph with n vertices and m edges, a depth p QAOA for the Max-cut problem requires 2⋅m⋅p CNOT gates. CNOT is one of the primary sources of error in modern quantum computers. In this paper, we propose two hardware independent methods to reduce the number of CNOT gates in the circuit. First, we present a method based on Edge Coloring of the input graph that minimizes the the number of cycles (termed as depth of the circuit), and reduces upto ⌊2n⌋ CNOT gates. Next, we depict another method based on Depth First Search (DFS) on the input graph that reduces n−1 CNOT gates, but increases depth of the circuit moderately. We analytically derive the condition for which the reduction in CNOT gates overshadows this increase in depth, and the error probability of the circuit is still lowered. We show that all IBM Quantum Hardware satisfy this condition. We simulate these two methods for graphs of various sparsity with the \textit{ibmq\_manhattan} noise model, and show that the DFS based method outperforms the edge coloring based method, which in turn, outperforms the traditional QAOA circuit in terms of reduction in the number of CNOT gates, and hence the probability of error of the circuit.
The max-cut problem (MCP) is a classic problem in the field of combinatorial optimization and has important applications in various fields, including statistical physics and image processing. However, except for some special cases, the MCP still encounters a non-deterministic polynomial complete problem (an NP-complete problem), and there is currently no known efficient classical algorithm that can solve it in polynomial time. The quantum approximate optimization algorithm (QAOA), as a pivotal algorithm in the noisy intermediate-scale quantum (NISQ) computing era, has shown significant potential for solving the MCP. However, due to the lack of quantum error correction, the reliability of computations in NISQ systems sharply declines as the circuit depth of the algorithm increases. Therefore, designing an efficient, shallow-depth, and low-complexity QAOA for the MCP is a critical challenge in demonstrating the advantages of quantum computing in the NISQ era.In this paper, according to the standard QAOA algorithm, we introduce Pauli Y rotation gates into the target Hamiltonian circuit for the MCP. By enhancing the flexibility of quantum trial functions and improving the efficiency of Hilbert space exploration within a single iteration, we significantly improve the performance of QAOA on the MCP.We conduct extensive numerical simulations using the MindSpore quantum platform, and compare the proposed RY-layer-assisted QAOA with standard QAOA and its existing variants, including MA-QAOA and QAOA+. The experiments are performed on various graph types, including complete graphs, 3-regular graphs, 4-regular graphs, and random graphs with edge probabilities between 0.3 and 0.5. Our results show that the RY-layer-assisted QAOA achieves higher approximation ratios in all graph types, particularly in regular and random graphs, where traditional QAOA variants are difficult to implement. Moreover, the proposed method exhibits strong robustness as the graph size increases, and can maintain high performance even for larger graphs. Importantly, the RY-layer-assisted QAOA requires fewer CNOT gates and has a lower circuit depth than the standard QAOA and its variants, making it more suitable for NISQ devices with limited coherence times and high error rates.In conclusion, the RY-layer-assisted QAOA provides a promising approach for solving MCP in the NISQ era. By improving the approximation ratio while reducing circuit complexity, this method demonstrates significant potential for practical quantum computing applications, thus paving the way for developing more efficient and reliable quantum optimization algorithms.