A Structured Method for Compilation of QAOA Circuits in Quantum Computing

• URL: http://arxiv.org/abs/2112.06143v4
• Date: Wed, 20 Jul 2022 00:56:08 GMT
• Title: A Structured Method for Compilation of QAOA Circuits in Quantum Computing
• Authors: Yuwei Jin, Jason Luo, Lucent Fong, Yanhao Chen, Ari B. Hayes, Chi Zhang, Fei Hua, Eddy Z. Zhang
• Abstract summary: The flexibility in reordering the two-qubit gates allows compiler optimizations to generate circuits with better depths, gate count, and fidelity. We propose a structured method that ensures linear depth for any compiled QAOA circuit on multi-dimensional quantum architectures. Overall, we can compile a circuit with up to 1024 qubits in 10 seconds with a 3.8X speedup in depth, 17% reduction in gate count, and 18X improvement for circuit ESP.
• Score: 5.560410979877026
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum Approximation Optimization Algorithm (QAOA) is a highly advocated variational algorithm for solving the combinatorial optimization problem. One critical feature in the quantum circuit of QAOA algorithm is that it consists of two-qubit operators that commute. The flexibility in reordering the two-qubit gates allows compiler optimizations to generate circuits with better depths, gate count, and fidelity. However, it also imposes significant challenges due to additional freedom exposed in the compilation. Prior studies lack the following: (1) Performance guarantee, (2) Scalability, and (3) Awareness of regularity in scalable hardware. We propose a structured method that ensures linear depth for any compiled QAOA circuit on multi-dimensional quantum architectures. We also demonstrate how our method runs on Google Sycamore and IBM Non-linear architectures in a scalable manner and in linear time. Overall, we can compile a circuit with up to 1024 qubits in 10 seconds with a 3.8X speedup in depth, 17% reduction in gate count, and 18X improvement for circuit ESP.

Related papers

• Coqa: Blazing Fast Compiler Optimizations for QAOA [3.165516590671437]
  We propose Coqa to optimize QAOA circuit compilation tailored to different types of quantum hardware. We are able to achieve an average of 30% reduction in gate count and a 39x acceleration in compilation time across our benchmarks.
  arXiv  Detail & Related papers  (2024-08-15T18:12:04Z)
• Compiling Quantum Circuits for Dynamically Field-Programmable Neutral Atoms Array Processors [5.012570785656963]
  Dynamically field-programmable qubit arrays (DPQA) have emerged as a promising platform for quantum information processing. In this paper, we consider a DPQA architecture that contains multiple arrays and supports 2D array movements. We show that our DPQA-based compiled circuits feature reduced scaling overhead compared to a grid fixed architecture.
  arXiv  Detail & Related papers  (2023-06-06T08:13:10Z)
• Wide Quantum Circuit Optimization with Topology Aware Synthesis [0.8469686352132708]
  Unitary synthesis is an optimization technique that can achieve optimal multi-qubit gate counts while mapping quantum circuits to restrictive qubit topologies. We present TopAS, a topology aware synthesis tool built with the emphBQSKit framework that preconditions quantum circuits before mapping.
  arXiv  Detail & Related papers  (2022-06-27T21:59:30Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• QGo: Scalable Quantum Circuit Optimization Using Automated Synthesis [3.284627771501259]
  On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates. We propose a hierarchical, block-by-block optimization framework, QGo, for quantum circuit optimization.
  arXiv  Detail & Related papers  (2020-12-17T18:54:38Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)
• Machine Learning Optimization of Quantum Circuit Layouts [63.55764634492974]
  We introduce a quantum circuit mapping, QXX, and its machine learning version, QXX-MLP. The latter infers automatically the optimal QXX parameter values such that the layed out circuit has a reduced depth. We present empiric evidence for the feasibility of learning the layout method using approximation.
  arXiv  Detail & Related papers  (2020-07-29T05:26:19Z)
• QUANTIFY: A framework for resource analysis and design verification of quantum circuits [69.43216268165402]
  QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits. It is based on Google Cirq and is developed with Clifford+T circuits in mind. For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
  arXiv  Detail & Related papers  (2020-07-21T15:36:25Z)
• Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets [47.00474212574662]
  Variational quantum algorithms are believed to be promising for solving computationally hard problems. In this paper, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances. Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
  arXiv  Detail & Related papers  (2020-05-11T17:20:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.