QGo: Scalable Quantum Circuit Optimization Using Automated Synthesis

• URL: http://arxiv.org/abs/2012.09835v5
• Date: Wed, 23 Mar 2022 17:58:00 GMT
• Title: QGo: Scalable Quantum Circuit Optimization Using Automated Synthesis
• Authors: Xin-Chuan Wu, Marc Grau Davis, Frederic T. Chong, Costin Iancu
• Abstract summary: 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.
• Score: 3.284627771501259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The current phase of quantum computing is in the Noisy Intermediate-Scale Quantum (NISQ) era. On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates, so it is essential to minimize their count. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity. However, the time-to-solution of synthesis grows exponentially with the number of qubits. As a result, synthesis is intractable for circuits on a large qubit scale. In this paper, we propose a hierarchical, block-by-block optimization framework, QGo, for quantum circuit optimization. Our approach allows an exponential cost optimization to scale to large circuits. QGo uses a combination of partitioning and synthesis: 1) partition the circuit into a sequence of independent circuit blocks; 2) re-generate and optimize each block using quantum synthesis; and 3) re-compose the final circuit by stitching all the blocks together. We perform our analysis and show the fidelity improvements in three different regimes: small-size circuits on real devices, medium-size circuits on noise simulations, and large-size circuits on analytical models. Using a set of NISQ benchmarks, we show that QGo can reduce the number of CNOT gates by 29.9% on average and up to 50% when compared with industrial compilers such as t|ket>. When executed on the IBM Athens system, shorter depth leads to higher circuit fidelity. We also demonstrate the scalability of our QGo technique to optimize circuits of 60+ qubits. Our technique is the first demonstration of successfully employing and scaling synthesis in the compilation toolchain for large circuits. Overall, our approach is robust for direct incorporation in production compiler toolchains.

Related papers

• Optimizing Quantum Circuits, Fast and Slow [7.543907169342984]
  We present a framework for thinking of rewriting and resynthesis as abstract circuit transformations. We then present a radically simple algorithm, GUOQ, for optimizing quantum circuits.
  arXiv  Detail & Related papers  (2024-11-06T18:34:35Z)
• Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
  We develop a reinforcement learning-based quantum compiler for a superconducting processor. We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
  arXiv  Detail & Related papers  (2024-06-18T01:49:48Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Nearest neighbor synthesis of CNOT circuits on general quantum architectures [12.363647205992951]
  This paper addresses the nearest neighbor synthesis of CNOT circuits in the architecture with and without Hamiltonian paths. A key-qubit priority mapping model for the general architecture with and without Hamiltonian paths is proposed. Experimental results show that the proposed method can enhance the fidelity of the CNOT circuit by about 64.7% on a real quantum computing device.
  arXiv  Detail & Related papers  (2023-10-01T06:30:58Z)
• 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)
• Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation [69.43216268165402]
  We present for the first time how to use graph neural network (GNN) autoencoders for the optimisation of quantum circuits. We construct directed acyclic graphs from the quantum circuits, encode the graphs and use the encodings to represent RL states. Our method is the first realistic first step towards very large scale RL quantum circuit optimisation.
  arXiv  Detail & Related papers  (2023-03-06T16:51:30Z)
• 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)
• A Structured Method for Compilation of QAOA Circuits in Quantum Computing [5.560410979877026]
  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.
  arXiv  Detail & Related papers  (2021-12-12T04:00:45Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• 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)

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.