Efficient quantum gate decomposition via adaptive circuit compression

• URL: http://arxiv.org/abs/2203.04426v2
• Date: Tue, 15 Nov 2022 11:34:53 GMT
• Title: Efficient quantum gate decomposition via adaptive circuit compression
• Authors: P\'eter Rakyta, Zolt\'an Zimbor\'as
• Abstract summary: The utilization of parametric two-qubit gates in the circuit design allows us to transform the discrete problem of circuit synthesis into an optimization problem over continuous variables. We implemented the algorithm in the SQUANDER software package and benchmarked it against several state-of-the-art quantum gate synthesis tools.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this work, we report on a novel quantum gate approximation algorithm based on the application of parametric two-qubit gates in the synthesis process. The utilization of these parametric two-qubit gates in the circuit design allows us to transform the discrete combinatorial problem of circuit synthesis into an optimization problem over continuous variables. The circuit is then compressed by a sequential removal of two-qubit gates from the design, while the remaining building blocks are continuously adapted to the reduced gate structure by iterated learning cycles. We implemented the developed algorithm in the SQUANDER software package and benchmarked it against several state-of-the-art quantum gate synthesis tools. Our numerical experiments revealed outstanding circuit compression capabilities of our compilation algorithm providing the most optimal gate count in the majority of the addressed quantum circuits.

Related papers

• Optimization Driven Quantum Circuit Reduction [20.697821016522358]
  We propose three different transpilation approaches to substantially reduce circuit lengths without affecting functionality. The first variant is based on a search scheme, and the other variants are driven by a database retrieval scheme and a machine learning based decision support. We show that our proposed methods generate short quantum circuits for restricted gate sets, superior to the typical results obtained by using different qiskit optimization levels.
  arXiv  Detail & Related papers  (2025-02-20T16:41:10Z)
• Efficient compilation of quantum circuits using multi-qubit gates [0.0]
  We present a compilation scheme which implements a general-circuit decomposition to a sequence of Ising-type, long-range, multi-qubit entangling gates. We numerically test our compilation and show that, compared to conventional realizations with two-qubit gates, our compilations improves the logarithm of quantum volume by $20%$ to $25%$.
  arXiv  Detail & Related papers  (2025-01-28T19:08:13Z)
• On the Constant Depth Implementation of Pauli Exponentials [49.48516314472825]
  We decompose arbitrary exponentials into circuits of constant depth using $mathcalO(n)$ ancillae and two-body XX and ZZ interactions. We prove the correctness of our approach, after introducing novel rewrite rules for circuits which benefit from qubit recycling.
  arXiv  Detail & Related papers  (2024-08-15T17:09:08Z)
• Redefining Lexicographical Ordering: Optimizing Pauli String Decompositions for Quantum Compiling [0.0]
  We propose a novel algorithm for the synthesis of trotterized time-evolution operators. Our synthesis procedure takes the qubit connectivity of a target quantum computer into account. We show a significant improvement for randomized circuits and different molecular ansatzes.
  arXiv  Detail & Related papers  (2024-08-01T07:50:16Z)
• 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)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Characterization, synthesis, and optimization of quantum circuits over multiple-control $\textit{Z}$-rotation gates: A systematic study [4.385466953937176]
  We study quantum circuits composed of multiple-control $Z$-rotation (MCZR) gates as primitives. We present a gate-exchange strategy together with a flexible iterative algorithm for effectively optimizing the depth of any MCZR circuit.
  arXiv  Detail & Related papers  (2023-04-18T06:34:18Z)
• Applications of Universal Parity Quantum Computation [0.0]
  We demonstrate the applicability of a universal gate set in the parity encoding, which is a dual to the standard gate model. Embedding these algorithms in the parity encoding reduces the circuit depth compared to conventional gate-based implementations. We propose simple implementations of multiqubit gates in tailored encodings and an efficient strategy to prepare graph states.
  arXiv  Detail & Related papers  (2022-05-19T12:31:46Z)
• Software mitigation of coherent two-qubit gate errors [55.878249096379804]
  Two-qubit gates are important components of quantum computing. But unwanted interactions between qubits (so-called parasitic gates) can degrade the performance of quantum applications. We present two software methods to mitigate parasitic two-qubit gate errors.
  arXiv  Detail & Related papers  (2021-11-08T17:37:27Z)
• Accurate methods for the analysis of strong-drive effects in parametric gates [94.70553167084388]
  We show how to efficiently extract gate parameters using exact numerics and a perturbative analytical approach. We identify optimal regimes of operation for different types of gates including $i$SWAP, controlled-Z, and CNOT.
  arXiv  Detail & Related papers  (2021-07-06T02:02:54Z)
• 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.