Optimizing Quantum Circuits, Fast and Slow

• URL: http://arxiv.org/abs/2411.04104v1
• Date: Wed, 06 Nov 2024 18:34:35 GMT
• Title: Optimizing Quantum Circuits, Fast and Slow
• Authors: Amanda Xu, Abtin Molavi, Swamit Tannu, Aws Albarghouthi,
• Abstract summary: 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.
• Score: 7.543907169342984
• License:
• Abstract: Optimizing quantum circuits is critical: the number of quantum operations needs to be minimized for a successful evaluation of a circuit on a quantum processor. In this paper we unify two disparate ideas for optimizing quantum circuits, rewrite rules, which are fast standard optimizer passes, and unitary synthesis, which is slow, requiring a search through the space of circuits. We present a clean, unifying framework for thinking of rewriting and resynthesis as abstract circuit transformations. We then present a radically simple algorithm, GUOQ, for optimizing quantum circuits that exploits the synergies of rewriting and resynthesis. Our extensive evaluation demonstrates the ability of GUOQ to strongly outperform existing optimizers on a wide range of benchmarks.

Related papers

• 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)
• Quantum Circuit Unoptimization [0.6449786007855248]
  We construct a quantum algorithmic primitive called quantum circuit unoptimization. It makes a given quantum circuit complex by introducing some redundancies while preserving circuit equivalence. We use quantum circuit unoptimization to generate compiler benchmarks and evaluate circuit optimization performance.
  arXiv  Detail & Related papers  (2023-11-07T08:38:18Z)
• Quantum Circuit Optimization through Iteratively Pre-Conditioned Gradient Descent [0.4915744683251151]
  iteratively preconditioned gradient descent (IPG) for optimizing quantum circuits and demonstrate performance speedups for state preparation and implementation of quantum algorithmics. We show an improvement in fidelity by a factor of $104$ for preparing a 4-qubit W state and a maximally entangled 5-qubit GHZ state compared to other commonly used classicals tuning the same ansatz. We also show gains for optimizing a unitary for a quantum Fourier transform using IPG, and report results of running such optimized circuits on IonQ's quantum processing unit (QPU)
  arXiv  Detail & Related papers  (2023-09-18T17:30:03Z)
• 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)
• Synthesizing Quantum-Circuit Optimizers [7.111661677477926]
  QUESO is an efficient approach for automatically synthesizing a quantum-circuit for a given quantum device. For an instance, in 1.2 minutes, QUESO can synthesize an correctness with high-probability guarantees.
  arXiv  Detail & Related papers  (2022-11-17T17:30:20Z)
• Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network [64.1951227380212]
  We propose that quantum circuits can be modeled as queuing networks. Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.
  arXiv  Detail & Related papers  (2021-06-26T10:55:52Z)
• 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)
• Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications [1.6329956884407544]
  We introduce two ideas for circuit optimization and combine them in a multi-tiered quantum circuit optimization protocol called AQCEL. AQCEL is deployed on an iterative and efficient quantum algorithm designed to model final state radiation in high energy physics. Our technique is generic and can be useful for a wide variety of quantum algorithms.
  arXiv  Detail & Related papers  (2021-02-19T16:20:31Z)
• 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)
• 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)
• Compilation of Fault-Tolerant Quantum Heuristics for Combinatorial Optimization [0.14755786263360526]
  We explore which quantum algorithms for optimization might be most practical to try out on a small fault-tolerant quantum computer. Our results discourage the notion that any quantum optimization realizing only a quadratic speedup will achieve an advantage over classical algorithms.
  arXiv  Detail & Related papers  (2020-07-14T22:54:04Z)

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.