Quantum Multiplexer Simplification for State Preparation

• URL: http://arxiv.org/abs/2409.05618v1
• Date: Mon, 9 Sep 2024 13:53:02 GMT
• Title: Quantum Multiplexer Simplification for State Preparation
• Authors: José A. de Carvalho, Carlos A. Batista, Tiago M. L. de Veras, Israel F. Araujo, Adenilton J. da Silva,
• Abstract summary: We propose an algorithm that detects whether a given quantum state can be factored into substates. The simplification is done by eliminating controls of quantum multiplexers. Considering efficiency in terms of depth and number of CNOT gates, our method is competitive with the methods in the literature.
• Score: 0.7270112855088837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The initialization of quantum states or Quantum State Preparation (QSP) is a basic subroutine in quantum algorithms. In the worst case, general QSP algorithms are expensive due to the application of multi-controlled gates required to build them. Here, we propose an algorithm that detects whether a given quantum state can be factored into substates, increasing the efficiency of compiling the QSP circuit when we initialize states with some level of disentanglement. The simplification is done by eliminating controls of quantum multiplexers, significantly reducing circuit depth and the number of CNOT gates with a better execution and compilation time than the previous QSP algorithms. Considering efficiency in terms of depth and number of CNOT gates, our method is competitive with the methods in the literature. However, when it comes to run-time and compilation efficiency, our result is significantly better, and the experiments show that by increasing the number of qubits, the gap between the temporal efficiency of the methods increases.

Related papers

• Measurement-Based Long-Range Entangling Gates in Constant Depth [0.46040036610482665]
  We show how to reduce the depth of quantum sub-routines to a constant depth using mid-circuit measurements and feed-forward operations. We verify the feasibility by implementing the measurement-based quantum fan-out gate and long-range CNOT gate on real quantum hardware.
  arXiv  Detail & Related papers  (2024-08-06T09:35:42Z)
• 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 Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• 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)
• QuBEC: Boosting Equivalence Checking for Quantum Circuits with QEC Embedding [4.15692939468851]
  We propose a Decision Diagram-based quantum equivalence checking approach, QuBEC, that requires less latency compared to existing techniques. Our proposed methodology reduces verification time on certain benchmark circuits by up to $271.49 times$.
  arXiv  Detail & Related papers  (2023-09-19T16:12:37Z)
• GASP -- A Genetic Algorithm for State Preparation [0.0]
  We present a genetic algorithm for state preparation (GASP) which generates relatively low-depth quantum circuits for initialising a quantum computer in a specified quantum state. GASP can produce more efficient circuits of a given accuracy with lower depth and gate counts than other methods.
  arXiv  Detail & Related papers  (2023-02-22T04:41:01Z)
• Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
  We design, implement, and evaluate three hybrid quantum k-Means algorithms. We exploit quantum phenomena to speed up the computation of distances. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
  arXiv  Detail & Related papers  (2022-12-13T16:04:16Z)
• Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer [1.2019888796331233]
  We introduce a new circuit called AQCEL, which aims to remove redundant controlled operations from controlled gates. As a benchmark, the AQCEL is deployed on a quantum algorithm designed to model final state radiation in high energy physics. We have demonstrated that the AQCEL-optimized circuit can produce equivalent final states with much smaller number of gates.
  arXiv  Detail & Related papers  (2022-09-06T09:19:07Z)
• 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)
• 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)

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.