Optimal Qubit Mapping with Simultaneous Gate Absorption

• URL: http://arxiv.org/abs/2109.06445v1
• Date: Tue, 14 Sep 2021 05:15:36 GMT
• Title: Optimal Qubit Mapping with Simultaneous Gate Absorption
• Authors: Bochen Tan and Jason Cong
• Abstract summary: A key step in compilation is mapping the qubits in the program to physical qubits on a given quantum computer. We present OLSQ-GA, an optimal qubit mapper with a key feature of simultaneous SWAP gate absorption. OLSQ-GA reduces depth by up to 50.0% and SWAP count by 100% compared to other state-of-the-art methods.
• Score: 9.530683922512873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Before quantum error correction (QEC) is achieved, quantum computers focus on noisy intermediate-scale quantum (NISQ) applications. Compared to the well-known quantum algorithms requiring QEC, like Shor's or Grover's algorithm, NISQ applications have different structures and properties to exploit in compilation. A key step in compilation is mapping the qubits in the program to physical qubits on a given quantum computer, which has been shown to be an NP-hard problem. In this paper, we present OLSQ-GA, an optimal qubit mapper with a key feature of simultaneous SWAP gate absorption during qubit mapping, which we show to be a very effective optimization technique for NISQ applications. For the class of quantum approximate optimization algorithm (QAOA), an important NISQ application, OLSQ-GA reduces depth by up to 50.0% and SWAP count by 100% compared to other state-of-the-art methods, which translates to 55.9% fidelity improvement. The solution optimality of OLSQ-GA is achieved by the exact SMT formulation. For better scalability, we augment our approach with additional constraints in the form of initial mapping or alternating matching, which speeds up OLSQ-GA by up to 272X with no or little loss of optimality.

Related papers

• Adaptive quantum optimization algorithms for programmable atom-cavity systems [6.508793834090864]
  We show cold atoms in an optical cavity can be built as a universal quantum with programmable all-to-all interactions. We find the success probability of the standard quantum approximate algorithm (QAOA) decays rapidly with the problem size. Inspired by the counterdiabatic driving, we propose an adaptive ansatz of QAOA which releases the parameter freedom of the NPP Hamiltonian to match higher-order counterdiabatic terms.
  arXiv  Detail & Related papers  (2024-06-11T08:37:31Z)
• 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)
• Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization [41.94295877935867]
  We propose a faster digital quantum algorithm for portfolio optimization using the digitized-counterdiabatic quantum optimization (DCQO) paradigm. Our approach notably reduces the circuit depth requirement of the algorithm and enhances the solution accuracy, making it suitable for current quantum processors. We experimentally demonstrate the advantages of our protocol using up to 20 qubits on an IonQ trapped-ion quantum computer.
  arXiv  Detail & Related papers  (2023-08-29T17:53:08Z)
• A Variational Qubit-Efficient MaxCut Heuristic Algorithm [0.0]
  We present a new variational Qubit-Efficient MaxCut (QEMC) algorithm that requires a logarithmic number of qubits with respect to the graph size. We demonstrate cutting-edge performance for graph instances consisting of up to 32 nodes (5 qubits) on real superconducting hardware, and for graphs with up to 2048 nodes (11 qubits) using noiseless simulations.
  arXiv  Detail & Related papers  (2023-08-20T23:06:18Z)
• Qubit efficient quantum algorithms for the vehicle routing problem on NISQ processors [48.68474702382697]
  Vehicle routing problem with time windows (VRPTW) is a common optimization problem faced within the logistics industry. In this work, we explore the use of a previously-introduced qubit encoding scheme to reduce the number of binary variables.
  arXiv  Detail & Related papers  (2023-06-14T13:44:35Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm [1.439946676159516]
  In this work, we investigate the verification of ReLU networks, which involves solving a robustness many-variable mixed-integer programs (MIPs) To alleviate this issue, we propose to use QC for neural network verification and introduce a hybrid quantum procedure to compute provable certificates. We show that, in a simulated environment, our certificate is sound, and provide bounds on the minimum number of qubits necessary to approximate the problem.
  arXiv  Detail & Related papers  (2022-05-02T13:23:56Z)
• Efficient Classical Computation of Quantum Mean Values for Shallow QAOA Circuits [15.279642278652654]
  We present a novel graph decomposition based classical algorithm that scales linearly with the number of qubits for the shallow QAOA circuits. Our results are not only important for the exploration of quantum advantages with QAOA, but also useful for the benchmarking of NISQ processors.
  arXiv  Detail & Related papers  (2021-12-21T12:41:31Z)
• 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)
• 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.