PCOAST: A Pauli-based Quantum Circuit Optimization Framework

• URL: http://arxiv.org/abs/2305.10966v3
• Date: Tue, 23 May 2023 18:22:38 GMT
• Title: PCOAST: A Pauli-based Quantum Circuit Optimization Framework
• Authors: Jennifer Paykin, Albert T. Schmitz, Mohannad Ibrahim, Xin-Chuan Wu, A. Y. Matsuura
• Abstract summary: PCOAST is a framework for quantum circuit optimizations based on the commutative properties of Pauli strings. We evaluate its compilation performance against two leading quantum compilers, Qiskit and tket.
• Score: 0.3974852803981997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents the Pauli-based Circuit Optimization, Analysis, and Synthesis Toolchain (PCOAST), a framework for quantum circuit optimizations based on the commutative properties of Pauli strings. Prior work has demonstrated that commuting Clifford gates past Pauli rotations can expose opportunities for optimization in unitary circuits. PCOAST extends that approach by adapting the technique to mixed unitary and non-unitary circuits via generalized preparation and measurement nodes parameterized by Pauli strings. The result is the PCOAST graph, which enables novel optimizations based on whether a user needs to preserve the quantum state after executing the circuit, or whether they only need to preserve the measurement outcomes. Finally, the framework adapts a highly tunable greedy synthesis algorithm to implement the PCOAST graph with a given gate set. PCOAST is implemented as a set of compiler passes in the Intel Quantum SDK. In this paper, we evaluate its compilation performance against two leading quantum compilers, Qiskit and tket. We find that PCOAST reduces total gate count by 32.53% and 43.33% on average, compared to to the best performance achieved by Qiskit and tket respectively, two-qubit gates by 29.22% and 20.58%, and circuit depth by 42.02% and 51.27%.

Related papers

• 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)
• 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)
• Optimization at the Interface of Unitary and Non-unitary Quantum Operations in PCOAST [0.3496513815948205]
  Pauli-based Circuit Optimization, Analysis and Synthesis Toolchain (PCOAST) introduced as framework for optimizing quantum circuits. In this paper, we focus on the set of subroutines which look to optimize the PCOAST graph in cases involving unitary and non-unitary operations. We evaluate the PCOAST optimization subroutines using the Intel Quantum SDK on examples of the Variational Quantum Eigensolver (VQE) algorithm.
  arXiv  Detail & Related papers  (2023-05-16T22:58:14Z)
• 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)
• Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits [15.9208532173357]
  We propose a novel quantum compiler optimization, named relaxed peephole optimization (RPO) for quantum computers. We define a qubit is in a basis state when, at a given point in time, its state is either in the X-, Y-, or Z-basis. We extend our approach to optimize the quantum gates when some input qubits are in known pure states.
  arXiv  Detail & Related papers  (2020-12-14T17:03:06Z)
• Coherent randomized benchmarking [68.8204255655161]
  We show that superpositions of different random sequences rather than independent samples are used. We show that this leads to a uniform and simple protocol with significant advantages with respect to gates that can be benchmarked.
  arXiv  Detail & Related papers  (2020-10-26T18:00:34Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• 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)
• A Generic Compilation Strategy for the Unitary Coupled Cluster Ansatz [68.8204255655161]
  We describe a compilation strategy for Variational Quantum Eigensolver (VQE) algorithms. We use the Unitary Coupled Cluster (UCC) ansatz to reduce circuit depth and gate count.
  arXiv  Detail & Related papers  (2020-07-20T22:26:16Z)
• 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.