Synthesizing Quantum-Circuit Optimizers

• URL: http://arxiv.org/abs/2211.09691v3
• Date: Wed, 10 May 2023 21:48:33 GMT
• Title: Synthesizing Quantum-Circuit Optimizers
• Authors: Amanda Xu, Abtin Molavi, Lauren Pick, Swamit Tannu, Aws Albarghouthi
• Abstract summary: 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.
• Score: 7.111661677477926
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modification of a quantum computer, we need to modify or rewrite major pieces of the optimizer to run successful experiments. In this paper, we present QUESO, an efficient approach for automatically synthesizing a quantum-circuit optimizer for a given quantum device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with high-probability correctness guarantees for IBM computers that significantly outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority (85%) of the circuits in a diverse benchmark suite. A number of theoretical and algorithmic insights underlie QUESO: (1) An algebraic approach for representing rewrite rules and their semantics. This facilitates reasoning about complex symbolic rewrite rules that are beyond the scope of existing techniques. (2) A fast approach for probabilistically verifying equivalence of quantum circuits by reducing the problem to a special form of polynomial identity testing. (3) A novel probabilistic data structure, called a polynomial identity filter (PIF), for efficiently synthesizing rewrite rules. (4) A beam-search-based algorithm that efficiently applies the synthesized symbolic rewrite rules to optimize quantum circuits.

Related papers

• Optimizing Quantum Circuits, Fast and Slow [7.543907169342984]
  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.
  arXiv  Detail & Related papers  (2024-11-06T18:34:35Z)
• 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 Worst-Case to Average-Case Reductions for All Linear Problems [66.65497337069792]
  We study the problem of designing worst-case to average-case reductions for quantum algorithms. We provide an explicit and efficient transformation of quantum algorithms that are only correct on a small fraction of their inputs into ones that are correct on all inputs.
  arXiv  Detail & Related papers  (2022-12-06T22:01:49Z)
• A Quantum Algorithm for Computing All Diagnoses of a Switching Circuit [73.70667578066775]
  Faults are by nature while most man-made systems, and especially computers, work deterministically. This paper provides such a connecting via quantum information theory which is an intuitive approach as quantum physics obeys probability laws.
  arXiv  Detail & Related papers  (2022-09-08T17:55:30Z)
• Approximate encoding of quantum states using shallow circuits [0.0]
  A common requirement of quantum simulations and algorithms is the preparation of complex states through sequences of 2-qubit gates. Here, we aim at creating an approximate encoding of the target state using a limited number of gates. Our work offers a universal method to prepare target states using local gates and represents a significant improvement over known strategies.
  arXiv  Detail & Related papers  (2022-06-30T18:00:04Z)
• 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)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)
• 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.