Digital Zero-Noise Extrapolation with Quantum Circuit Unoptimization

• URL: http://arxiv.org/abs/2503.06341v2
• Date: Wed, 16 Apr 2025 18:53:44 GMT
• Title: Digital Zero-Noise Extrapolation with Quantum Circuit Unoptimization
• Authors: Elijah Pelofske, Vincent Russo,
• Abstract summary: Quantum circuit unoptimization is an algorithm that transforms a quantum circuit into a different circuit.<n>By employing quantum circuit unoptimization as a form of circuit folding, noise can be systematically amplified.<n>We show that using quantum circuit unoptimization to perform ZNE can approximately recover signal from noisy quantum simulations.
• Score: 0.9208007322096533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum circuit unoptimization is an algorithm that transforms a quantum circuit into a different circuit that uses more gate operations while maintaining the same unitary transformation. We demonstrate that this method can implement digital zero-noise extrapolation (ZNE), a quantum error mitigation technique. By employing quantum circuit unoptimization as a form of circuit folding, noise can be systematically amplified. The key advantages of this approach are twofold. First, its ability to generate an exponentially increasing number of distinct circuit variants as the noise level is amplified, which allows noise averaging over many circuit instances with slightly different circuit structure which mitigates the effect of biased error propagation because of the significantly altered circuit structure from quantum circuit unoptimization, or highly biased local noise on a quantum processor. Second, quantum circuit unoptimization by design resists circuit simplification back to the original unmodified circuit, making it plausible to use ZNE in contexts where circuit compiler optimization is applied server-side. We evaluate the effectiveness of quantum circuit unoptimization as a noise-scaling method for ZNE in two test cases using depolarizing noise numerical simulations: random quantum volume circuits, where the observable is the heavy output probability, and QAOA circuits for the (unweighted) maximum cut problem on random 3-regular graphs, where the observable is the cut value. We show that using quantum circuit unoptimization to perform ZNE can approximately recover signal from noisy quantum simulations.

Related papers

• Optimization Driven Quantum Circuit Reduction [20.697821016522358]
  We propose three different transpilation approaches to substantially reduce circuit lengths without affecting functionality.<n>The first variant is based on a search scheme, and the other variants are driven by a database retrieval scheme and a machine learning based decision support.<n>We show that our proposed methods generate short quantum circuits for restricted gate sets, superior to the typical results obtained by using different qiskit optimization levels.
  arXiv  Detail & Related papers  (2025-02-20T16:41:10Z)
• 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)
• 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)
• 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)
• Scalable noisy quantum circuits for biased-noise qubits [37.69303106863453]
  We consider biased-noise qubits affected only by bit-flip errors, which is motivated by existing systems of stabilized cat qubits. For realistic noise models, phase-flip will not be negligible, but in the Pauli-Twirling approximation, we show that our benchmark could check the correctness of circuits containing up to $106$ gates.
  arXiv  Detail & Related papers  (2023-05-03T11:27:50Z)
• Characterization, synthesis, and optimization of quantum circuits over multiple-control $\textit{Z}$-rotation gates: A systematic study [4.385466953937176]
  We study quantum circuits composed of multiple-control $Z$-rotation (MCZR) gates as primitives. We present a gate-exchange strategy together with a flexible iterative algorithm for effectively optimizing the depth of any MCZR circuit.
  arXiv  Detail & Related papers  (2023-04-18T06:34:18Z)
• Efficient estimation of trainability for variational quantum circuits [43.028111013960206]
  We find an efficient method to compute the cost function and its variance for a wide class of variational quantum circuits. This method can be used to certify trainability for variational quantum circuits and explore design strategies that can overcome the barren plateau problem.
  arXiv  Detail & Related papers  (2023-02-09T14:05:18Z)
• Approximate Quantum Compiling for Quantum Simulation: A Tensor Network based approach [1.237454174824584]
  We introduce AQCtensor, a novel algorithm to produce short-depth quantum circuits from Matrix Product States (MPS) Our approach is specifically tailored to the preparation of quantum states generated from the time evolution of quantum many-body Hamiltonians. For simulation problems on 100 qubits, we show that AQCtensor achieves at least an order of magnitude reduction in the depth of the resulting optimized circuit.
  arXiv  Detail & Related papers  (2023-01-20T14:40:29Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• 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)
• 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)
• Boundaries of quantum supremacy via random circuit sampling [69.16452769334367]
  Google's recent quantum supremacy experiment heralded a transition point where quantum computing performed a computational task, random circuit sampling. We examine the constraints of the observed quantum runtime advantage in a larger number of qubits and gates.
  arXiv  Detail & Related papers  (2020-05-05T20:11:53Z)

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.