Quantum Divide and Compute: Hardware Demonstrations and Noisy Simulations

• URL: http://arxiv.org/abs/2005.12874v1
• Date: Tue, 26 May 2020 17:08:13 GMT
• Title: Quantum Divide and Compute: Hardware Demonstrations and Noisy Simulations
• Authors: Thomas Ayral, Fran\c{c}ois-Marie Le R\'egent, Zain Saleem, Yuri Alexeev and Martin Suchara
• Abstract summary: We show a recently introduced method that breaks a circuit into smaller subcircuits or fragments. This makes it possible to run circuits that are either too wide or too deep for a given quantum processor. We investigate the behavior of the method on one of IBM's 20-qubit superconducting quantum processors.
• Score: 0.9659642285903421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Noisy, intermediate-scale quantum computers come with intrinsic limitations in terms of the number of qubits (circuit "width") and decoherence time (circuit "depth") they can have. Here, for the first time, we demonstrate a recently introduced method that breaks a circuit into smaller subcircuits or fragments, and thus makes it possible to run circuits that are either too wide or too deep for a given quantum processor. We investigate the behavior of the method on one of IBM's 20-qubit superconducting quantum processors with various numbers of qubits and fragments. We build noise models that capture decoherence, readout error, and gate imperfections for this particular processor. We then carry out noisy simulations of the method in order to account for the observed experimental results. We find an agreement within 20% between the experimental and the simulated success probabilities, and we observe that recombining noisy fragments yields overall results that can outperform the results without fragmentation.

Related papers

• 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)
• Optimized Noise Suppression for Quantum Circuits [0.40964539027092917]
  Crosstalk noise is a severe error source in, e.g., cross-resonance based superconducting quantum processors. Intrepid programming algorithm extends previous work on optimized qubit routing by swap insertion. We evaluate the proposed method by characterizing crosstalk noise for two chips with up to 127 qubits.
  arXiv  Detail & Related papers  (2024-01-12T07:34:59Z)
• 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)
• Enhancing Virtual Distillation with Circuit Cutting for Quantum Error Mitigation [8.464296815220935]
  We propose an error mitigation strategy that uses circuit-cutting technology to cut the entire circuit into fragments. The fragments responsible for generating the noisy quantum state can be executed on a noisy quantum device, while the remaining fragments are efficiently simulated on a noiseless classical simulator.
  arXiv  Detail & Related papers  (2023-10-07T06:59:08Z)
• Circuit Cutting with Non-Maximally Entangled States [59.11160990637615]
  Distributed quantum computing combines the computational power of multiple devices to overcome the limitations of individual devices. circuit cutting techniques enable the distribution of quantum computations through classical communication. Quantum teleportation allows the distribution of quantum computations without an exponential increase in shots. We propose a novel circuit cutting technique that leverages non-maximally entangled qubit pairs.
  arXiv  Detail & Related papers  (2023-06-21T08:03:34Z)
• Characterizing quantum processors using discrete time crystals [0.0]
  We present a method for characterizing the performance of noisy quantum processors using discrete time crystals. We construct small sets of qubit layouts that cover the full topology of a target system, and execute our metric over these sets on a wide range of IBM Quantum processors.
  arXiv  Detail & Related papers  (2023-01-18T16:08:50Z)
• Approximation Algorithm for Noisy Quantum Circuit Simulation [3.55689240295244]
  This paper introduces a novel approximation algorithm for simulating noisy quantum circuits. Our method offers a speedup over the commonly-used approximation (sampling) algorithm -- quantum trajectories method.
  arXiv  Detail & Related papers  (2022-11-30T14:20:22Z)
• Pulse-level noisy quantum circuits with QuTiP [53.356579534933765]
  We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian.
  arXiv  Detail & Related papers  (2021-05-20T17:06:52Z)
• Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Era [0.0]
  NISQ devices have several physical limitations and unavoidable noisy quantum operations. Only small circuits can be executed on a quantum machine to get reliable results. We propose a Quantum Multi-programming Compiler (QuMC) to execute multiple quantum circuits on quantum hardware simultaneously.
  arXiv  Detail & Related papers  (2021-02-10T08:46:16Z)
• A deep learning model for noise prediction on near-term quantum devices [137.6408511310322]
  We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model. A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise.
  arXiv  Detail & Related papers  (2020-05-21T17:47:29Z)
• 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.