mlsanityData to 7 Oct 2026

Long-time simulations with high fidelity on quantum hardware

arXiv 2102.04313 ↗First posted 8 Feb 2021quant-ph cs.LG stat.MLcited by 8

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Abstract (arXiv)

Moderate-size quantum computers are now publicly accessible over the cloud, opening the exciting possibility of performing dynamical simulations of quantum systems. However, while rapidly improving, these devices have short coherence times, limiting the depth of algorithms that may be successfully implemented. Here we demonstrate that, despite these limitations, it is possible to implement long-time, high fidelity simulations on current hardware. Specifically, we simulate an XY-model spin chain on the Rigetti and IBM quantum computers, maintaining a fidelity of at least 0.9 for over 600 time steps. This is a factor of 150 longer than is possible using the iterated Trotter method. Our simulations are performed using a new algorithm that we call the fixed state Variational Fast Forwarding (fsVFF) algorithm. This algorithm decreases the circuit depth and width required for a quantum simulation by finding an approximate diagonalization of a short time evolution unitary. Crucially, fsVFF only requires finding a diagonalization on the subspace spanned by the initial state, rather than on the total Hilbert space as with previous methods, substantially reducing the required resources. We further demonstrate the viability of fsVFF through large numerical implementations of the algorithm, as well as an analysis of its noise resilience and the scaling of simulation errors.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 13 of its 63 references that are in the map. First posted on arXiv 8 Feb 2021.

Builds on

13 of 63 references.

References · 13

  1. 2020Variational Quantum Algorithmscited by 687
  2. 2018Barren plateaus in quantum neural network training landscapescited by 624
  3. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  4. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 124
  5. 2020Entanglement Induced Barren Plateauscited by 101
  6. 2020Layerwise learning for quantum neural networkscited by 87
  7. 2020Effect of barren plateaus on gradient-free optimizationcited by 79
  8. 2021Noisy intermediate-scale quantum (NISQ) algorithmscited by 79
  9. 2019Stochastic gradient descent for hybrid quantum-classical optimizationcited by 59
  10. 2020Trainability of Dissipative Perceptron-Based Quantum Neural Networkscited by 56
  11. 2020On barren plateaus and cost function locality in variational quantum algorithmscited by 36
  12. 2020Reformulation of the No-Free-Lunch Theorem for Entangled Data Setscited by 28
1 more
  1. 2020Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuitscited by 26

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Cited by year

Citing papers per year: 2021: 5; 2022: 1; 2023: 2.

8 papers.