Long-time simulations with high fidelity on quantum hardware
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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
- 2020Variational Quantum Algorithmscited by 687
- 2018Barren plateaus in quantum neural network training landscapescited by 624
- 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
- 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 124
- 2020Entanglement Induced Barren Plateauscited by 101
- 2020Layerwise learning for quantum neural networkscited by 87
- 2020Effect of barren plateaus on gradient-free optimizationcited by 79
- 2021Noisy intermediate-scale quantum (NISQ) algorithmscited by 79
- 2019Stochastic gradient descent for hybrid quantum-classical optimizationcited by 59
- 2020Trainability of Dissipative Perceptron-Based Quantum Neural Networkscited by 56
- 2020On barren plateaus and cost function locality in variational quantum algorithmscited by 36
- 2020Reformulation of the No-Free-Lunch Theorem for Entangled Data Setscited by 28
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Citing papers per year: 2021: 5; 2022: 1; 2023: 2.
8 papers.
- 2021Generalization in quantum machine learning from few training datacited by 214
- 2021Theory of overparametrization in quantum neural networkscited by 100
- 2023Trainability barriers and opportunities in quantum generative modelingcited by 35
- 2021A semi-agnostic ansatz with variable structure for quantum machine learningcited by 15
- 2021Optimal training of variational quantum algorithms without barren plateauscited by 15
- 2022Dynamical simulation via quantum machine learning with provable generalizationcited by 15
- 2021Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?cited by 14
- 2023The power and limitations of learning quantum dynamics incoherentlycited by 12
- 2023Trainability barriers and opportunities in quantum generative modeling4 May 2023
- 2023The power and limitations of learning quantum dynamics incoherently22 Mar 2023
- 2022Dynamical simulation via quantum machine learning with provable generalization21 Apr 2022
- 2021Generalization in quantum machine learning from few training data9 Nov 2021
- 2021Theory of overparametrization in quantum neural networks23 Sep 2021
- 2021Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?2 Sep 2021
- 2021Optimal training of variational quantum algorithms without barren plateaus29 Apr 2021
- 2021A semi-agnostic ansatz with variable structure for quantum machine learning11 Mar 2021