Enhancing variational Monte Carlo using a programmable quantum simulator
- URL: http://arxiv.org/abs/2308.02647v1
- Date: Fri, 4 Aug 2023 18:08:49 GMT
- Title: Enhancing variational Monte Carlo using a programmable quantum simulator
- Authors: M. Schuyler Moss, Sepehr Ebadi, Tout T. Wang, Giulia Semeghini,
Annabelle Bohrdt, Mikhail D. Lukin, and Roger G. Melko
- Abstract summary: We show that projective measurement data can be used to enhance in silico simulations of quantum matter.
We employ data-enhanced variational Monte Carlo to train powerful autoregressive wavefunction ans"atze based on recurrent neural networks.
Our work highlights the promise of hybrid quantum--classical approaches for large-scale simulation of quantum many-body systems.
- Score: 0.3078264203938486
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Programmable quantum simulators based on Rydberg atom arrays are a
fast-emerging quantum platform, bringing together long coherence times,
high-fidelity operations, and large numbers of interacting qubits
deterministically arranged in flexible geometries. Today's Rydberg array
devices are demonstrating their utility as quantum simulators for studying
phases and phase transitions in quantum matter. In this paper, we show that
unprocessed and imperfect experimental projective measurement data can be used
to enhance in silico simulations of quantum matter, by improving the
performance of variational Monte Carlo simulations. As an example, we focus on
data spanning the disordered-to-checkerboard transition in a $16 \times 16$
square lattice array [S. Ebadi et al. Nature 595, 227 (2021)] and employ
data-enhanced variational Monte Carlo to train powerful autoregressive
wavefunction ans\"atze based on recurrent neural networks (RNNs). We observe
universal improvements in the convergence times of our simulations with this
hybrid training scheme. Notably, we also find that pre-training with
experimental data enables relatively simple RNN ans\"atze to accurately capture
phases of matter that are not learned with a purely variational training
approach. Our work highlights the promise of hybrid quantum--classical
approaches for large-scale simulation of quantum many-body systems, combining
autoregressive language models with experimental data from existing quantum
devices.
Related papers
- Programmable Simulations of Molecules and Materials with Reconfigurable
Quantum Processors [0.3320294284424914]
We introduce a simulation framework for strongly correlated quantum systems that can be represented by model spin Hamiltonians.
Our approach leverages reconfigurable qubit architectures to programmably simulate real-time dynamics.
We show how this method can be used to compute key properties of a polynuclear transition-metal catalyst and 2D magnetic materials.
arXiv Detail & Related papers (2023-12-04T19:00:01Z) - Hybrid quantum learning with data re-uploading on a small-scale
superconducting quantum simulator [29.81784450632149]
Supervised quantum learning is an emergent multidisciplinary domain bridging between variational quantum algorithms and classical machine learning.
We train a quantum circuit on simple binary and multi-label tasks, achieving classification accuracy around 95%, and a hybrid model with data re-uploading with accuracy around 90% when recognizing handwritten decimal digits.
arXiv Detail & Related papers (2023-05-04T16:03:32Z) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - Quantum emulation of the transient dynamics in the multistate
Landau-Zener model [50.591267188664666]
We study the transient dynamics in the multistate Landau-Zener model as a function of the Landau-Zener velocity.
Our experiments pave the way for more complex simulations with qubits coupled to an engineered bosonic mode spectrum.
arXiv Detail & Related papers (2022-11-26T15:04:11Z) - Differentiable matrix product states for simulating variational quantum
computational chemistry [6.954927515599816]
We propose a parallelizable classical simulator for variational quantum eigensolver(VQE)
Our simulator seamlessly integrates the quantum circuit evolution into the classical auto-differentiation framework.
As applications, we use our simulator to study commonly used small molecules such as HF, LiH and H$$O, as well as larger molecules CO$$, BeH$ and H$_4$ with up to $40$ qubits.
arXiv Detail & Related papers (2022-11-15T08:36:26Z) - Simulating Hamiltonian dynamics in a programmable photonic quantum
processor using linear combinations of unitary operations [4.353492002036882]
We modify the multi-product Trotterization and combine it with the oblivious amplitude amplification to simultaneously reach a high simulation precision and high success probability.
We experimentally implement the modified multi-product algorithm in an integrated-photonics programmable quantum simulator in silicon.
arXiv Detail & Related papers (2022-11-12T18:49:41Z) - Probing finite-temperature observables in quantum simulators of spin
systems with short-time dynamics [62.997667081978825]
We show how finite-temperature observables can be obtained with an algorithm motivated from the Jarzynski equality.
We show that a finite temperature phase transition in the long-range transverse field Ising model can be characterized in trapped ion quantum simulators.
arXiv Detail & Related papers (2022-06-03T18:00:02Z) - Data-Enhanced Variational Monte Carlo Simulations for Rydberg Atom
Arrays [0.3425341633647624]
Rydberg atom arrays are programmable quantum simulators capable of preparing interacting qubit systems in a variety of quantum states.
In this paper, we demonstrate how pretraining modern RNNs on even small amounts of data significantly reduces the convergence time for a subsequent variational optimization of the wavefunction.
This suggests that essentially any amount of measurements obtained from a state prepared in an experimental quantum simulator could provide significant value for neural-network-based VMC strategies.
arXiv Detail & Related papers (2022-03-09T19:00:04Z) - Quantum algorithms for quantum dynamics: A performance study on the
spin-boson model [68.8204255655161]
Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator.
variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware.
We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
arXiv Detail & Related papers (2021-08-09T18:00:05Z) - Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits
at Exascale [57.84751206630535]
We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework.
The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators.
By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
arXiv Detail & Related papers (2021-04-21T13:26:42Z) - Quantum Markov Chain Monte Carlo with Digital Dissipative Dynamics on
Quantum Computers [52.77024349608834]
We develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits.
We evaluate the algorithm by simulating thermal states of the transverse Ising model.
arXiv Detail & Related papers (2021-03-04T18:21:00Z)
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.