Efficient quantum-enhanced classical simulation for patches of quantum landscapes
- URL: http://arxiv.org/abs/2411.19896v1
- Date: Fri, 29 Nov 2024 18:00:07 GMT
- Title: Efficient quantum-enhanced classical simulation for patches of quantum landscapes
- Authors: Sacha Lerch, Ricard Puig, Manuel S. Rudolph, Armando Angrisani, Tyson Jones, M. Cerezo, Supanut Thanasilp, Zoƫ Holmes,
- Abstract summary: We show that it is always possible to generate a classical surrogate of a sub-region of an expectation landscape produced by a parameterized quantum circuit.
We provide a quantum-enhanced classical algorithm which, after simple measurements on a quantum device, allows one to classically simulate approximate expectation values of a subregion of a landscape.
- Score: 0.0
- License:
- Abstract: Understanding the capabilities of classical simulation methods is key to identifying where quantum computers are advantageous. Not only does this ensure that quantum computers are used only where necessary, but also one can potentially identify subroutines that can be offloaded onto a classical device. In this work, we show that it is always possible to generate a classical surrogate of a sub-region (dubbed a "patch") of an expectation landscape produced by a parameterized quantum circuit. That is, we provide a quantum-enhanced classical algorithm which, after simple measurements on a quantum device, allows one to classically simulate approximate expectation values of a subregion of a landscape. We provide time and sample complexity guarantees for a range of families of circuits of interest, and further numerically demonstrate our simulation algorithms on an exactly verifiable simulation of a Hamiltonian variational ansatz and long-time dynamics simulation on a 127-qubit heavy-hex topology.
Related papers
- Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Hybrid quantum gap estimation algorithm using a filtered time series [0.0]
We prove that classical post-processing, i.e., long-time filtering of an offline time series, exponentially improves the circuit depth needed for quantum time evolution.
We apply the filtering method to the construction of a hybrid quantum-classical algorithm to estimate energy gap.
Our findings set the stage for unbiased quantum simulation to offer memory advantage in the near term.
arXiv Detail & Related papers (2022-12-28T18:59:59Z) - QuDiet: A Classical Simulation Platform for Qubit-Qudit Hybrid Quantum
Systems [7.416447177941264]
textbfQuDiet is a python-based higher-dimensional quantum computing simulator.
textbfQuDiet offers multi-valued logic operations by utilizing generalized quantum gates.
textbfQuDiet provides a full qubit-qudit hybrid quantum simulator package.
arXiv Detail & Related papers (2022-11-15T06:07:04Z) - 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) - Classically optimized Hamiltonian simulation [0.0]
Hamiltonian simulation is a promising application for quantum computers.
We show that, compared to Trotter product formulas, the classically optimized circuits can be orders of magnitude more accurate.
arXiv Detail & Related papers (2022-05-23T16:14:43Z) - 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) - Doubling the size of quantum simulators by entanglement forging [2.309018557701645]
Quantum computers are promising for simulations of chemical and physical systems.
We present a method, classical entanglement forging, that harnesses classical resources to capture quantum correlations.
We compute the ground state energy of a water molecule in the most accurate simulation to date.
arXiv Detail & Related papers (2021-04-20T19:32:37Z) - Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
We present a constructive algorithm for generating quantum circuits with time-independent depth.
We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model.
In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
arXiv Detail & Related papers (2021-04-01T19:06:00Z) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z) - Classical variational simulation of the Quantum Approximate Optimization
Algorithm [0.0]
We introduce a method to simulate layered quantum circuits consisting of parametrized gates.
A neural-network parametrization of the many-qubit wave function is used.
For the largest circuits simulated, we reach 54 qubits at 4 QAOA layers.
arXiv Detail & Related papers (2020-09-03T15:55:27Z)
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.