Ancillary entangling Floquet kicks for accelerating quantum algorithms
- URL: http://arxiv.org/abs/2408.13345v1
- Date: Fri, 23 Aug 2024 19:40:24 GMT
- Title: Ancillary entangling Floquet kicks for accelerating quantum algorithms
- Authors: C. -C. Joseph Wang, Phillip C. Lotshaw, Titus Morris, Vicente Leyton-Ortega, Daniel Claudino, Travis S. Humble,
- Abstract summary: We accelerate quantum simulation using digital multi-qubit gates that entangle primary system qubits with the ancillary qubits.
For simple but nontrivial short-ranged, infinite long-ranged transverse-field Ising models, and the hydrogen molecule model after qubit encoding, we show improvement in the time to solution by one hundred percent.
- Score: 0.21990652930491855
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum simulation with adiabatic annealing can provide insight into difficult problems that are impossible to study with classical computers. However, it deteriorates when the systems scale up due to the shrinkage of the excitation gap and thus places an annealing rate bottleneck for high success probability. Here, we accelerate quantum simulation using digital multi-qubit gates that entangle primary system qubits with the ancillary qubits. The practical benefits originate from tuning the ancillary gauge degrees of freedom to enhance the quantum algorithm's original functionality in the system subspace. For simple but nontrivial short-ranged, infinite long-ranged transverse-field Ising models, and the hydrogen molecule model after qubit encoding, we show improvement in the time to solution by one hundred percent but with higher accuracy through exact state-vector numerical simulation in a digital-analog setting. The findings are further supported by time-averaged Hamiltonian theory.
Related papers
- Benchmarking a heuristic Floquet adiabatic algorithm for the Max-Cut problem [0.0]
We show that adiabatic evolution can be performed with a fixed, finite Trotter step.
We give numerical evidence using matrix-product-state simulations that it can optimally solve the Max-Cut problem.
Extrapolating our numerical results, we estimate the resources needed for a quantum computer to compete with classical exact or approximate solvers.
arXiv Detail & Related papers (2024-04-24T17:29:03Z) - Large-scale simulations of Floquet physics on near-term quantum computers [0.3252295747842729]
We introduce the Quantum High-Frequency Floquet Simulation (QHiFFS) algorithm as a method to simulate fast-driven quantum systems on quantum hardware.
Central to QHiFFS is the concept of a kick operator which transforms the system into a basis where the dynamics is governed by a time-independent effective Hamiltonian.
arXiv Detail & Related papers (2023-03-03T20:45:01Z) - 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) - Simulating the Mott transition on a noisy digital quantum computer via
Cartan-based fast-forwarding circuits [62.73367618671969]
Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model.
Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models.
This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
arXiv Detail & Related papers (2021-12-10T17:32:15Z) - 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) - An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian
Simulation [55.41644538483948]
Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in chip size and error rates.
We derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions.
The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond $mathcalO(103)$ spins.
arXiv Detail & Related papers (2021-08-06T19:38:03Z) - Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems.
We propose an algorithm to implement imaginary time propagation on a quantum computer.
arXiv Detail & Related papers (2021-02-24T12:48:00Z) - Continuous-time dynamics and error scaling of noisy highly-entangling
quantum circuits [58.720142291102135]
We simulate a noisy quantum Fourier transform processor with up to 21 qubits.
We take into account microscopic dissipative processes rather than relying on digital error models.
We show that depending on the dissipative mechanisms at play, the choice of input state has a strong impact on the performance of the quantum algorithm.
arXiv Detail & Related papers (2021-02-08T14:55:44Z) - Fast and differentiable simulation of driven quantum systems [58.720142291102135]
We introduce a semi-analytic method based on the Dyson expansion that allows us to time-evolve driven quantum systems much faster than standard numerical methods.
We show results of the optimization of a two-qubit gate using transmon qubits in the circuit QED architecture.
arXiv Detail & Related papers (2020-12-16T21:43:38Z) - Reinforcement Learning for Digital Quantum Simulation [0.0]
We introduce a reinforcement learning algorithm to build optimized quantum circuits for digital quantum simulation.
We consistently obtain quantum circuits that reproduce physical observables with as little as three entangling gates for long times and large system sizes.
arXiv Detail & Related papers (2020-06-29T18:00:11Z)
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.