Navigating the noise-depth tradeoff in adiabatic quantum circuits
- URL: http://arxiv.org/abs/2209.11245v2
- Date: Fri, 24 Mar 2023 09:45:29 GMT
- Title: Navigating the noise-depth tradeoff in adiabatic quantum circuits
- Authors: Daniel Azses, Maxime Dupont, Bram Evert, Matthew J. Reagor, Emanuele
G. Dalla Torre
- Abstract summary: On an ideal quantum computer, the solution quality improves monotonically with increasing circuit depth.
By contrast, increasing the depth in current noisy computers introduces more noise and deteriorates any computational advantage.
We implement this algorithm on a noisy superconducting quantum processor and find that the dependence of the density of defects on the circuit depth follows the predicted non-monotonous behavior.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Adiabatic quantum algorithms solve computational problems by slowly evolving
a trivial state to the desired solution. On an ideal quantum computer, the
solution quality improves monotonically with increasing circuit depth. By
contrast, increasing the depth in current noisy computers introduces more noise
and eventually deteriorates any computational advantage. What is the optimal
circuit depth that provides the best solution? Here, we address this question
by investigating an adiabatic circuit that interpolates between the
paramagnetic and ferromagnetic ground states of the one-dimensional quantum
Ising model. We characterize the quality of the final output by the density of
defects $d$, as a function of the circuit depth $N$ and noise strength
$\sigma$. We find that $d$ is well-described by the simple form
$d_\mathrm{ideal}+d_\mathrm{noise}$, where the ideal case $d_\mathrm{ideal}\sim
N^{-1/2}$ is controlled by the Kibble-Zurek mechanism, and the noise
contribution scales as $d_\mathrm{noise}\sim N\sigma^2$. It follows that the
optimal number of steps minimizing the number of defects goes as
$\sim\sigma^{-4/3}$. We implement this algorithm on a noisy superconducting
quantum processor and find that the dependence of the density of defects on the
circuit depth follows the predicted non-monotonous behavior and agrees well
with noisy simulations. Our work allows one to efficiently benchmark quantum
devices and extract their effective noise strength $\sigma$.
Related papers
- Optimizing random local Hamiltonians by dissipation [44.99833362998488]
We prove that a simplified quantum Gibbs sampling algorithm achieves a $Omega(frac1k)$-fraction approximation of the optimum.
Our results suggest that finding low-energy states for sparsified (quasi)local spin and fermionic models is quantumly easy but classically nontrivial.
arXiv Detail & Related papers (2024-11-04T20:21:16Z) - Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping [3.47862118034022]
Noise-Directed Remapping (NDAR) is a algorithm for approximately solving binary optimization problems by leveraging certain types of noise.
We consider access to a noisy quantum processor with dynamics that features a global attractor state.
Our algorithm bootstraps the noise attractor state by iteratively gauge-transforming the cost-function Hamiltonian in a way that transforms the noise attractor into higher-quality solutions.
arXiv Detail & Related papers (2024-04-01T18:28:57Z) - A two-circuit approach to reducing quantum resources for the quantum lattice Boltzmann method [41.66129197681683]
Current quantum algorithms for solving CFD problems use a single quantum circuit and, in some cases, lattice-based methods.
We introduce the a novel multiple circuits algorithm that makes use of a quantum lattice Boltzmann method (QLBM)
The problem is cast as a stream function--vorticity formulation of the 2D Navier-Stokes equations and verified and tested on a 2D lid-driven cavity flow.
arXiv Detail & Related papers (2024-01-20T15:32:01Z) - Towards large-scale quantum optimization solvers with few qubits [59.63282173947468]
We introduce a variational quantum solver for optimizations over $m=mathcalO(nk)$ binary variables using only $n$ qubits, with tunable $k>1$.
We analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature.
arXiv Detail & Related papers (2024-01-17T18:59:38Z) - Optimized Noise Suppression for Quantum Circuits [0.40964539027092917]
Crosstalk noise is a severe error source in, e.g., cross-resonance based superconducting quantum processors.
Intrepid programming algorithm extends previous work on optimized qubit routing by swap insertion.
We evaluate the proposed method by characterizing crosstalk noise for two chips with up to 127 qubits.
arXiv Detail & Related papers (2024-01-12T07:34:59Z) - Simulating photonic devices with noisy optical elements [0.615738282053772]
In the near-term, the performance of any quantum algorithm should be tested and simulated in the presence of noise.
We apply the recently proposed noisy gates approach to efficiently simulate noisy optical circuits.
We also evaluate the performance of a photonic variational quantum algorithm to solve the MAX-2-CUT problem.
arXiv Detail & Related papers (2023-11-17T16:06:20Z) - Error-mitigated fermionic classical shadows on noisy quantum devices [0.3775283002059579]
Classical shadow (CS) algorithms offer a solution by reducing the number of quantum state copies needed.
We propose an error-mitigated CS algorithm assuming gate-independent, time-stationary, and Markovian (GTM) noise.
Our algorithm efficiently estimates $k$-RDMs with $widetildemathcal O(knk)$ state copies and $widetildemathcal O(sqrtn)$ calibration measurements for GTM noise.
arXiv Detail & Related papers (2023-10-19T13:27:19Z) - The Quantum Approximate Optimization Algorithm performance with low
entanglement and high circuit depth [0.0]
Variational quantum algorithms constitute one of the most widespread methods for using current noisy quantum computers.
We investigate entanglement's role in these methods for solving optimization problems.
We conclude that entanglement plays a minor role in the MaxCut and Exact Cover 3 problems studied here.
arXiv Detail & Related papers (2022-07-07T16:21:36Z) - A single $T$-gate makes distribution learning hard [56.045224655472865]
This work provides an extensive characterization of the learnability of the output distributions of local quantum circuits.
We show that for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=omega(log(n))$ Clifford circuits is hard.
arXiv Detail & Related papers (2022-07-07T08:04:15Z) - Unimon qubit [42.83899285555746]
Superconducting qubits are one of the most promising candidates to implement quantum computers.
Here, we introduce and demonstrate a superconducting-qubit type, the unimon, which combines the desired properties of high non-linearity, full insensitivity to dc charge noise, insensitivity to flux noise, and a simple structure consisting only of a single Josephson junction in a resonator.
arXiv Detail & Related papers (2022-03-11T12:57:43Z) - A Hybrid Quantum-Classical Algorithm for Robust Fitting [47.42391857319388]
We propose a hybrid quantum-classical algorithm for robust fitting.
Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs.
We present results obtained using an actual quantum computer.
arXiv Detail & Related papers (2022-01-25T05:59:24Z)
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.