Greedy Gradient-free Adaptive Variational Quantum Algorithms on a Noisy
Intermediate Scale Quantum Computer
- URL: http://arxiv.org/abs/2306.17159v5
- Date: Mon, 11 Sep 2023 15:21:30 GMT
- Title: Greedy Gradient-free Adaptive Variational Quantum Algorithms on a Noisy
Intermediate Scale Quantum Computer
- Authors: C\'esar Feniou, Baptiste Claudon, Muhammad Hassan, Axel Courtat,
Olivier Adjoua, Yvon Maday, Jean-Philip Piquemal
- Abstract summary: Hybrid quantum-classical adaptive Variational Quantum Eigensolvers (VQE) hold the potential to outperform classical computing for quantum many-body systems.
New techniques to execute adaptive algorithms on a 25-qubit error-mitigated QPU to a GPU-accelerated HPC simulator are presented.
- Score: 0.632231271751641
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Hybrid quantum-classical adaptive Variational Quantum Eigensolvers (VQE)
already hold the potential to outperform classical computing for simulating
quantum many-body systems. However, their practical implementation on current
quantum processing units (QPUs) is very challenging due to the noisy evaluation
of a polynomially scaling number of observables, undertaken for operator
selection and optimisation of a high-dimensional cost function. To overcome
this, we propose new techniques to execute adaptive algorithms on a 25-qubit
error-mitigated QPU coupled to a GPU-accelerated HPC simulator. Targeting
physics applications, we compute the ground state of a 25-body Ising model
using the newly introduced Greedy Gradient-free Adaptive VQE (CGA-VQE)
requiring only five circuit measurements per iteration, regardless of the
number of qubits and size of the operator pool. Towards chemistry, we combine
the GGA-VQE and Overlap-ADAPT-VQE algorithms to approximate a molecular system
ground state. We show that the QPU successfully executes the algorithms and
yields the correct choice of parametrised unitary operators. While the QPU
evaluation of the resulting ansatz wave-function is polluted by hardware noise,
a single final evaluation of the sought-after observables on a classical
GPU-accelerated/noiseless simulator allows the recovery of the correct
approximation of the ground state, thus highlighting the need for hybrid
quantum-classical observable measurement.
Related papers
- Efficient charge-preserving excited state preparation with variational quantum algorithms [33.03471460050495]
We introduce a charge-preserving VQD (CPVQD) algorithm, designed to incorporate symmetry and the corresponding conserved charge into the VQD framework.
Results show applications in high-energy physics, nuclear physics, and quantum chemistry.
arXiv Detail & Related papers (2024-10-18T10:30:14Z) - Benchmarking Variational Quantum Eigensolvers for Entanglement Detection in Many-Body Hamiltonian Ground States [37.69303106863453]
Variational quantum algorithms (VQAs) have emerged in recent years as a promise to obtain quantum advantage.
We use a specific class of VQA named variational quantum eigensolvers (VQEs) to benchmark them at entanglement witnessing and entangled ground state detection.
Quantum circuits whose structure is inspired by the Hamiltonian interactions presented better results on cost function estimation than problem-agnostic circuits.
arXiv Detail & Related papers (2024-07-05T12:06:40Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Variational Quantum Eigensolvers with Quantum Gaussian Filters for solving ground-state problems in quantum many-body systems [2.5425769156210896]
We present a novel quantum algorithm for approximating the ground-state in quantum many-body systems.
Our approach integrates Variational Quantum Eigensolvers (VQE) with Quantum Gaussian Filters (QGF)
Our method shows improved convergence speed and accuracy, particularly under noisy conditions.
arXiv Detail & Related papers (2024-01-24T14:01:52Z) - Real-time error mitigation for variational optimization on quantum
hardware [45.935798913942904]
We define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs.
Our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function.
arXiv Detail & Related papers (2023-11-09T19:00:01Z) - Error Mitigation-Aided Optimization of Parameterized Quantum Circuits:
Convergence Analysis [42.275148861039895]
Variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy processors.
gate noise due to imperfections and decoherence affects the gradient estimates by introducing a bias.
Quantum error mitigation (QEM) techniques can reduce the estimation bias without requiring any increase in the number of qubits.
QEM can reduce the number of required iterations, but only as long as the quantum noise level is sufficiently small.
arXiv Detail & Related papers (2022-09-23T10:48:04Z) - The Variational Quantum Eigensolver: a review of methods and best
practices [3.628860803653535]
The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground state energy of a Hamiltonian.
This review aims to provide an overview of the progress that has been made on the different parts of the algorithm.
arXiv Detail & Related papers (2021-11-09T14:40:18Z) - 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) - Layer VQE: A Variational Approach for Combinatorial Optimization on
Noisy Quantum Computers [5.644434841659249]
We propose an iterative Layer VQE (L-VQE) approach, inspired by the Variational Quantum Eigensolver (VQE)
We show that L-VQE is more robust to finite sampling errors and has a higher chance of finding the solution as compared with standard VQE approaches.
Our simulation results show that L-VQE performs well under realistic hardware noise.
arXiv Detail & Related papers (2021-02-10T16:53:22Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - Classical Optimizers for Noisy Intermediate-Scale Quantum Devices [1.43494686131174]
We present a collection of tunings tuned for usage on Noisy Intermediate-Scale Quantum (NISQ) devices.
We analyze the efficiency and effectiveness of different minimizes in a VQE case study.
While most results to date concentrated on tuning the quantum VQE circuit, we show that, in the presence of quantum noise, the classical minimizer step needs to be carefully chosen to obtain correct results.
arXiv Detail & Related papers (2020-04-06T21:31:22Z)
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.