A Variational Approach to the Quantum Separability Problem
- URL: http://arxiv.org/abs/2209.01430v2
- Date: Thu, 17 Nov 2022 00:41:26 GMT
- Title: A Variational Approach to the Quantum Separability Problem
- Authors: Mirko Consiglio, Tony John George Apollaro, Marcin Wie\'sniak
- Abstract summary: We present the variational separability verifier (VSV) that determines the closest separable state (CSS) of an arbitrary quantum state with respect to the Hilbert-Schmidt distance (HSD)
Results indicate that current noisy intermediate-scale quantum (NISQ) devices may be useful in addressing the $NP$-hard full separability problem using the VSV.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present the variational separability verifier (VSV), which is a novel
variational quantum algorithm (VQA) that determines the closest separable state
(CSS) of an arbitrary quantum state with respect to the Hilbert-Schmidt
distance (HSD). We first assess the performance of the VSV by investigating the
convergence of the optimization procedure for Greenberger-Horne-Zeilinger (GHZ)
states of up to seven qubits, using both statevector and shot-based
simulations. We also numerically determine the (CSS) of maximally entangled
multipartite $X$-states ($X$-MEMS), and subsequently use the results of the
algorithm to surmise the analytical form of the aforementioned (CSS). Our
results indicate that current noisy intermediate-scale quantum (NISQ) devices
may be useful in addressing the $NP$-hard full separability problem using the
VSV, due to the shallow quantum circuit imposed by employing the destructive
SWAP test to evaluate the (HSD). The (VSV) may also possibly lead to the
characterization of multipartite quantum states, once the algorithm is adapted
and improved to obtain the closest $k$-separable state ($k$-CSS) of a
multipartite entangled state.
Related papers
- Robust Experimental Signatures of Phase Transitions in the Variational Quantum Eigensolver [0.0]
Variational Quantum Eigensolver (VQE) is widely considered to be a promising candidate for a quantum-classical algorithm.
In this work, we use several IBM devices to explore a finite-size spin model with multiple phase-like' regions.
arXiv Detail & Related papers (2024-02-29T08:34:11Z) - Multi-sequence alignment using the Quantum Approximate Optimization
Algorithm [0.0]
We present a Hamiltonian formulation and implementation of the Multiple Sequence Alignment problem with the variational Quantum Approximate Optimization Algorithm (QAOA)
We consider a small instance of our QAOA-MSA algorithm in both a quantum simulator and its performance on an actual quantum computer.
While the ideal solution to the instance of MSA investigated is shown to be the most probable state sampled for a shallow p5 quantum circuit, the level of noise in current devices is still a formidable challenge.
arXiv Detail & Related papers (2023-08-23T12:46:24Z) - 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) - Optimal quantum control via genetic algorithms for quantum state
engineering in driven-resonator mediated networks [68.8204255655161]
We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms.
We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator.
We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
arXiv Detail & Related papers (2022-06-29T14:34:00Z) - Variational determination of arbitrarily many eigenpairs in one quantum
circuit [8.118991737495524]
A variational quantum eigensolver (VQE) was first introduced for computing ground states.
We propose a new algorithm to determine many low energy eigenstates simultaneously.
Our algorithm reduces significantly the complexity of circuits and the readout errors.
arXiv Detail & Related papers (2022-06-22T13:01:37Z) - Quantum Davidson Algorithm for Excited States [42.666709382892265]
We introduce the quantum Krylov subspace (QKS) method to address both ground and excited states.
By using the residues of eigenstates to expand the Krylov subspace, we formulate a compact subspace that aligns closely with the exact solutions.
Using quantum simulators, we employ the novel QDavidson algorithm to delve into the excited state properties of various systems.
arXiv Detail & Related papers (2022-04-22T15:03:03Z) - Efficient Bipartite Entanglement Detection Scheme with a Quantum
Adversarial Solver [89.80359585967642]
Proposal reformulates the bipartite entanglement detection as a two-player zero-sum game completed by parameterized quantum circuits.
We experimentally implement our protocol on a linear optical network and exhibit its effectiveness to accomplish the bipartite entanglement detection for 5-qubit quantum pure states and 2-qubit quantum mixed states.
arXiv Detail & Related papers (2022-03-15T09:46:45Z) - Robust preparation of Wigner-negative states with optimized
SNAP-displacement sequences [41.42601188771239]
We create non-classical states of light in three-dimensional microwave cavities.
These states are useful for quantum computation.
We show that this way of creating non-classical states is robust to fluctuations of the system parameters.
arXiv Detail & Related papers (2021-11-15T18:20:38Z) - 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) - Calculating transition amplitudes by variational quantum deflation [5.306344552127684]
Variational quantum eigensolver (VQE) is an appealing candidate for the application of near-term quantum computers.
No method to evaluate transition amplitudes between the eigenstates found by the VQD without using any costly Hadamard-test-like circuit.
Our method relies only on the ability to estimate between two states, so it does not restrict the validity to the VQD eigenstates.
arXiv Detail & Related papers (2020-02-26T19:00:00Z)
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.