Schrödinger as a Quantum Programmer: Estimating Entanglement via Steering
- URL: http://arxiv.org/abs/2303.07911v4
- Date: Sat, 1 Jun 2024 13:34:59 GMT
- Title: Schrödinger as a Quantum Programmer: Estimating Entanglement via Steering
- Authors: Aby Philip, Soorya Rethinasamy, Vincent Russo, Mark M. Wilde,
- Abstract summary: We develop a quantum algorithm that tests for and quantifies the separability of a general bipartite state by using the quantum steering effect.
Our findings provide a meaningful connection between steering, entanglement, quantum algorithms, and quantum computational complexity theory.
- Score: 3.187381965457262
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantifying entanglement is an important task by which the resourcefulness of a quantum state can be measured. Here, we develop a quantum algorithm that tests for and quantifies the separability of a general bipartite state by using the quantum steering effect, the latter initially discovered by Schr\"odinger. Our separability test consists of a distributed quantum computation involving two parties: a computationally limited client, who prepares a purification of the state of interest, and a computationally unbounded server, who tries to steer the reduced systems to a probabilistic ensemble of pure product states. To design a practical algorithm, we replace the role of the server with a combination of parameterized unitary circuits and classical optimization techniques to perform the necessary computation. The result is a variational quantum steering algorithm (VQSA), a modified separability test that is implementable on quantum computers that are available today. We then simulate our VQSA on noisy quantum simulators and find favorable convergence properties on the examples tested. We also develop semidefinite programs, executable on classical computers, that benchmark the results obtained from our VQSA. Thus, our findings provide a meaningful connection between steering, entanglement, quantum algorithms, and quantum computational complexity theory. They also demonstrate the value of a parameterized mid-circuit measurement in a VQSA.
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 Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Parallel Quantum Hough Transform [0.0]
We propose a Parallel Quantum Hough transform (PQHT) algorithm that we execute on a quantum computer.
The modules were developed using IBM Quantum Composer and tested using the IBM QASM simulator.
The successful run results on Fraunhofer Q System One in Ehningen will be presented as a proof of concept for the PQHT algorithm.
arXiv Detail & Related papers (2023-11-15T14:42:51Z) - Quantum Imitation Learning [74.15588381240795]
We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL.
We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL)
Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
arXiv Detail & Related papers (2023-04-04T12:47:35Z) - Realization of quantum signal processing on a noisy quantum computer [0.4593579891394288]
We propose a strategy to run an entire QSP protocol on noisy quantum hardware by carefully reducing overhead costs at each step.
We test the protocol by running the algorithm on the Quantinuum H1-1 trapped-ion quantum computer powered by Honeywell.
Our results are the first step in the experimental realization of QSP-based quantum algorithms.
arXiv Detail & Related papers (2023-03-09T19:00:17Z) - Variational Quantum Eigensolver for Classification in Credit Sales Risk [0.5524804393257919]
We take into consideration a quantum circuit which is based on the Variational Quantum Eigensolver (VQE) and so-called SWAP-Test.
In the utilized data set, two classes may be observed -- cases with low and high credit risk.
The solution is compact and requires only logarithmically increasing number of qubits.
arXiv Detail & Related papers (2023-03-05T23:08:39Z) - Testing quantum computers with the protocol of quantum state matching [0.0]
The presence of noise in quantum computers hinders their effective operation.
We suggest the application of the so-called quantum state matching protocol for testing purposes.
For systematically varied inputs we find that the device with the smaller quantum volume performs better on our tests than the one with larger quantum volume.
arXiv Detail & Related papers (2022-10-18T08:25:34Z) - Machine learning applications for noisy intermediate-scale quantum
computers [0.0]
We develop and study three quantum machine learning applications suitable for NISQ computers.
These algorithms are variational in nature and use parameterised quantum circuits (PQCs) as the underlying quantum machine learning model.
We propose a variational algorithm in the area of approximate quantum cloning, where the data becomes quantum in nature.
arXiv Detail & Related papers (2022-05-19T09:26:57Z) - Benchmarking Small-Scale Quantum Devices on Computing Graph Edit
Distance [52.77024349608834]
Graph Edit Distance (GED) measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical.
In this paper we present a comparative study of two quantum approaches to computing GED.
arXiv Detail & Related papers (2021-11-19T12:35:26Z) - 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)
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.