High-threshold quantum computing by fusing one-dimensional cluster
states
- URL: http://arxiv.org/abs/2212.06775v2
- Date: Mon, 17 Jul 2023 10:00:09 GMT
- Title: High-threshold quantum computing by fusing one-dimensional cluster
states
- Authors: Stefano Paesani and Benjamin J. Brown
- Abstract summary: We propose a measurement-based model for fault-tolerant quantum computation that can be realised with one-dimensional cluster states and fusion measurements only.
Our simulations demonstrate high thresholds compared with other measurement-based models realized with basic entangled resources and two-qubit fusion measurements.
- Score: 3.8073142980733
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a measurement-based model for fault-tolerant quantum computation
that can be realised with one-dimensional cluster states and fusion
measurements only; basic resources that are readily available with scalable
photonic hardware. Our simulations demonstrate high thresholds compared with
other measurement-based models realized with basic entangled resources and
two-qubit fusion measurements. Its high tolerance to noise indicates that our
practical construction offers a promising route to scalable quantum computing
with quantum emitters and linear-optical elements.
Related papers
- Tailoring fusion-based photonic quantum computing schemes to quantum emitters [0.0]
Fusion-based quantum computation is a promising quantum computing model where small-sized photonic resource states are simultaneously entangled and measured by fusion gates.
Here, we propose fusion-based architectures tailored to the capabilities and noise models in quantum emitters.
We show that high tolerance to dominant physical error mechanisms can be achieved, with fault-tolerance thresholds of 8% for photon loss, 4% for photon distinguishability between emitters, and spin noise thresholds well above memory-induced errors.
arXiv Detail & Related papers (2024-10-09T11:31:49Z) - 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) - Multimodal deep representation learning for quantum cross-platform
verification [60.01590250213637]
Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms.
We introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities.
We devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation.
arXiv Detail & Related papers (2023-11-07T04:35:03Z) - Higher-dimensional symmetric informationally complete measurement via
programmable photonic integrated optics [7.0015653334875205]
We demonstrate an integrated quantum photonic platform to realize such a measurement on three-level quantum systems.
The device operates at the high fidelities necessary for a genuine many-outcome quantum measurement.
It is programmable and can readily implement other quantum measurements at similarly high quality.
arXiv Detail & Related papers (2023-10-13T03:28:06Z) - Flexible entangled state generation in linear optics [0.0]
Fault-tolerant quantum computation can be achieved by creating constant-sized, entangled resource states.
We show that it is possible to boost the success probability of photonic GHZ state analysers with single photon auxiliary states.
arXiv Detail & Related papers (2023-10-10T17:58:21Z) - Anticipative measurements in hybrid quantum-classical computation [68.8204255655161]
We present an approach where the quantum computation is supplemented by a classical result.
Taking advantage of its anticipation also leads to a new type of quantum measurements, which we call anticipative.
In an anticipative quantum measurement the combination of the results from classical and quantum computations happens only in the end.
arXiv Detail & Related papers (2022-09-12T15:47:44Z) - Scalable Simulation of Quantum Measurement Process with Quantum
Computers [13.14263204660076]
We propose qubit models to emulate the quantum measurement process.
One model is motivated by single-photon detection and the other by spin measurement.
We generate Schr"odinger cat-like state, and their corresponding quantum circuits are shown explicitly.
arXiv Detail & Related papers (2022-06-28T14:21:43Z) - Generalization Metrics for Practical Quantum Advantage in Generative
Models [68.8204255655161]
Generative modeling is a widely accepted natural use case for quantum computers.
We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance.
Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
arXiv Detail & Related papers (2022-01-21T16:35:35Z) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z) - Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum
dynamics [0.0]
We present a scalable algorithm based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum systems.
Specifically, we show that our algorithm is able to reconstruct the Hamiltonian of an arbitrary size quasi-1D bosonic system.
arXiv Detail & Related papers (2021-03-01T19:00:15Z) - Direct estimation of quantum coherence by collective measurements [54.97898890263183]
We introduce a collective measurement scheme for estimating the amount of coherence in quantum states.
Our scheme outperforms other estimation methods based on tomography or adaptive measurements.
We show that our method is accessible with today's technology by implementing it experimentally with photons.
arXiv Detail & Related papers (2020-01-06T03:50:42Z)
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.