Cavity-QED tools for MBQC with optical binomial-codes
- URL: http://arxiv.org/abs/2601.15019v1
- Date: Wed, 21 Jan 2026 14:17:17 GMT
- Title: Cavity-QED tools for MBQC with optical binomial-codes
- Authors: G. P. Teja, Radim Filip,
- Abstract summary: Measurement-based quantum computation (MBQC) offers a promising paradigm for photonic quantum computing.<n>Our work proposes the first steps for existing optical atom-cavity architectures to lay the groundwork for their use in quantum computation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Measurement-based quantum computation (MBQC) offers a promising paradigm for photonic quantum computing, but its implementation requires the generation of specific non-Gaussian resource states. While continuous-variable encodings such as the highly complex (GKP) states have been widely studied, the much simpler binomial codes offer an experimentally accessible alternative, though they demand a distinct set of operational tools. Here, we present a toolkit for MBQC using optical binomial codes, detailing a cavity-QED protocol for conditional generation of cluster states and the implementation of Pauli measurements. Our work proposes the first steps for existing optical atom-cavity architectures to lay the groundwork for their use in quantum computation.
Related papers
- Qute: Towards Quantum-Native Database [40.35292966418181]
This paper envisions a quantum database (Qute) that treats quantum computation as a first-class execution option.<n>By deploying Qute on a real quantum processor, we show that it outperforms a classical baseline at scale.
arXiv Detail & Related papers (2026-02-16T12:39:46Z) - Quantum Executor: A Unified Interface for Quantum Computing [46.36953285198747]
Quantum Executor is a backend-agnostic execution engine designed to orchestrate quantum experiments across heterogeneous platforms.<n>Key features include support for asynchronous and distributed execution, customizable execution strategies and a unified API for managing quantum experiments.
arXiv Detail & Related papers (2025-07-10T09:55:32Z) - Quantum QSAR for drug discovery [41.94295877935867]
Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery.<n>This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs)<n>By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models.
arXiv Detail & Related papers (2025-05-06T17:58:33Z) - Empirical Power of Quantum Encoding Methods for Binary Classification [0.2118773996967412]
We will focus on encoding schemes and their effects on various machine learning metrics.<n>Specifically, we focus on real-world data encoding to demonstrate differences between quantum encoding strategies for several real-world datasets.
arXiv Detail & Related papers (2024-08-23T14:34:57Z) - Single-Round Proofs of Quantumness from Knowledge Assumptions [41.94295877935867]
A proof of quantumness is an efficiently verifiable interactive test that an efficient quantum computer can pass.
Existing single-round protocols require large quantum circuits, whereas multi-round ones use smaller circuits but require experimentally challenging mid-circuit measurements.
We construct efficient single-round proofs of quantumness based on existing knowledge assumptions.
arXiv Detail & Related papers (2024-05-24T17:33:10Z) - Measurement-based quantum machine learning [0.0]
We propose a universal quantum neural network assembled from measurement-based quantum computing neurons.<n>We numerically demonstrate that MuTA can learn a universal set of gates in the presence of noise.<n>We incorporate hardware constraints imposed by photonic Gottesman-Kitaev-Preskill qubits.
arXiv Detail & Related papers (2024-05-14T05:17:01Z) - Realizations of Measurement Based Quantum Computing [0.0]
Measurement Based Quantum Computation model achieves universal quantum computation by employing projective single qubit measurements with classical feedforward on a highly entangled multipartite cluster state.
This review focuses on three such efforts, each utilizing a different quantum computing technology viz., superconducting qubits, trapped ion qubits and squeezed photon states.
arXiv Detail & Related papers (2021-12-22T01:04:11Z) - Probably approximately correct quantum source coding [0.0]
Holevo's and Nayak's bounds give an estimate of the amount of classical information that can be stored in a quantum state.
We show two novel applications in quantum learning theory and delegated quantum computation with a purely classical client.
arXiv Detail & Related papers (2021-12-13T17:57:30Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z) - Polylog-overhead highly fault-tolerant measurement-based quantum
computation: all-Gaussian implementation with Gottesman-Kitaev-Preskill code [3.6748639131154315]
We develop a fault-tolerant quantum computation protocol for measurement-based quantum computation (MBQC)
Our protocol achieves the threshold $7.8$ dB in terms of the squeezing level of the best existing protocol for fault-tolerant quantum computation.
Our results open a new way towards realization of a large class of quantum speedups.
arXiv Detail & Related papers (2020-06-09T17:30:41Z)
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.