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Distributed Quantum Computing over Classical Channels: Wire Cutting, Gate Cutting, and Quasiprobability Decomposition

This article explores distributed quantum computing over classical communication channels using circuit knitting techniques like wire cutting and gate cutting with quasiprobability decomposition. It includes theoretical foundations, mathematical derivations of sampling overheads, and a functional Python simulation demonstrating expectation value reconstruction.

Quavis - Quantum Vision 13 min read Advanced
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Polar Angle (θ): 0.50π
Azimuth Angle (φ): 0.25π
Qubit Mathematical State
|ψ⟩ = 0.707|0⟩ + (0.500 + 0.500i)|1⟩
P(|0⟩) = 50.0%
P(|1⟩) = 50.0%

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Key pillars of current quantum computing experimentation and publications.

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VQE, QAOA, and Hamiltonian simulations targeting molecular ground states and chemistry.

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Coherence properties, error threshold analysis, and topological qubit architecture research.

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PennyLane, Qiskit, and JAX-quantum comparisons for hybrid classical-quantum models.

Error Correction

Surface codes, syndrome parity checks, and logical physical mapping algorithms.

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#Quantum Computing #Circuit Knitting #Quasiprobability Decomposition
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Distributed Quantum Computing over Classical Channels: Wire Cutting, Gate Cutting, and Quasiprobability Decomposition

This article explores distributed quantum computing over classical communication channels using circuit knitting techniques like wire cutting and gate cutting with quasiprobability decomposition. It includes theoretical foundations, mathematical derivations of sampling overheads, and a functional Python simulation demonstrating expectation value reconstruction.

Quavis - Quantum Vision 13 min read 2026-08-19 11:34
4 reads
#Neutral-Atom Quantum Computing #Rydberg Blockade #Quantum Error Correction
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Architecting Fault Tolerance on Neutral-Atom Quantum Computers: Coherent Transport, Rydberg Entanglement, and Non-Local Surface Codes

This article provides an in-depth architectural and physical breakdown of fault-tolerant quantum computing using neutral-atom optical tweezer arrays, covering Rydberg blockade entangling dynamics, coherent atomic transport, and non-local surface code syndrome extraction. It includes a complete, functional Python numerical simulation of a two-atom Levine-Pichler CZ gate.

Quavis - Quantum Vision 14 min read 2026-08-18 11:41
7 reads
#Quantum Computing #Neutral Atoms #Quantum Error Correction
Advanced

Architecting Fault Tolerance: A Deep Dive into Reconfigurable Neutral-Atom Quantum Computers

This article provides a comprehensive physical and architectural analysis of reconfigurable neutral-atom quantum computing, detailing Rydberg blockade mechanics, mid-circuit syndrome extraction, 3D topological codes, and numerical simulation of optimal two-qubit entangling gates.

Quavis - Quantum Vision 13 min read 2026-08-17 11:41
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Active QPUs

QuaVis-Heron-1 156 Qubits | Heavy-Hex
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LVL 5 PRIORITY 2026-08-24 08:15

QCrypt 2026: 16th International Conference on Quantum Cryptography

The premier international scientific conference dedicated to quantum cryptography, presenting leading experimental and theoretical breakthroughs in quantum secure communications, post-quantum protocols, and quantum key distribution.

📍 Learning Crossroads (CRX), University of Ottawa, Ottawa, Canada

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LVL 5 PRIORITY 2026-08-24 08:15

QCrypt 2026 - 16th International Conference on Quantum Cryptography

The premier global conference on quantum cryptography, presenting theoretical breakthroughs and experimental advancements in quantum key distribution, device-independent security, and post-quantum protocols.

📍 Learning Crossroads (CRX), University of Ottawa, Ottawa, Canada

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LVL 4 PRIORITY 2026-08-24 08:30

QCrypt 2026 (16th International Conference on Quantum Cryptography)

The leading annual scientific conference presenting top-tier research advancements in quantum cryptography, quantum key distribution, and quantum-safe communications.

📍 University of Ottawa, Ottawa, Ontario, Canada

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Quantum Machine Learning Paradigms

Quavis - Quantum Vision tracks articles covering multiple operational layers of quantum computing algorithms to speed up generative models, classify quantum states, and optimize deep neural structures.

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Variational Quantum Eigensolver (VQE)

VQE leverages parameterized quantum circuits combined with classical optimization loops to find ground state energies, forming the core of molecular simulations and physical chemistry models.

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Quantum Neural Networks (QNN)

By utilizing quantum entanglement and superposition, QNNs represent data spaces of exponentially high dimensions. Ideal for complex classification tasks and high-energy physics models.

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Quantum Kernel Estimation

Map classical data into quantum Hilbert space via feature maps. This allows SVMs and classical linear classifiers to process previously non-linear relationships with quantum precision.