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# Quantum NLP with the lambeq–PennyLane integration | PennyLane Blog
The Quantinuum team gives an introduction to quantum natural language processing (QNLP) and showcases the recently published integ…
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# Abstract
Use machine learning to place gates that solve a simple problem (like a sum, substract, etc.), so the final quantum circuit is optimized and done by the computer insted of a human.
# Me…
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This is an issue to track progress on the Qualtran to QIR Compiler.
The primary motivation for converting Qualtran circuits to QIR is to:
Enhance Interoperability: QIR-compatible Qualtran circuit…
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# Description
The "length" of a quantum circuit is the primary factor when determining the magnitude of the errors in the resulting output distribution; quantum circuits with greater depth have decre…
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Most of the circuits we build using this library will be large nested circuits, where we have a few composite outer level operations like loading data from QROM, selectively applying a linear combinat…
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### What is the expected behavior?
Ignis APIs are currently too tied to physical qubits. For example to generate quantum_volume circuits, you have to say which physical qubits you want it for. …
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As discussed today, it may be a good idea to incorporate some specialized/customized minimizers for variational circuits.
Here I leave some references where they are introduced or benchmarked.
[…
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# Abstract
We want to use the full capabilities of both Qiskit and pyTorch to develop hybrid quantum-classical machine learning algorithms (e.g. meta-learning for quantum circuits with classical neur…
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Description
- This project is intended to explore couple papers in literature of Quantum Transformer models [self attention model: https://arxiv.org/abs/2205.05625 , Quantum vision transformers : htt…
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Qiskit now support building parametrized quantum circuits, see https://qiskit.org/documentation/terra/custom_gates.html#parameterized-gates.
The advantage of parametrized circuits is that Qiskit on…