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Qᴜᴀʟᴛʀᴀɴ is a Python library for expressing and analyzing Fault Tolerant Quantum algorithms.

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- Part of #905. - Removed _t_complexity_ calculation and moved to assert since this has a call_graph, the T count can be inferred.

This introduces a `CostKey` to count qubits. Part of #899.

Add a new `CostKey` for multiplying success probabilities. Part of #899

This is a proof of principle, re: #957 There's really no problem working with 5,000+-bit bloqs (and their decomposition) if they're written with a bit of care. It's an incredibly...

contributors welcome

Calculate complementary polynomial using Eq. 60 of the [paper](https://arxiv.org/abs/2308.01501)

#956 removed titles from single qubit gates but this means on each now renders like: Is the solution here to just say goodbye to $H^{\otimes N}$

Fixes https://github.com/quantumlib/Qualtran/issues/108 Partially addresses https://github.com/quantumlib/Qualtran/issues/368 Fixes https://github.com/quantumlib/Qualtran/issues/78 We still need an adjoint bloq for QROMs to make the complexity for clean ancilla version match that of the paper, and I...

Does it make sense for these to have symbolic register sizes?

I've added tutorials for Cirq and Qualtran. These tutorials were developed for the course "Practical Quantum Computing" by me and Professor Alexandru Paler, and they've received positive feedback from our...