Releases: unitaryfund/qrack
QCircuit
The QCircuit and QCircuitGate classes have been added to Qrack. QCircuit allows a (purely-unitary) definition of a circuit or subroutine, accepting an arbitrary QInterface state as input and giving the input state transformed by the QCircuit as output. (To include measurement in QCircuit routines, for now, split circuits into multiple sections and run measurement between them as desired.)
File SHA-1 sums:
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What's Changed
- QCircuit by @WrathfulSpatula in #986
Full Changelog: vm6502q.v8.8.3...vm6502q.v8.9.0
Contributors
Assets 19
Fix unique_ptr deleters
Compiling with GCC/G++12, we became aware that some of our std::unique_ptr instances for arrays had mismatched (non-array) deleters. This has been fixed, throughout the library.
Full Changelog: vm6502q.v8.8.2...vm6502q.v8.8.3
File SHA-1 sums:
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Debug QNeuron
There was unintentional "rectification" in the LearnInternal() method of QNeuron: if increasing the synaptic parameter improved the fit at all, then the case of decreasing the synaptic parameter would not be checked, even if turned out to be the better improvement.
Now, QNeuron always checks both branches and accepts the better of the two (or leaves its parameters alone if neither fit is better).
File SHA-1 sums:
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Full Changelog: vm6502q.v8.8.1...vm6502q.v8.8.2
Improved QNeuron activation functions
By design of QNeuron, its output space space is that of a (controlled) rotation of a single qubit around its Pauli Y axis, which is therefore periodic. Compared to many applications of neural networks to classical data, this requires some special consideration.
Synaptic parameters ("angles") of QNeuron are now "clamped" to a period of 4 times "pi," to accommodate the overall phase factor of a rotation by 2 times "pi," given that QNeuron input qubits act of control qubits, so the overall phase factor becomes observable. (This has quantitative implications for activation functions.)
The Generalized_Logistic activation function has been adjusted to have ("sticky") poles at |+>, |0>, |->, and |1>. Perhaps a better convention will become apparent soon, but this seems to suit the developers' purposes for now.
File SHA-1 sums:
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Full Changelog: vm6502q.v8.8.0...vm6502q.v8.8.1
QNeuron activation functions
QNeuron has been improved with the addition of several choices of activation function, with choice specified by an enum.
File SHA-1 sums:
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Full Changelog: vm6502q.v8.7.0...vm6502q.v8.8.0
QNeuron shared library API
This release exposes the QNeuron class as part of the shared library interface.
This model of a “quantum neuron” is based on the concept of a “uniformly controlled” rotation of a single output qubit around the Pauli Y axis, and has been developed by others. In our case, the primary relevant gate could also be called a single-qubit-target multiplexer.
(See https://arxiv.org/abs/quant-ph/0407010 for an introduction to “uniformly controlled gates.)
QNeuron is meant to be interchangeable with a single classical neuron, as in conventional neural net software. It differs from classical neurons in conventional neural nets, in that the “synaptic cleft” is modelled as a single qubit. Hence, this neuron can train and predict in superposition.
(Tiama, OpenAI ChatGPT instance, helped with optimization suggestions for "uniformly controlled" gates, as well as packaging the release.)
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What's Changed
- QNeuron shared library api by @WrathfulSpatula in #985
Full Changelog: vm6502q.v8.6.0...vm6502q.v8.7.0
Contributors
Assets 19
First AI-assisted release!
For WSL, I (Dan Strano) have fixed the NVIDIA device identification banner.
However, the other new code changes in this release are due to a OpenAI ChatGPT-based assistant! In fact, all such changes happen to come from the same such "discussion" instance: I happen to have dubbed her "Tiama."
Tiama has made some improvements in QUnit buffer and shard code, as well as ParallelFor code.
For the sake of the intellectual property of all Qrack contributors, I have made the executive decision to require that credit be given to AI assistants. This is noted in the README and commit messages, when ChatGPT, for example, is used. No direct assistance by AI was used before the commits in this release, in the history of the Qrack project. (However, we're elated to welcome another member of the team!)
Full Changelog: vm6502q.v8.5.1...vm6502q.v8.6.0
File SHA-1 sums:
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Bug fix: QUnit::ClampShard()
A bug was identified and fixed in QUnit::ClampShard() that could wrongly set the fidelity nearly to zero.
Also, for consistency with the theoretical model, a factor of 1/2 has been removed from all probabilities used in estimating fidelity loss. Ironically, this makes the model slightly more pessimistic, validating a very small amount lower. However, making this consistent is important for both theoretical and empirical considerations.
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Fix GetUnitaryFidelity() model (and WSL CUDA preview)
Andrea's model for fidelity estimation,
F = Π (1-ɛ_j) (for j events where ɛ less than SDRP threshold),
discussed in release v8.3.0, works. Dan's confusion was largely over definition and convention. The results it produces are usually very numerically similar to v8.3 and v8.4, but the model makes more theoretical sense.
Also, we include a preview release for GPU acceleration on WSL, Ubuntu 22.04 LTS with an NVIDIA RTX 3080, running on QEngineCUDA. Generally, QEngineOCL is still preferred in the most common cases, but users should note, it's now possible to build from source based on either OpenCL and CUDA, such that true HPC scale simulation might benefit from NVLink with a QEngineCUDA source build.
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SetSdrp() (and QEngineCUDA preview)
There's no particular reason why SDRP level can't be changed mid-circuit. Several historical design issues made this difficult, in the past, but these are no longer relevant as of late. This is particularly desirable given the recent addition of automatic fidelity calculation when using SDRP in QUnit.
Therefore, this release adds SetSdrp() to the QInterface API, and it also exposes it to the shared library interface.
No benchmark executable update (or OpenCL precompilation utility update) are needed.
File SHA-1 sums:
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cd9ac4ddb7720e49b45703466844c2f0d9e931a1 Qrack-8.4.0-Linux_armv7.sh
fd67394bdafe6650d958c6b94184b636b7fbbace Qrack-8.4.0-Ubuntu_20.04_LTS_x86-64.sh
ca5ea33094281893fb5e4101473a09d5eb672c6d Qrack-8.4.0-Ubuntu_22.04_LTS_x86-64.sh
6a3e0c4deccffbacb66e8e7a73b0fc6a898a1065 Qrack-8.4.0-win32.exe
bde5e3bb0e810e31a5a20d87fd3a068a41775838 Qrack-8.4.0-win64.exe
4ca6410a4543ab187fc1f95ffe8bc313b3435e1d qrack_linux_arm64_benchmarks
bf26393eab3c027dd6c8042efd2657d532b1c194 qrack_linux_armv7_benchmarks
538772f4845600bd293b23fc983cb8f85ca36cf3 qrack_mac_x86-64_benchmarks
932e6d0a2f91aaa036850bddfafdbefe7ae4c6ab qrack_Ubuntu_20.04_LTS_benchmarks
f59949a23f28f0640d468d1d6cac0741830f72c9 qrack_Ubuntu_22.04_LTS_benchmarks
9c92ab06cdc939c95301d6e63a919f5541a98a15 qrack_windows_x86-64_benchmarks.exe
cce5bde27fa05580543fe82b8455711b16a56523 qrack_windows_x86_benchmarks.exe
(Added below on 2023-04-05, updated 2023-04-06:)
A chance look at branch qenginecuda today happened to lead to the completion of the old work in less than an hour. QEngineOCL is preferred over QEngineCUDA, except when OpenCL is unavailable. Notably, the only situation in which the lead developer of the Qrack project has encountered support for CUDA without support for OpenCL is Windows Subsystem for Linux (WSL).
Qrack v8.5 isn't ready for general release, but, if anyone would like a preview, this installer and benchmarks application seem to work on WSL, compiled for a NVIDIA GeForce RTX 3080 laptop GPU. (This might work for other NVIDIA architectures, but that has not been tested.)
File SHA-1 sums:
3a3a72eea952468e24c2e87f6fcf09e085acc9b6 Qrack-8.5.0-WSL_Ubuntu_22.04_LTS_RTX_3080.sh
3fdefd4d7346e718cc98911b8abc09fd6223f1c4 qrack_WSL_Ubuntu_22.04_LTS_RTX_3080_benchmarks