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Test suite updates and package requirement adjustments.
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white-noise committed Sep 10, 2024
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3 changes: 2 additions & 1 deletion .gitignore
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Expand Up @@ -6,5 +6,6 @@ build
dist
OLD
NOTES-*
local_README.md

qsp/
qsp/
20 changes: 10 additions & 10 deletions README.md
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Expand Up @@ -14,7 +14,7 @@ In their most basic forms, QSP/QSVT give a recipe for a desired spectral transfo
- Run the corresponding sequence of gates as a quantum circuit, interleaving QSP signals and phases, followed by a measurement in a chosen basis.

> :warning: The theory of QSP is not only under active development, but comprises multiple subtly different conventions, each of which can use different terminology compared the barebones outline given here. These included conventions for how the *signal* is encoded, how the *phases* are applied, the basis to measure in, whether one desires to transform eigenvalues or singular values, whether the classical algorithm to find these phases is exact or iterative, and so on.
>
>
> Regardless, the basic scheme of QSP and QSVT is relatively fixed: given a specific circuit ansatz and a theory for the polynomial transformations achievable for that ansatz, generate those conditions and algorithms relating the *achieved function* and *circuit parameterization*. Understanding the bidirectional map between phases and polynomial transforms, as well as the efficiency of loading linear systems into quantum processes, constitutes most of the theory of these algorithms.
This package provides such conditions and algorithms, and automatically treats a few common conventions, with options to modify the code in basic ways to encompass others. These conventions are enumerated in the recent pedagogical work [A Grand Unification of Quantum Algorithms](https://arxiv.org/abs/2105.02859), and the QSP phase-finding algorithms we treat can be broken roughly into three types:
Expand Down Expand Up @@ -63,7 +63,7 @@ The guiding principle to take away from the discussion above is the following: t
This package includes various tools for plotting aspects of the computed QSP unitaries, many of which can be run from the command line. As an example, in the chart below the dashed line shows the target ideal polynomial QSP *response function* approximating a scaled version of $1/a$ over a sub-interval of $[-1,1]$. The dark line shows the real part of an actual response function, i.e., the matrix element $P_x(a)$, achieved by a QSP circuit with computed phases, while the blue line shows the imaginary part of the QSP response, with `n = 20` (the length of the QSP phase list less one).

<p align="center">
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_inversion.png" alt="QSP response function for the inverse function 1/a" width="75%"/>
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_inversion.png" alt="QSP response function for the inverse function 1/a" width="75%"/>
</p>

This was generated by running `pyqsp --plot --tolerance=0.01 --seqargs 3 invert`, which also spits out the the following verbose text:
Expand All @@ -89,7 +89,7 @@ pyqsp --plot-positive-only --plot --polyargs=19,10 --plot-real-only --polyname p
yields QSP phases for a degree `19` polynomial approximation, using the error function applied to `kappa * a`, where `kappa` is `10`. This also gives a plotted response function:

<p align="center">
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_amplitude_amplification.png" alt="Example QSP response function approximating the sign function" width="75%"/>
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_amplitude_amplification.png" alt="Example QSP response function approximating the sign function" width="75%"/>
</p>

A threshold function further generalizes on the sign function, e.g., as used in distinguishing eigenvalues or singular values through windowing. Running
Expand All @@ -99,7 +99,7 @@ pyqsp --plot-real-only --plot --polyargs=20,20 --polyname poly_thresh poly
yields QSP phases for a degree `20` polynomial approximation, using two error functions applied to `kappa * 20`, with a plotted response function:

<p align="center">
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_bandpass_response.png" alt="Example QSP response function approximating a threshold function" width="75%"/>
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_bandpass_response.png" alt="Example QSP response function approximating a threshold function" width="75%"/>
</p>

In addition to approximations to piecewise continuous functions, the smooth trigonometric functions sine and cosine functions also often appear, e.g., in Hamiltonian simulation. Running:
Expand All @@ -109,7 +109,7 @@ pyqsp --plot --func "np.cos(3*x)" --polydeg 6 --plot-qsp-model polyfunc
produces QSP phases for a degree `6` polynomial approximation of `cos(3*x)`, with the plotted response function:

<p align="center">
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/IMAGE-sample-qsp-response-for-cos-using-tf-order-6.png?raw=true" alt="Example QSP response function approximating a cosine function" width="75%"/>
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/IMAGE-sample-qsp-response-for-cos-using-tf-order-6.png?raw=true" alt="Example QSP response function approximating a cosine function" width="75%"/>
</p>

This last example also shows how an arbitrary function can be specified (using a `numpy` expression) as a string, and fit using an arbitrary order polynomial (may need to be even or odd, to match the function), using optimization via tensorflow, and a keras model. The example also shows an alternative style of plot, produced using the `--plot-qsp-model` flag.
Expand Down Expand Up @@ -178,23 +178,23 @@ Newly added methods related to the theory of symmetric quantum signal processing
For instance, the current file returns approximations to cosine, sine, and a step function, of which we reproduce the first and third plots below.

<p align="center">
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_cosine_approximation.png" alt="QSP response function approximating trigonometric cosine" width="75%"/>
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_cosine_approximation.png" alt="QSP response function approximating trigonometric cosine" width="75%"/>
</p>

As the quality of the approximation is quite high, causing the three intended plots to superpose, we include a logarithmic plot of the pairwise difference between the plotted values, indicating near-machine-precision limited performance.

Other benefits of the `symmetric_qsp` method appear when we approximate a scaled version of $1/x$. This is implemented in `pyqsp/sym_qsp_opt.py` for the choices `kappa = 5` (specifying the domain of valid approximation) and `epsilon = 0.01` (the uniform approximation error on the domain). We plot this approximation, indicating the region of validity from `[-1, -1/kappa]`-union-`[1/kappa, 1]` in gray, clearly showing that the approximation error bound and QSP phase error bounds are satisfied in the valid region.

<p align="center">
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_qsp_inverse_approx.png" alt="QSP response function approximating scaled 1/x" width="75%"/>
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_qsp_inverse_approx.png" alt="QSP response function approximating scaled 1/x" width="75%"/>
</p>

> :round_pushpin: Previously the computation of QSP phases to approximate 1/x was limited by two factors: (1) the instability of direct polynomial completion methods in the `laurent` approach, and (2) integer overflow errors resulting from computing coefficients of the analytic polynomial approximation in a naïve way. The plot above has been generated in a way which avoids both issues, and its degree can be easily pushed into the hundreds. The plot above uses `d = 155`.
Finally, we can move away from functions for which we have analytic descriptions of their Chebyshev expansions to general piecewise continuous functions for which we numerically compute Chebyshev interpolants. One such example is the step function, plotted analogously below.

<p align="center">
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_step_approximation.png" alt="QSP response function approximating a step function" width="75%"/>
<img src="https://github.com/ichuang/pyqsp/blob/master/docs/ex_step_approximation.png" alt="QSP response function approximating a step function" width="75%"/>
</p>

As in the case of trigonometric cosine and inverse, the step function's approximation is also excellent within the specified region, and far more forgiving in its generation than with the earlier `laurent` method.
Expand All @@ -209,7 +209,7 @@ pg = poly.PolySign()
pcoefs = pg.generate(
degree=161,
delta=25,
chebyshev_basis=True,
chebyshev_basis=True,
cheb_samples=250)
pcoefs = pcoefs.coef

Expand Down Expand Up @@ -300,7 +300,7 @@ optional arguments:
--measurement MEASUREMENT
measurement basis if using the polyfunc argument
--output-json output QSP phase angles in JSON format
--plot-positive-only when plotting only a-values (x-axis) from 0 to +1, instead of from -1 to +1
--plot-positive-only when plotting only a-values (x-axis) from 0 to +1, instead of from -1 to +1
--plot-tight-y when plotting scale y-axis tightly to real part of data
--plot-npts PLOT_NPTS
number of points to use in plotting
Expand Down
2 changes: 2 additions & 0 deletions base_requirements.txt
Original file line number Diff line number Diff line change
Expand Up @@ -3,3 +3,5 @@ matplotlib
numpy~=1.19.2
protobuf~=3.13.0
scipy
setuptools
pytest
247 changes: 247 additions & 0 deletions pyqsp/bin/Activate.ps1
Original file line number Diff line number Diff line change
@@ -0,0 +1,247 @@
<#
.Synopsis
Activate a Python virtual environment for the current PowerShell session.
.Description
Pushes the python executable for a virtual environment to the front of the
$Env:PATH environment variable and sets the prompt to signify that you are
in a Python virtual environment. Makes use of the command line switches as
well as the `pyvenv.cfg` file values present in the virtual environment.
.Parameter VenvDir
Path to the directory that contains the virtual environment to activate. The
default value for this is the parent of the directory that the Activate.ps1
script is located within.
.Parameter Prompt
The prompt prefix to display when this virtual environment is activated. By
default, this prompt is the name of the virtual environment folder (VenvDir)
surrounded by parentheses and followed by a single space (ie. '(.venv) ').
.Example
Activate.ps1
Activates the Python virtual environment that contains the Activate.ps1 script.
.Example
Activate.ps1 -Verbose
Activates the Python virtual environment that contains the Activate.ps1 script,
and shows extra information about the activation as it executes.
.Example
Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
Activates the Python virtual environment located in the specified location.
.Example
Activate.ps1 -Prompt "MyPython"
Activates the Python virtual environment that contains the Activate.ps1 script,
and prefixes the current prompt with the specified string (surrounded in
parentheses) while the virtual environment is active.
.Notes
On Windows, it may be required to enable this Activate.ps1 script by setting the
execution policy for the user. You can do this by issuing the following PowerShell
command:
PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
For more information on Execution Policies:
https://go.microsoft.com/fwlink/?LinkID=135170
#>
Param(
[Parameter(Mandatory = $false)]
[String]
$VenvDir,
[Parameter(Mandatory = $false)]
[String]
$Prompt
)

<# Function declarations --------------------------------------------------- #>

<#
.Synopsis
Remove all shell session elements added by the Activate script, including the
addition of the virtual environment's Python executable from the beginning of
the PATH variable.
.Parameter NonDestructive
If present, do not remove this function from the global namespace for the
session.
#>
function global:deactivate ([switch]$NonDestructive) {
# Revert to original values

# The prior prompt:
if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
}

# The prior PYTHONHOME:
if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
}

# The prior PATH:
if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
Remove-Item -Path Env:_OLD_VIRTUAL_PATH
}

# Just remove the VIRTUAL_ENV altogether:
if (Test-Path -Path Env:VIRTUAL_ENV) {
Remove-Item -Path env:VIRTUAL_ENV
}

# Just remove VIRTUAL_ENV_PROMPT altogether.
if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) {
Remove-Item -Path env:VIRTUAL_ENV_PROMPT
}

# Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
}

# Leave deactivate function in the global namespace if requested:
if (-not $NonDestructive) {
Remove-Item -Path function:deactivate
}
}

<#
.Description
Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
given folder, and returns them in a map.
For each line in the pyvenv.cfg file, if that line can be parsed into exactly
two strings separated by `=` (with any amount of whitespace surrounding the =)
then it is considered a `key = value` line. The left hand string is the key,
the right hand is the value.
If the value starts with a `'` or a `"` then the first and last character is
stripped from the value before being captured.
.Parameter ConfigDir
Path to the directory that contains the `pyvenv.cfg` file.
#>
function Get-PyVenvConfig(
[String]
$ConfigDir
) {
Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"

# Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
$pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue

# An empty map will be returned if no config file is found.
$pyvenvConfig = @{ }

if ($pyvenvConfigPath) {

Write-Verbose "File exists, parse `key = value` lines"
$pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath

$pyvenvConfigContent | ForEach-Object {
$keyval = $PSItem -split "\s*=\s*", 2
if ($keyval[0] -and $keyval[1]) {
$val = $keyval[1]

# Remove extraneous quotations around a string value.
if ("'""".Contains($val.Substring(0, 1))) {
$val = $val.Substring(1, $val.Length - 2)
}

$pyvenvConfig[$keyval[0]] = $val
Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
}
}
}
return $pyvenvConfig
}


<# Begin Activate script --------------------------------------------------- #>

# Determine the containing directory of this script
$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
$VenvExecDir = Get-Item -Path $VenvExecPath

Write-Verbose "Activation script is located in path: '$VenvExecPath'"
Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"

# Set values required in priority: CmdLine, ConfigFile, Default
# First, get the location of the virtual environment, it might not be
# VenvExecDir if specified on the command line.
if ($VenvDir) {
Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
}
else {
Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
$VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
Write-Verbose "VenvDir=$VenvDir"
}

# Next, read the `pyvenv.cfg` file to determine any required value such
# as `prompt`.
$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir

# Next, set the prompt from the command line, or the config file, or
# just use the name of the virtual environment folder.
if ($Prompt) {
Write-Verbose "Prompt specified as argument, using '$Prompt'"
}
else {
Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
$Prompt = $pyvenvCfg['prompt'];
}
else {
Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)"
Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
$Prompt = Split-Path -Path $venvDir -Leaf
}
}

Write-Verbose "Prompt = '$Prompt'"
Write-Verbose "VenvDir='$VenvDir'"

# Deactivate any currently active virtual environment, but leave the
# deactivate function in place.
deactivate -nondestructive

# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
# that there is an activated venv.
$env:VIRTUAL_ENV = $VenvDir

if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {

Write-Verbose "Setting prompt to '$Prompt'"

# Set the prompt to include the env name
# Make sure _OLD_VIRTUAL_PROMPT is global
function global:_OLD_VIRTUAL_PROMPT { "" }
Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt

function global:prompt {
Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
_OLD_VIRTUAL_PROMPT
}
$env:VIRTUAL_ENV_PROMPT = $Prompt
}

# Clear PYTHONHOME
if (Test-Path -Path Env:PYTHONHOME) {
Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
Remove-Item -Path Env:PYTHONHOME
}

# Add the venv to the PATH
Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"
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