microsoft
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QuantumLibraries
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Simplify gradient estimation and classifier structure generation (#200) 

* Clarify the restriction on the number of bits for IntAsBoolArray (#171)

* Clarify the restriction on the number of bits for IntAsBoolArray

This should fix #166 by providing a more specific error message.

* Update Standard/src/Convert/Convert.qs

Co-Authored-By: Chris Granade <chgranad@microsoft.com>

* Allow to have bits = 0

Looks like our tests assume that number = 0 with bits = 0 is a valid scenario; updating the change to account for that

* Package updates (#188)

* Quantum AND gates (a.k.a. CCNOT with constant target) (#186)

* Two AND gate implementations.

* Added test case.

* Formatting.

* Code formatting.

* Update Standard/src/Canon/And.qs

Co-Authored-By: Chris Granade <chgranad@microsoft.com>

* Assertion for 0-target.

* Added DOI to references.

* Named application for CCNOTop.

* Rename operations.

* Add Test attribute.

* Add links to arXiv.

* Rename operations.

* Better assertion for 0-target.

* Fix bug in LowDepthAnd.

* Docs.

* Doc string convention.

* Controlled variant for `ApplyAnd`.

* Controlled AndLowDepth.

* Adjoint Controlled LowDepthAnd.

* References.

* Simplify code.

* Apply suggestions from code review

Co-Authored-By: Chris Granade <chgranad@microsoft.com>

* Integrate comment.

* Removed comment ref to IncrementByIntegerPhaseLE (#189)

There appears to be no function IncrementByIntegerPhaseLE, and I guess it is covered by ApplyLEOperationOnPhaseLE.

Co-authored-by: Chris Granade <cgranade@gmail.com>

* New Hadamard and SWAP test operations. (#196)

* First work on Hadamard and SWAP test operations.

* (c) header and typo fix.

* Fixed typo with placement of phase shift.

* Put public operations above private.

* Added tests for new operations.

* Added API documentation comments.

* Newline at end of file.

* Refactor AA namespace to use Q# style guide (#197)

* Began simplifying AA interface.

* Expose traditional AA as new public operation.

* Removed rest of "AmpAmp" prefix.

* Resolve deprecation warning.

* Begin moving classifier structure creation to Q#.

* Moving example datasets into new namespace.

* Fix datasets used in layered structure demo.

* Fixed bug with cyclic entangling layer.

* Use new Hadamard test operation to simplify grad est.

* Simplify input encoder logic.

* Removed layer construction methods moved out to Q#.

* Revised name of approximate input encoder.

* Removed unused interop library.

* Update pack script for new layout.

* Addressing feedback.

Co-authored-by: Mariia Mykhailova <michaylova@gmail.com>
Co-authored-by: bettinaheim <34236215+bettinaheim@users.noreply.github.com>
Co-authored-by: Mathias Soeken <mathias.soeken@gmail.com>
Co-authored-by: numpde <21158052+numpde@users.noreply.github.com>
This commit is contained in:
Chris Granade 2020-01-13 11:39:44 -08:00 коммит произвёл GitHub
Родитель 67cd36914e
Коммит 3e1e4619e6
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4
Build/pack.ps1
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@ -14,7 +14,7 @@ function Pack-One() {
-c $Env:BUILD_CONFIGURATION `
-v $Env:BUILD_VERBOSITY `
-o $Env:NUGET_OUTDIR `
/property:PackageVersion=$Env:NUGET_VERSION
/property:PackageVersion=$Env:NUGET_VERSION
if ($LastExitCode -ne 0) {
Write-Host "##vso[task.logissue type=error;]Failed to pack $project."
@ -29,7 +29,7 @@ Write-Host "##[info]Pack Chemistry library"
Pack-One '../Chemistry/src/DataModel/DataModel.csproj'
Write-Host "##[info]Pack QML library"
Pack-One '../MachineLearning/src/DataModel/DataModel.csproj'
Pack-One '../MachineLearning/src/MachineLearning.csproj'
Write-Host "##[info]Pack Numerics library"
Pack-One '../Numerics/src/Numerics.csproj'

76
MachineLearning.sln
Просмотреть файл

@ -3,15 +3,11 @@ Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 15
VisualStudioVersion = 15.0.26124.0
MinimumVisualStudioVersion = 15.0.26124.0
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "MachineLearning", "MachineLearning", "{A16B06ED-70E8-4494-B040-D446F0F74588}"
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "MachineLearning", "MachineLearning", "{D067C787-94C3-4DB8-9012-1F22AE784BEF}"
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "src", "src", "{E540364C-047F-446E-B8C1-CD41224E2282}"
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MachineLearningTests", "MachineLearning\tests\MachineLearningTests.csproj", "{94EBDF5F-0A9D-4CE5-9D16-3FF323B8792C}"
EndProject
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Runtime", "MachineLearning\src\Runtime\Runtime.csproj", "{4C399D64-0435-47E0-99D3-AA898E640717}"
EndProject
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "DataModel", "MachineLearning\src\DataModel\DataModel.csproj", "{E4A725A7-3525-4FC5-9794-4317B5DB9C9B}"
EndProject
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MachineLearningTests", "MachineLearning\tests\MachineLearningTests.csproj", "{0F1A243B-5263-4F97-ACAE-A7A6BAB8163B}"
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MachineLearning", "MachineLearning\src\MachineLearning.csproj", "{B045BF35-6BE6-4982-9618-8725C70D3F91}"
EndProject
Global
GlobalSection(SolutionConfigurationPlatforms) = preSolution
@ -26,47 +22,33 @@ Global
HideSolutionNode = FALSE
EndGlobalSection
GlobalSection(ProjectConfigurationPlatforms) = postSolution
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{94EBDF5F-0A9D-4CE5-9D16-3FF323B8792C}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{94EBDF5F-0A9D-4CE5-9D16-3FF323B8792C}.Debug|x86.ActiveCfg = Debug|Any CPU
{94EBDF5F-0A9D-4CE5-9D16-3FF323B8792C}.Debug|x86.Build.0 = Debug|Any CPU
{94EBDF5F-0A9D-4CE5-9D16-3FF323B8792C}.Release|Any CPU.ActiveCfg = Release|Any CPU
{94EBDF5F-0A9D-4CE5-9D16-3FF323B8792C}.Release|Any CPU.Build.0 = Release|Any CPU
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{B045BF35-6BE6-4982-9618-8725C70D3F91}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{B045BF35-6BE6-4982-9618-8725C70D3F91}.Debug|x64.Build.0 = Debug|Any CPU
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{B045BF35-6BE6-4982-9618-8725C70D3F91}.Debug|x86.Build.0 = Debug|Any CPU
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EndGlobalSection
GlobalSection(NestedProjects) = preSolution
{E540364C-047F-446E-B8C1-CD41224E2282} = {A16B06ED-70E8-4494-B040-D446F0F74588}
{4C399D64-0435-47E0-99D3-AA898E640717} = {E540364C-047F-446E-B8C1-CD41224E2282}
{E4A725A7-3525-4FC5-9794-4317B5DB9C9B} = {E540364C-047F-446E-B8C1-CD41224E2282}
{0F1A243B-5263-4F97-ACAE-A7A6BAB8163B} = {A16B06ED-70E8-4494-B040-D446F0F74588}
{94EBDF5F-0A9D-4CE5-9D16-3FF323B8792C} = {D067C787-94C3-4DB8-9012-1F22AE784BEF}
{B045BF35-6BE6-4982-9618-8725C70D3F91} = {D067C787-94C3-4DB8-9012-1F22AE784BEF}
EndGlobalSection
EndGlobal

4
MachineLearning/src/Runtime/Classification.qs → MachineLearning/src/Classification.qs
Просмотреть файл

@ -7,7 +7,7 @@ namespace Microsoft.Quantum.MachineLearning {
open Microsoft.Quantum.Canon;
open Microsoft.Quantum.Convert;
operation _PrepareClassification(
encoder : (LittleEndian => Unit is Adj + Ctl),
parameters : Double[],
@ -28,7 +28,7 @@ namespace Microsoft.Quantum.MachineLearning {
)
: Double {
let nQubits = FeatureRegisterSize(sample);
let circEnc = NoisyInputEncoder(tolerance / IntAsDouble(Length(gates!)), sample);
let circEnc = ApproximateInputEncoder(tolerance / IntAsDouble(Length(gates!)), sample);
let encodedSample = StateGenerator(nQubits, circEnc);
return 1.0 - EstimateFrequencyA(
_PrepareClassification(encodedSample::Apply, parameters, gates, _),

66
MachineLearning/src/Convert.qs Normal file
Просмотреть файл

@ -0,0 +1,66 @@
// Copyright (c) Microsoft Corporation.
// Licensed under the MIT License.
namespace Microsoft.Quantum.MachineLearning {
open Microsoft.Quantum.Intrinsic;
open Microsoft.Quantum.Canon;
open Microsoft.Quantum.Math;
function unFlattenSchedule(sc : Int[][]) : SamplingSchedule
{
mutable ret = new Range[0];
for (flattenedRange in sc) {
set ret += [flattenedRange[0]..flattenedRange[1]..flattenedRange[2]];
}
return SamplingSchedule(ret);
}
function unFlattenLabeledSamples(dat:Double[][], labs:Int[]) : LabeledSample[] {
mutable cnt = MinI(Length(dat), Length(labs));
mutable ret = new LabeledSample[cnt];
for (j in 0..(cnt - 1)) {
set ret w/= j <- LabeledSample(dat[j], labs[j]);
}
return ret;
}
/// Debugging prop
operation unFlattenPauli(p:Int): Pauli
{
if (p==1)
{
return PauliX;
}
if (p==2)
{
return PauliY;
}
if (p==3)
{
return PauliZ;
}
return PauliI;
}
/// Debugging prop
/// upcasting controlled rotation in flat representation (paramIx,pauliIx,gateSpan)
operation unFlattenControlledRotation(cod:Int[]): ControlledRotation {
return ControlledRotation(
GateSpan(
cod[2], cod[3...]
),
unFlattenPauli(cod[1]),
cod[0]
);
}
/// Debugging prop
operation unFlattenGateSequence(seq: Int[][]) : GateSequence {
mutable tmp = new ControlledRotation[Length(seq)];
for (icr in 0..(Length(seq) - 1)) {
set tmp w/= icr <- unFlattenControlledRotation(seq[icr]);
}
return GateSequence(tmp);
}
}

247
MachineLearning/src/DataModel/Interop.cs
Просмотреть файл

@ -1,247 +0,0 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
using Microsoft.Quantum.Simulation.Core;
using Microsoft.Quantum.Simulation.Simulators;
using System;
using System.IO;
using System.Linq;
using System.Runtime.InteropServices;
using System.Collections.Generic;
using System.Threading;
using System.Threading.Tasks;
/// <summary>
/// This code space provides suggested interoperability classes for running the
/// Q# Qccc (quantum circuit-centric classifier) code on Microsoft quantum simulator
/// </summary>
namespace Microsoft.Quantum.MachineLearning.Interop
{
using Microsoft.Quantum.Logical;
using Microsoft.Quantum.MachineLearning;
using System.Runtime.CompilerServices;
using System.Runtime.ExceptionServices;
using System.Runtime.InteropServices.ComTypes;
using System.Xml;
/// <summary>
/// Quick conversions into IQArray format
/// </summary>
public class Qonvert
{
public static long ToC(char pauli)
{
if (pauli.Equals('I'))
{
return 0L;
}
if (pauli.Equals('X'))
{
return 1L;
}
if (pauli.Equals('Y'))
{
return 2L;
}
if (pauli.Equals('Z'))
{
return 3L;
}
return -1L;
}
public static IQArray<IQArray<long>> ToQ(List<long[]> src)
{
List<IQArray<long>> tmp = new List<IQArray<long>>(src.Count);
for (int ix = 0; ix < src.Count; ix++)
{
tmp.Add(new QArray<long>(src[ix]));
}
return new QArray<IQArray<long>>(tmp.ToArray());
}
public static IQArray<long> ToQ(List<long> src)
{
return new QArray<long>(src.ToArray());
}
public static IQArray<IQArray<double>> ToQ(List<double[]> src)
{
List<IQArray<double>> tmp = new List<IQArray<double>>(src.Count);
for (int ix = 0; ix < src.Count; ix++)
{
tmp.Add(new QArray<double>(src[ix]));
}
return new QArray<IQArray<double>>(tmp.ToArray());
}
} //Qonvert
public class ClassificationModel
{
long _nQubits;
IQArray<IQArray<long>> _structure;
IQArray<double> _cachedParameters;
double _bias;
public ClassificationModel(long nQubits)
{
_nQubits = nQubits;
_structure = null;
_cachedParameters = null;
_bias = -2.0;
}
public ClassificationModel(long nQubits, List<long[]> structure)
{
_nQubits = nQubits;
_structure = Qonvert.ToQ(structure);
_cachedParameters = null;
_bias = -2.0;
}
public ClassificationModel(long nQubits, List<long[]> structure,double[] parameters)
{
_nQubits = nQubits;
_structure = Qonvert.ToQ(structure);
_cachedParameters = new QArray<double>(parameters);
_bias = -2.0;
}
public ClassificationModel(long nQubits, List<long[]> structure, double[] parameters, double bias)
{
_nQubits = nQubits;
_structure = Qonvert.ToQ(structure);
_cachedParameters = new QArray<double>(parameters);
_bias = bias;
}
public bool isTrained
{
get { return (_bias > -1.5) && (_structure != null) && (_cachedParameters != null); }
}
public IQArray<IQArray<long>> CircuitStructure
{
get { return _structure; }
set { _structure = value; }
}
public IQArray<double> CachedParameters
{
get { return _cachedParameters; }
}
public double Bias
{
get { return _bias; }
}
/// <summary>
/// Creates a layer of nQubits Pauli rotations
/// </summary>
/// <param name="nQubits">Number of qubits to rotate</param>
/// <param name="pauli">Type of Pauli gate</param>
/// <returns>Sequence of nQubits rotation templates</returns>
public static List<long[]> LocalRotationsLayer(long nQubits, char pauli)
{
List<long[]> ret = new List<long[]>((int)nQubits);
for (long iq = 0; iq < nQubits; iq++)
{
long[] localRp = new long[] { -1, Qonvert.ToC(pauli), iq };
ret.Add(localRp);
}
return ret;
}
/// <summary>
/// Creates a layer of nQubits Pauli rotations
/// </summary>
/// <param name="nQubits">Number of qubits to rotate</param>
/// <param name="pauli">Type of Pauli gate</param>
/// <returns>Sequence of nQubits rotation templates</returns>
public static List<long[]> PartialLocalLayer(long[] indices, char pauli)
{
List<long[]> ret = new List<long[]>(indices.Length);
foreach (long iq in indices)
{
long[] localRp = new long[] { -1, Qonvert.ToC(pauli), iq };
ret.Add(localRp);
}
return ret;
}
/// <summary>
/// Creates a cyclic block of nQubits controlled rotations that starts
/// with control qubit (nQubits-1), target qubit (cspan-1) % nQubits , followed by a
/// ladder of entanglers with control qubits iq and target qubit (iq+cspan) % nQubits
/// </summary>
/// <param name="nQubits">Number of qubits to entangle</param>
/// <param name="pauli"></param>
/// <param name="cspan">index offset between control and target qubits</param>
/// <returns></returns>
public static List<long[]> CyclicEntanglerLayer(long nQubits, char pauli, long cspan)
{
List<long[]> ret = new List<long[]>((int)nQubits);
ret.Add(new long[] { -1, Qonvert.ToC(pauli), (long) ((cspan-1) % nQubits), nQubits - 1 });
for (long iq = 1; iq < nQubits; iq++)
{
long[] entRp = new long[] { -1, Qonvert.ToC(pauli), (long)(((iq + 1) * cspan - 1) % nQubits), (long)((iq*cspan - 1) % nQubits) };
ret.Add(entRp);
}
return ret;
}
public static List<long[]> CyclicEntanglerLayer(long nQubits, char pauli)
{
return CyclicEntanglerLayer(nQubits, pauli, 1L);
}
public static List<long[]> JoinLayers(List<List<long[]>> layers)
{
List<long[]> structure = new List<long[]>(layers.Count * layers[0].Count);
for (int ila = 0; ila < layers.Count; ila++)
{
structure.AddRange(layers[ila]);
}
return structure;
}
public static void reindex(List<long[]> struc)
{
for (int ix=0; ix < struc.Count; ix++)
{
long[] gt = struc[ix];
gt[0] = ix;
struc[ix] = gt;
}
}
public long CountMisclassifications(double tolerance, IQArray<IQArray<double>> samples, IQArray<long> knownLabels, IQArray<IQArray<long>> validationSchedule, long nMeasurements, uint randomizationSeed)
{
if (this.isTrained)
{
var sim = new QuantumSimulator(false, randomizationSeed);
return CountValidationMisses.Run(sim, tolerance, this._nQubits, samples, knownLabels, validationSchedule, this._structure, this.CachedParameters, this.Bias, nMeasurements).Result;
}
return long.MaxValue;
}
public long CountMisclassifications(double tolerance, List<double[]> samples, List<long> knownLabels, List<long[]> validationSchedule, long nMeasurements, uint randomizationSeed)
{
return CountMisclassifications(tolerance, Qonvert.ToQ(samples), Qonvert.ToQ(knownLabels), Qonvert.ToQ(validationSchedule), nMeasurements, randomizationSeed);
}
public long CountMisclassifications(double tolerance, List<double[]> samples, List<long> knownLabels, long nMeasurements, uint randomizationSeed)
{
var validationSchedule = new List<long[]>(1);
validationSchedule.Add(new long[] { 0L, 1L, ((long)(samples.Count - 1)) });
return CountMisclassifications(tolerance, Qonvert.ToQ(samples), Qonvert.ToQ(knownLabels), Qonvert.ToQ(validationSchedule), nMeasurements, randomizationSeed);
}
} //class ClassificationModel
}

13
MachineLearning/src/Runtime/Examples.qs → MachineLearning/src/Datasets/IrisDataset.qs
Просмотреть файл

@ -1,7 +1,8 @@
namespace Microsoft.Quantum.MachineLearning {
open Microsoft.Quantum.Primitive;
open Microsoft.Quantum.Convert;
open Microsoft.Quantum.Math;
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
namespace Microsoft.Quantum.MachineLearning.Datasets {
open Microsoft.Quantum.MachineLearning;
operation IrisTrainingData() : LabeledSample[] {
return [LabeledSample(([0.581557, 0.562824, 0.447721, 0.380219], 1)),
@ -87,8 +88,4 @@ namespace Microsoft.Quantum.MachineLearning {
];
}
operation Examples () : Unit
{
}
}

6
MachineLearning/src/Datasets/Properties/NamespaceInfo.qs Normal file
Просмотреть файл

@ -0,0 +1,6 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
/// # Summary
/// TODO
namespace Microsoft.Quantum.MachineLearning.Datasets { }

190
MachineLearning/src/Datasets/WineDataset.qs Normal file
Просмотреть файл

@ -0,0 +1,190 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
namespace Microsoft.Quantum.MachineLearning.Datasets {
open Microsoft.Quantum.MachineLearning;
operation WineData() : LabeledSample[] {
return [
LabeledSample([0.363205411076437,0.0638668636484459,0.178532471398649,-0.0288976056473681,0.433507480753481,0.26765452507215,0.295933366591769,0.0407618624310926,0.335961380122613,0.183046369886591,0.197761713895692,0.42801676498221,0.286786326879289,0.102563320682669,0.102563320682669,0.102563320682669], 1),
LabeledSample([0.249938938530866,0.103806961963521,0.0288119563511447,-0.316327610625978,0.213915431364644,0.331181226646993,0.358247971682033,0.0243452672750831,0.102474003311389,0.142145109786172,0.289810795079708,0.452985093276961,0.397496120059943,0.145592040692672,0.145592040692672,0.145592040692672], 1),
LabeledSample([0.18017551770934,0.159954186450334,0.330335005051653,0.11029657222382,0.172026075662364,0.285685353685049,0.3437678958266,0.0687101235918643,0.503546189307832,0.198186899162854,0.204255658590153,0.289189504133849,0.363892624800524,0.109472606661304,0.109472606661304,0.109472606661304], 1),
LabeledSample([0.3478185280261,0.0855741081675129,0.193962405871525,0.0214750733479136,0.256731607736543,0.454743707959586,0.32073047384103,-0.00578276910401499,0.274962030580035,0.291023282748096,0.0739296574471514,0.294961529988612,0.424228072429038,0.091790139403591,0.091790139403591,0.091790139403591], 1),
LabeledSample([0.362297632590457,0.0713765952097998,0.1918508326995,-0.0471194731007316,0.279898551657966,0.380054980216169,0.348218985017828,0.113013918394729,0.255594598610856,0.259339012361842,0.206751611476739,0.206506348822603,0.466330974472638,0.103865658362098,0.103865658362098,0.103865658362098], 1),
LabeledSample([0.456363512237805,0.0989267315498482,0.220396293818176,-0.0848977157377709,0.126570706690875,0.231455369932631,0.253057526681058,0.0748906926624935,0.296662776679902,0.183096193491806,0.215186020544714,0.415101672494017,0.45173680541366,0.119319816525617,0.119319816525617,0.119319816525617], 1),
LabeledSample([0.365824230547686,0.134384795182974,0.304649953841435,0.0652321211089234,0.408030734314465,0.244219941368521,0.237852026774996,0.0838599153918433,0.0708419429859215,0.158763660222825,0.231785452457851,0.392781508852428,0.429930004244632,0.112903947819199,0.112903947819199,0.112903947819199], 1),
LabeledSample([0.566183478055546,0.0654724741578849,0.0439622864967305,-0.119206110391732,0.143004569028705,0.320916045012887,0.340893691950237,0.0630283295835976,0.305745022199024,0.184756786750361,0.267797291682324,0.244495204044942,0.333036138702624,0.122972758364531,0.122972758364531,0.122972758364531], 1),
LabeledSample([0.337713046108315,0.122718945059575,0.10700561052042,0.0764645126338355,0.201946721703765,0.300339986693404,0.331847112159928,-0.0299072363915305,0.357482732812817,0.189234861665545,0.330277114346835,0.269442761691643,0.484863724448287,0.10199748278123,0.10199748278123,0.10199748278123], 1),
LabeledSample([0.466701129863441,0.0454693396504955,0.161149317242173,0.0326284696596175,0.133694628665751,0.177107619792893,0.278006483001738,0.0233203461257999,0.201144615896292,0.191636316683084,0.381999714367244,0.268736937921384,0.521861643126541,0.139462703109841,0.139462703109841,0.139462703109841], 1),
LabeledSample([0.352356775250346,0.0849147066288441,0.213994619198652,-0.0143836459418002,0.0452884898165237,0.283231761082382,0.322192973337249,0.0671114837383224,0.268871309289799,0.230329073599442,0.342318391134332,0.273703498451036,0.513006853430941,0.130939282823474,0.130939282823474,0.130939282823474], 1),
LabeledSample([0.43089413940549,0.0626199622575399,0.147535243701546,-0.201497441491844,0.0531208270245816,0.316671679326467,0.364741512975659,0.205099273306104,0.444154125496589,0.157464852880314,0.312671915003017,0.168320603210868,0.306105868416982,0.096560575593511,0.096560575593511,0.096560575593511], 1),
LabeledSample([0.272726407517705,0.0827168324333284,0.3496106940634,0.0446612423026689,0.296538163194931,0.299452238244459,0.294137889421737,0.0690664590267155,0.12788722340928,0.313586218717624,0.376912296280345,0.225524110093736,0.426286381219746,0.110040339141755,0.110040339141755,0.110040339141755], 1),
LabeledSample([0.383572208467366,0.0935136576220856,0.343620408177357,0.168402125357628,0.367514242898029,0.27310051209359,0.313807941452454,0.101821494321014,0.257525175254316,0.224370057363687,0.22136861749585,0.165326053699488,0.391595143068333,0.104650184385657,0.104650184385657,0.104650184385657], 1),
LabeledSample([0.316013513913644,0.0493226285823722,0.306955303083295,0.179519413042103,0.334809905839043,0.328494083962476,0.375590411592675,0.198432370679759,0.203509333335944,0.267811707061401,0.277734218718739,0.158015615196535,0.343836981820164,0.111558803879491,0.111558803879491,0.111558803879491], 1),
LabeledSample([0.31870497106277,0.0429712614071499,0.184285151711081,0.00964542259308528,0.209961702784,0.342183874334946,0.378094966508491,0.0787814637408952,0.20026575558015,0.344191686729784,0.285912288149745,0.176960266353846,0.505227332666082,0.0918346293577422,0.0918346293577422,0.0918346293577422], 1),
LabeledSample([0.356307758352805,0.0571295107798862,0.103665767801926,-0.012079833971045,0.453569690442849,0.336083659933176,0.334421582101246,-0.00692790241244995,0.307010347549411,0.196965160324229,0.246333773806227,0.41175624147722,0.169625166567189,0.109966889006815,0.109966889006815,0.109966889006815], 1),
LabeledSample([0.161217748043416,0.432012087856065,0.378968684463582,0.131159855324296,0.217860878028004,0.226361121435969,0.255816095501333,0.00678340496692604,0.323664339685322,0.137120494937026,0.242891887659839,0.436933255666067,0.195077722155152,0.130180031547429,0.130180031547429,0.130180031547429], 1),
LabeledSample([0.33387321962864,0.104003734911291,0.174357166514354,0.0188598420614302,0.200238000417398,0.278477279672617,0.335197145956839,0.0359753128735102,0.222720512525599,0.076991773708088,0.301339874113075,0.552589883968051,0.339490864618573,0.127425890371198,0.127425890371198,0.127425890371198], 1),
LabeledSample([0.164804886510591,0.0722538432783593,0.333530926538808,0.0997386246383389,0.144057309090506,0.28478622392534,0.286789335102904,0.0251279078559012,0.174644396421027,0.103329402020434,0.336621180516676,0.538126003301765,0.387138650464392,0.150272467405704,0.150272467405704,0.150272467405704], 1),
LabeledSample([0.138045034940391,0.10096733243651,0.565457577461934,0.357529010586571,0.372975197652389,0.212164465546768,0.223324251871177,0.245882968670251,0.222005459488057,0.0440548078915432,0.236854156367615,0.257151478091355,0.167676962473548,0.0951383178498998,0.0951383178498998,0.0951383178498998], 1),
LabeledSample([0.27706228465463,0.0944690423993351,0.371279295793269,-0.0090237059702085,0.101794091444167,0.367202038390134,0.366416680746154,0.14682141141967,0.153385221675984,0.16810232470317,0.159181546018473,0.370233800319128,0.454471601718924,0.134936499639745,0.134936499639745,0.134936499639745], 1),
LabeledSample([0.317210982002182,0.106460924648269,0.033146499020418,0.0535834830689089,0.143461867556865,0.287506074549938,0.276972889518789,0.0472878373297268,0.147745460243886,0.117321269850464,0.302067517823741,0.305652499113775,0.63044189160374,0.167495270900221,0.167495270900221,0.167495270900221], 1),
LabeledSample([0.323782804937267,0.0967686336582948,0.413611657738741,0.150690255678227,0.243561977694651,0.328356719570779,0.307544863864273,0.15984164703184,0.214782154388153,0.117457351693143,0.361086484054943,0.346951270439723,0.238229898839817,0.111512154006688,0.111512154006688,0.111512154006688], 1),
LabeledSample([0.279349555106746,0.0370104144183211,0.334213378417837,0.282297751914523,0.165302668712666,0.321496686499494,0.331821540835722,0.0539160322994988,0.368686009342752,0.191790839601492,0.301258189226166,0.179073860160303,0.395943808377124,0.105194017606742,0.105194017606742,0.105194017606742], 1),
LabeledSample([0.27005015291199,0.0630772889648215,0.154616485583782,0.138130446588243,0.235268666883373,0.310026088106195,0.346841534612754,-0.0331329802316931,0.27231483724987,0.293017836931826,0.253125380319702,0.23158729460557,0.539645635992194,0.112998758441953,0.112998758441953,0.112998758441953], 1),
LabeledSample([0.346713411485213,0.105012869661639,0.184811072491187,0.0548181961587539,0.253625600781446,0.237872750892395,0.318961201449689,0.271339376712739,0.332372860697775,0.0850741828543554,0.420506007983091,0.274055170038427,0.333109503610011,0.135065944999207,0.135065944999207,0.135065944999207], 1),
LabeledSample([0.271022354903467,0.0390745208209509,0.317889164962927,0.139509100089267,0.474001987855805,0.294622995319019,0.243367356561056,0.29314414864518,0.0882580916700038,0.163164893944254,0.323990267781163,0.229579697508733,0.354597817947863,0.100055953996837,0.100055953996837,0.100055953996837], 1),
LabeledSample([0.296171884335753,0.100614775138648,0.218752326936746,0.244152694965527,0.196663727926573,0.319415898270834,0.37104777890512,0.0223818870342515,0.291890527888441,0.19251205199505,0.258089258396546,0.435586076048342,0.288896417266678,0.133850434708707,0.133850434708707,0.133850434708707], 1),
LabeledSample([0.252373671257443,0.0723018808380158,0.532596533272876,-0.044098308106594,0.338217719219032,0.293745858093293,0.3187720066088,0.147760962531924,0.123245390734908,0.151752742109324,0.304201799039272,0.250586710453292,0.269209619708056,0.135799995890695,0.135799995890695,0.135799995890695], 1),
LabeledSample([0.231647849319649,0.0870580778073824,0.379819510930802,0.119683262892996,0.201958323330672,0.291859534446306,0.318243685177281,0.0818257194381086,0.177409914789709,0.142552607383287,0.38749726469097,0.206569993185964,0.474496045065283,0.159647804264275,0.159647804264275,0.159647804264275], 1),
LabeledSample([0.255247226696605,0.06041599445067,-0.00255968322164522,-0.0557623660678594,0.217229013118827,0.294756572623486,0.393362717775018,0.0682465918139527,0.160216510643726,0.0927370291446394,0.481064257912117,0.285307935542058,0.446167701011294,0.171719265613694,0.171719265613694,0.171719265613694], 1),
LabeledSample([0.275026515547499,0.0727400792013697,0.128762793923213,-0.00279155677458797,0.374437692908058,0.396130353966779,0.375086796481439,0.127101363132371,0.397509763863738,0.245200476434608,0.159660362687242,0.358672446067021,0.187892640693357,0.116812751455572,0.116812751455572,0.116812751455572], 1),
LabeledSample([0.311461752837503,0.502549497625733,0.0136433770988428,0.163056233779351,0.0668046663611401,0.272607631039843,0.350031792999902,0.0420992128778312,0.1808955506979,0.136019537594797,0.166608583769084,0.34215084378215,0.397280719417458,0.149116970955777,0.149116970955777,0.149116970955777], 1),
LabeledSample([0.335978144067938,0.0983210378580677,0.298097188286758,-0.0620608660613614,0.180728542341856,0.415699424320707,0.413265127196523,-0.0999159645032936,0.203949116092578,0.189765659712172,0.106979744949575,0.395413251109613,0.336768787203074,0.115010614246019,0.115010614246019,0.115010614246019], 1),
LabeledSample([0.267254400330717,0.530864539992977,0.148894591250536,0.0799552165600163,0.265475033620942,0.336749597682553,0.340364890972572,0.128256883977383,0.249894873995414,0.144049494069581,0.0831136106174955,0.343047025915129,0.164850715568234,0.14950754712276,0.14950754712276,0.14950754712276], 1),
LabeledSample([0.409535358236058,0.423821376417793,0.209554705754144,0.132754750116135,0.302864073191331,0.3178875886134,0.26924524714014,0.0733184371664153,0.0732958296204928,0.178502810621827,0.101371504171095,0.346751724293268,0.334343976936978,0.116814815828549,0.116814815828549,0.116814815828549], 1),
LabeledSample([0.411838215785579,0.329697985039249,0.101980670189617,-0.0118834750397091,0.181041987677738,0.391008260201782,0.328985525228739,0.0305415683419268,0.326811408573422,0.141709171615972,0.208590524286652,0.345418151382434,0.302489845499348,0.108179366027653,0.108179366027653,0.108179366027653], 1),
LabeledSample([0.365420884131811,0.0721780607230944,0.0113078420011285,-0.0135763788382327,0.194210964330357,0.405316516129519,0.414348135414213,-0.0504648498369757,0.357633344975218,0.257048685083646,0.13808777164507,0.366864443994308,0.3020895160535,0.123590452351992,0.123590452351992,0.123590452351992], 1),
LabeledSample([0.4006706465907,0.123743705191569,0.191332850350624,0.13232426073567,0.214876890555312,0.302289422501197,0.325178974246259,0.103811797461618,0.424125864699886,0.259450220277008,0.255979506564098,0.215885956908381,0.335711269129357,0.121012196145575,0.121012196145575,0.121012196145575], 1),
LabeledSample([0.165010086109523,0.0737493375917614,-0.0379955147007807,-0.188436093151012,0.0741761269601386,0.276461086643119,0.361942753242732,-0.0987966298943638,0.552050688643421,0.316782308151838,0.276026551516262,0.240036696328028,0.360509719502187,0.113722177892932,0.113722177892932,0.113722177892932], 1),
LabeledSample([0.322538934941513,0.0620906870973839,0.30117864344653,0.0462124741572112,0.0975246212047387,0.208157123222826,0.317250883177015,-0.0333862138801274,0.372973198362888,0.200289943745998,0.361594501059424,0.347288303861623,0.41855376578972,0.113862402088516,0.113862402088516,0.113862402088516], 1),
LabeledSample([0.285886366617401,0.0684810811583678,0.171380327231864,-0.0984477110534525,0.263309088994922,0.509940512787294,0.390809494758036,0.0871231538320202,0.224056703303123,0.27312555178283,0.176819633724361,0.286947787961415,0.33981923310787,0.101558439774049,0.101558439774049,0.101558439774049], 1),
LabeledSample([0.297296105273249,0.0946255708393242,0.334838488001863,0.0395700727110477,0.327258215505474,0.333258754902954,0.272944821496624,0.181405049197193,0.187812762259515,0.240783305911936,0.271469879527389,0.23461272684689,0.453599279540649,0.109042577429231,0.109042577429231,0.109042577429231], 1),
LabeledSample([0.269540789063922,0.0742425940934737,0.217552306967554,0.208824725336092,0.355277219636782,0.339660238719292,0.28494135945769,-0.0599238355949813,0.421647315225907,0.249123651029931,0.17789873992421,0.269695620986192,0.347813079058712,0.114482784095866,0.114482784095866,0.114482784095866], 1),
LabeledSample([0.404625688519658,0.0700267657079979,0.120472577645772,0.00219084178798748,0.379823180206969,0.402241148897651,0.321584301076104,0.0192020687698115,0.300130523389213,0.259466069761067,0.149793101053706,0.336182443721132,0.273109601918351,0.114834162473097,0.114834162473097,0.114834162473097], 1),
LabeledSample([0.227072284249524,0.115957953910278,0.37165420977739,0.0283164248068712,0.202551129726376,0.369900783939047,0.378352967053039,0.0898969763675667,0.202299134157373,0.243962099768371,0.256021152092969,0.238164818457677,0.447570516715675,0.121031883725162,0.121031883725162,0.121031883725162], 1),
LabeledSample([0.290513586918522,0.0288038623450319,0.232722173051494,0.021033401910653,0.251796587067657,0.434955256846487,0.412889359036101,-0.0703239873536929,0.290647100154398,0.27851991038108,0.109312082995173,0.223464411693962,0.414532651593511,0.110132685819811,0.110132685819811,0.110132685819811], 1),
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LabeledSample([0.471825455298159,0.0805157625548287,0.102338403158557,0.0106143299846797,0.13783550627248,0.282993959641305,0.255288568929083,0.0787232630337918,0.283117434475593,0.174153574685115,0.272150764672242,0.413684819363966,0.424013222162737,0.134641493192199,0.134641493192199,0.134641493192199], 1),
LabeledSample([0.34641200164034,0.099744551278731,0.38776203448908,0.181916386282477,0.311546673657173,0.175216256169144,0.293174280061248,0.125937342360261,0.139917604026267,0.136174356784758,0.320610477987196,0.219965569994473,0.463069568846749,0.133283652542607,0.133283652542607,0.133283652542607], 1),
LabeledSample([0.404853350133791,0.366370427401772,0.233457215510936,-0.117646343734435,0.422400124731543,0.316834280568532,0.311952030312985,-0.0142353771929971,0.245689562716628,0.158495894279496,0.111287504351714,0.309881505730385,0.174043923126029,0.10490304431372,0.10490304431372,0.10490304431372], 1),
LabeledSample([0.25251240458689,0.100686161713017,0.0315578727081392,0.0718934503369092,0.291240948285929,0.427841821532188,0.412648557737393,0.116841462722393,0.314820284876223,0.209407419028952,0.141208499852881,0.368089079182708,0.330970427145695,0.141657365746273,0.141657365746273,0.141657365746273], 1),
LabeledSample([0.350364491958287,0.0688422581963099,0.109742569360303,0.0707531010098024,0.224015978099777,0.28190906228017,0.388953246261309,0.135993554782958,0.244051253148224,0.3726688048422,0.264003118925063,0.219311926596273,0.455200762754877,0.104203528797661,0.104203528797661,0.104203528797661], 1),
LabeledSample([0.397327283049911,0.0773527405250614,0.126236452283931,0.0330706981684607,0.408012616077325,0.260360655336735,0.364814158384692,-0.00206578487920926,0.163991130541285,0.253478466473427,0.165540214451542,0.30416373966438,0.433971873268533,0.132120542922879,0.132120542922879,0.132120542922879], 1),
LabeledSample([0.107297174901783,-0.0613762696733566,-0.632314571586212,-0.416852421444992,0.0508952503409911,0.064074731214031,-0.144536144592534,0.153765312304364,-0.303969426648252,-0.0375252361713336,0.388754164139066,-0.078967037248287,0.0668579337914334,0.186423454283019,0.186423454283019,0.186423454283019], 1),
LabeledSample([0.0337268344229458,-0.00184814410831371,-0.171994660095448,0.0487910037701937,0.715459227632856,0.00029799084750352,0.034974910385745,-0.0990313039755913,0.387197494235993,0.033304059568937,0.411585327770438,0.236608730976454,0.165595696405405,0.115678798001455,0.115678798001455,0.115678798001455], 1),
LabeledSample([-0.0223561749320937,-0.0207526232365009,0.176225433251552,0.326430295003409,0.219297804670973,0.590124842186138,0.236154622574307,-0.143389150319129,0.192871802948094,0.0710337180025261,0.249885838045267,0.317860451806615,0.343996729607254,0.146892033179513,0.146892033179513,0.146892033179513], 1),
LabeledSample([0.032885091939905,0.0461926134522565,0.34638380929578,0.203505193733194,0.264570166804038,0.0160341545388597,0.151445308394402,0.427311190150461,-0.0254549665189148,0.0510306773298033,0.692797615252923,0.0575883314039526,-0.0798332036067795,0.149101002314511,0.149101002314511,0.149101002314511], 0),
LabeledSample([0.142629866189693,0.410472062836346,0.207336198359131,0.378232988385718,0.183357859335752,0.316154994878092,0.273883001957771,0.322495142977759,0.249438841565115,0.0166674724899229,0.366212353243424,0.265767421147697,0.00611339840415559,0.122818414884191,0.122818414884191,0.122818414884191], 0),
LabeledSample([-0.0679233939259161,-0.072886846046031,0.477512324840591,0.174125713388414,0.155497662993969,0.17129901382616,0.318776243273762,0.0194230007106634,0.555783444679686,0.0737664378133595,0.0782223803712395,0.384101054511456,0.0667032731300068,0.185992205916374,0.185992205916374,0.185992205916374], 0),
LabeledSample([0.00999269298689842,0.0262030266834996,0.197155588375633,0.548367414525349,-0.232718880830273,0.151355729009093,0.127012275111873,0.411786294151836,0.109592670067503,-0.0518380454362711,0.363744240593521,0.381580392735396,0.15243777913074,0.164338283611199,0.164338283611199,0.164338283611199], 0),
LabeledSample([-0.131400682086754,0.171521987350065,0.191616648663646,0.162786759491588,0.210638981678084,0.534289546493504,0.311213752749673,-0.0862263866819605,0.103472561468339,0.0878652293863465,0.485065843433271,0.317875059263178,-0.102522755165598,0.173880513696912,0.173880513696912,0.173880513696912], 0),
LabeledSample([0.0506049151151188,0.287715578010986,0.0887170666404207,0.109273692933807,0.0318657512060172,0.306583832312144,0.338235987285259,0.334511582631667,0.477276067209907,-0.00128966778775066,0.519921454232635,0.183408801865957,-0.0302403142292243,0.116720585385637,0.116720585385637,0.116720585385637], 0),
LabeledSample([0.155537012760084,0.12469512047752,-0.0685853167040014,0.378983217011539,-0.0016059855847115,0.173834869196373,0.240415288132954,0.246327374233871,0.171038483773879,0.0635733414875313,0.381511309986817,0.572462983878879,0.223146044444717,0.188745574564891,0.188745574564891,0.188745574564891], 0),
LabeledSample([-0.106888248404423,0.0254982746554491,0.435809951544879,0.247866151735291,0.115539233295032,0.323105832423766,0.385789333857532,0.130580401233695,0.459264529749233,0.0247638879079171,0.20854914148463,0.334532005981271,0.169375895367498,0.138197555234402,0.138197555234402,0.138197555234402], 0),
LabeledSample([0.0100012070121382,0.0385909848160964,0.358005307896772,0.471274773272444,-0.0168340126254455,0.306861704693128,0.301875027533534,0.431885187577969,-0.024144046014302,0.0518235705697981,0.216614240137034,0.375408000771501,-0.0613295291494424,0.164478303953699,0.164478303953699,0.164478303953699], 0),
LabeledSample([-0.0156009038121407,0.536226598008622,-0.0510488844092566,0.262251476208149,0.0344260697722476,0.0761242352448198,0.163083120634928,0.366107933805052,0.353446929363601,-0.108649979874075,0.253047115727131,0.385416407008332,0.114718978391023,0.192142309706158,0.192142309706158,0.192142309706158], 0),
LabeledSample([-0.0370723732585497,0.462706141337283,0.194819679329056,0.264097858693995,-0.0344675209732017,0.315276508881407,0.351600930409003,-0.00185817840721325,0.514394278278426,0.030985137447176,0.0195438132713035,0.376880126062964,-0.0475330499513248,0.118842742281355,0.118842742281355,0.118842742281355], 0),
LabeledSample([-0.0016596349541218,0.631802778994405,0.258923690167821,0.431565727191915,-0.0777693312326763,0.107217718812087,0.165357588010325,0.407926583994051,0.0968775514246654,0.0247476673279324,0.068467607179257,0.172207746838557,0.111312979635777,0.16279792602384,0.16279792602384,0.16279792602384], 0),
LabeledSample([0.143710241385037,0.00813969645743141,0.166626922787307,0.125887427831217,-0.00148386915721329,0.0690924636334773,-0.132562337719547,0.813875842290254,0.0620432660245373,0.296664745393943,0.0286792231349211,-0.180653988476775,0.185389155342318,0.174393680319152,0.174393680319152,0.174393680319152], 0),
LabeledSample([0.194304705256292,0.30346843378612,0.374041666089729,0.48352572078128,0.205759387922832,0.170334766020302,-0.100580387509185,0.527373130970181,-0.0960922066168464,0.194750484480948,0.146211633441668,0.031454392555221,0.10170488371806,0.137823608782152,0.137823608782152,0.137823608782152], 0),
LabeledSample([0.0570260846145295,0.52764742101151,0.111869455738976,0.180967382649137,0.397178358074248,-0.193581548022407,-0.0718130900746043,0.148619617280605,0.014552586962639,0.503530729139833,-0.0748446558522674,-0.0107044561285139,0.341924057887159,0.157289053900933,0.157289053900933,0.157289053900933], 0),
LabeledSample([0.248098725379752,0.456746224327013,0.201739035375683,0.329290659490806,0.353941083012747,-0.155688188640652,0.0634239539879236,0.208825572195049,0.0125037152349941,0.520052128663407,-0.13353266010295,-0.188043841093254,0.0704360793068273,0.135144187394862,0.135144187394862,0.135144187394862], 0),
LabeledSample([0.154461249938759,0.0178069859154663,0.0362860191116435,0.292137716451667,0.273737481826492,-0.157722750938907,-0.123811269772362,0.623254105885853,0.116416845998361,0.467427306225011,-0.150509693940165,-0.155859335652357,0.164324900252044,0.162881127264595,0.162881127264595,0.162881127264595], 0),
LabeledSample([0.283967248673866,0.0653052200973657,0.31906371675043,0.319600990343548,0.0409193502319166,0.21981005179708,0.00324997177552983,0.413419912003247,0.317297662817938,0.571709617497126,-0.110212961327936,-0.0560612150021478,0.100443678932404,0.111543056863857,0.111543056863857,0.111543056863857], 0),
LabeledSample([0.437022295249671,0.277016772723886,0.170318463731936,0.376347873321372,0.0654029814959019,-0.18590177141098,-0.0863664388167373,0.338687920702156,-0.0242372506105056,0.560202129237804,-0.0221037853452648,-0.0993942479877979,0.123995050925897,0.141968301419256,0.141968301419256,0.141968301419256], 0),
LabeledSample([0.273452627665534,0.484246176255015,0.447645208017183,0.447862902118889,0.287256166753207,0.0390992590174893,-0.0208585770470468,0.0801713198810822,0.00235790979876691,0.382106195445461,-0.0395631261301076,-0.0257173942959117,0.108602367051486,0.11375808821732,0.11375808821732,0.11375808821732], 0),
LabeledSample([0.323768883858918,0.456898831708986,0.280009612342141,0.414792798825381,0.213720336482119,-0.0173248427920594,-0.0676281609721251,0.35366736131149,0.0994909525019752,0.364562841125483,-0.020970585691538,-0.12622312846499,0.216164946371533,0.13468998110011,0.13468998110011,0.13468998110011], 0)
];
}
}

0
MachineLearning/src/Runtime/Features.qs → MachineLearning/src/Features.qs
Просмотреть файл

56
MachineLearning/src/Runtime/GradientEstimation.qs → MachineLearning/src/GradientEstimation.qs
Просмотреть файл

@ -13,40 +13,25 @@ namespace Microsoft.Quantum.MachineLearning {
open Microsoft.Quantum.Characterization;
// NOTE: the last qubit of 'reg' in this context is the auxillary qubit used in the Hadamard test.
operation _endToEndHTcircuit(enc: (LittleEndian => Unit is Adj + Ctl), param1 : Double[], gates1: GateSequence, param2 : Double[], gates2: GateSequence, reg: Qubit[]): Unit is Adj + Ctl {
let L = Length(reg) - 1;
let g1 = _ApplyGates(param1, gates1, _);
let g2 = _ApplyGates(param2, gates2, _);
enc(LittleEndian(reg[0..(L-1)]));
within {
H(Tail(reg));
} apply {
(Controlled g1) ([reg[L]], reg[0..(L-1)]);
within {
X(Tail(reg));
} apply {
(Controlled g2) ([reg[L]], reg[0..(L-1)]);
(Controlled Z) ([reg[L]], reg[(L-1)]);
}
}
operation _ApplyLEOperationToRawRegister(op : (LittleEndian => Unit is Adj), target : Qubit[]) : Unit is Adj {
op(LittleEndian(target));
}
operation endToEndHTcircuit(enc: (LittleEndian => Unit is Adj + Ctl),param1 : Double[], gates1: GateSequence, param2 : Double[], gates2: GateSequence) : (Qubit[] => Unit is Adj) {
return _endToEndHTcircuit(enc,param1, gates1, param2, gates2, _);
}
operation HardamardTestPhysical(enc2: (LittleEndian => Unit is Adj + Ctl), param1 : Double[], gates1: GateSequence, param2 : Double[], gates2: GateSequence, nQubits: Int, nMeasurements : Int): Double {
return 1.0 - EstimateFrequencyA(
endToEndHTcircuit(enc2,param1,gates1,param2,gates2),
_TailMeasurement(nQubits),
nQubits,
nMeasurements
operation _EstimateDerivativeWithParameterShift(
inputEncoder : StateGenerator,
gates : GateSequence,
parameters : (Double[], Double[]),
nQubits : Int,
nMeasurements : Int
) : Double {
return EstimateRealOverlapBetweenStates(
_ApplyLEOperationToRawRegister(inputEncoder::Apply, _),
_ApplyGates(Fst(parameters), gates, _),
_ApplyGates(Snd(parameters), gates, _),
nQubits, nMeasurements
);
}
/// # Summary
/// polymorphic classical/quantum gradient estimator
///
@ -96,9 +81,9 @@ namespace Microsoft.Quantum.MachineLearning {
w/ gate::Index <- (param[gate::Index] + PI());
// NB: This the *antiderivative* of the bracket
let newDer = 2.0 * HardamardTestPhysical(
sg::Apply, param, gates, paramShift, gates, nQubits + 1, nMeasurements
) - 1.0;
let newDer = _EstimateDerivativeWithParameterShift(
sg, gates, (param, paramShift), nQubits, nMeasurements
);
if (IsEmpty(gate::Span::ControlIndices)) {
//uncontrolled gate
set grad w/= gate::Index <- grad[gate::Index] + newDer;
@ -108,10 +93,9 @@ namespace Microsoft.Quantum.MachineLearning {
w/ gate::Index <- (param[gate::Index] + 3.0 * PI());
//Assumption: any rotation R has the property that R(\theta+2 Pi)=(-1).R(\theta)
// NB: This the *antiderivative* of the bracket
let newDer1 = 2.0 * HardamardTestPhysical(
sg::Apply, param, gates, controlledShift, gates, nQubits + 1,
nMeasurements
) - 1.0;
let newDer1 = _EstimateDerivativeWithParameterShift(
sg, gates, (param, controlledShift), nQubits, nMeasurements
);
set grad w/= gate::Index <- (grad[gate::Index] + 0.5 * (newDer - newDer1));
}
}

44
MachineLearning/src/Runtime/InputEncoding.qs → MachineLearning/src/InputEncoding.qs
Просмотреть файл

@ -64,23 +64,24 @@ namespace Microsoft.Quantum.MachineLearning {
}
}
function NoisyInputEncoder(tolerance: Double,coefficients : Double[]) : (LittleEndian => Unit is Adj + Ctl) {
function ApproximateInputEncoder(tolerance : Double,coefficients : Double[])
: (LittleEndian => Unit is Adj + Ctl) {
//First quantize the coefficients: for a coef x find such y*tolerance, where y is integer and |x-y*tolerance| \neq tolerance/2
let nCoefficients = Length(coefficients);
mutable coefficientsComplexPolar = new ComplexPolar[nCoefficients];
mutable complexCoefficients = new ComplexPolar[Length(coefficients)];
mutable cNegative = 0;
for (idx in 0 .. nCoefficients - 1) {
mutable coef = coefficients[idx];
for ((idx, coef) in Enumerated(coefficients)) {
mutable magnitude = coef;
if (tolerance > 1E-9) {
set coef = tolerance * IntAsDouble(Round(coefficients[idx] / tolerance)); //quantization
set magnitude = tolerance * IntAsDouble(Round(coefficients[idx] / tolerance)); //quantization
}
mutable ang = 0.0;
if (coef < 0.0) {
if (magnitude < 0.0) {
set cNegative += 1;
set coef = -coef;
set magnitude = -magnitude;
set ang = PI();
}
set coefficientsComplexPolar w/= idx <- ComplexPolar(coef, ang);
set complexCoefficients w/= idx <- ComplexPolar(magnitude, ang);
}
// Check if we can apply the explicit two-qubit case.
@ -92,11 +93,11 @@ namespace Microsoft.Quantum.MachineLearning {
// Here, by a "few," we mean fewer than the number of qubits required
// to encode features.
if ((cNegative > 0) and (IntAsDouble(cNegative) < Lg(IntAsDouble(Length(coefficients))) + 1.0)) {
return _EncodeSparseNegativeInput(cNegative, tolerance, coefficientsComplexPolar, _); //TODO:MORE:ACCEPTANCE ("Wines" passing soi far)
return _EncodeSparseNegativeInput(cNegative, tolerance, complexCoefficients, _); //TODO:MORE:ACCEPTANCE ("Wines" passing soi far)
}
// Finally, we fall back to arbitrary state preparation.
return ApproximatelyPrepareArbitraryState(tolerance, coefficientsComplexPolar, _);
return ApproximatelyPrepareArbitraryState(tolerance, complexCoefficients, _);
} //EncodeNoisyInput
//TODO:REVIEW: Design consideration! The implicit qubit count must be read off from the state encoder, NOT from the gate sequence!
@ -105,23 +106,18 @@ namespace Microsoft.Quantum.MachineLearning {
/// The vector of 'coefficients' does not have to be unitary
function InputEncoder(coefficients : Double[]): (LittleEndian => Unit is Adj + Ctl) {
//default implementation, does not respect sparcity
let nCoefficients = Length(coefficients);
mutable coefficientsComplexPolar = new ComplexPolar[nCoefficients];
mutable allPositive = true;
for (idx in 0 .. nCoefficients - 1) {
mutable coef = coefficients[idx];
mutable ang = 0.0;
if (coef < 0.0) {
set allPositive = false;
set coef = -coef;
set ang = PI();
}
set coefficientsComplexPolar w/= idx<-ComplexPolar(coef,ang);
mutable complexCoefficients = new ComplexPolar[Length(coefficients)];
for ((idx, coefficient) in Enumerated(coefficients)) {
set complexCoefficients w/= idx <- ComplexPolar(
coefficient >= 0.0
? (coefficient, 0.0)
| (-coefficient, PI())
);
}
if (_CanApplyTwoQubitCase(coefficients)) {
return _ApplyTwoQubitCase(coefficients, _);
}
return ApproximatelyPrepareArbitraryState(1E-12, coefficientsComplexPolar, _); //this is preparing the state almost exactly so far
return ApproximatelyPrepareArbitraryState(1E-12, complexCoefficients, _); //this is preparing the state almost exactly so far
}
}
}

38
MachineLearning/src/DataModel/DataModel.csproj → MachineLearning/src/MachineLearning.csproj
Просмотреть файл

@ -1,26 +1,36 @@
<Project Sdk="Microsoft.NET.Sdk">
<Project Sdk="Microsoft.Quantum.Sdk/0.10.1912.2605-alpha">
<PropertyGroup>
<TargetFramework>netstandard2.1</TargetFramework>
<PlatformTarget>x64</PlatformTarget>
<AssemblyName>Microsoft.Quantum.MachineLearning.DataModel</AssemblyName>
<DocumentationFile>bin\$(BuildConfiguration)\$(PlatformTarget)\$(AssemblyName).xml</DocumentationFile>
<AssemblyName>Microsoft.Quantum.MachineLearning</AssemblyName>
<QsharpDocsGeneration>true</QsharpDocsGeneration>
<!--
Include core packages using project references instead, since they get
built at the same time as this project.
-->
<IncludeQsharpCorePackages>false</IncludeQsharpCorePackages>
</PropertyGroup>
<ItemGroup>
<Compile Include="..\Common\DelaySign.cs" Link="Properties\DelaySign.cs" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\..\Standard\src\Standard.csproj" />
</ItemGroup>
<!-- Configure NuGet package metadata as well. -->
<PropertyGroup>
<RunQDocGen>True</RunQDocGen>
<Authors>Microsoft</Authors>
<Description>Microsoft's Quantum ML Libraries.</Description>
<Copyright>© Microsoft Corporation. All rights reserved.</Copyright>
<PackageId>Microsoft.Quantum.MachineLearning</PackageId>
<PackageReleaseNotes>See: https://docs.microsoft.com/en-us/quantum/relnotes/</PackageReleaseNotes>
<PackageReleaseNotes>See: https://docs.microsoft.com/quantum/relnotes/</PackageReleaseNotes>
<PackageLicenseUrl>https://github.com/Microsoft/QuantumLibraries/raw/master/LICENSE.txt</PackageLicenseUrl>
<PackageProjectUrl>https://github.com/Microsoft/QuantumLibraries/tree/master/Chemistry</PackageProjectUrl>
<PackageProjectUrl>https://github.com/Microsoft/QuantumLibraries/tree/master/MachineLearning</PackageProjectUrl>
<PackageIconUrl>https://secure.gravatar.com/avatar/bd1f02955b2853ba0a3b1cdc2434e8ec.png</PackageIconUrl>
<PackageTags>Quantum Q# Qsharp</PackageTags>
<NoWarn>1591</NoWarn>
<GenerateDocumentationFile>true</GenerateDocumentationFile>
<PublishRepositoryUrl>true</PublishRepositoryUrl>
<EmbedUntrackedSources>true</EmbedUntrackedSources>
<IncludeSymbols>true</IncludeSymbols>
@ -28,16 +38,4 @@
<AllowedOutputExtensionsInPackageBuildOutputFolder>$(AllowedOutputExtensionsInPackageBuildOutputFolder);.pdb;.xml</AllowedOutputExtensionsInPackageBuildOutputFolder>
</PropertyGroup>
<ItemGroup>
<Compile Include="..\..\Common\DelaySign.cs" Link="Properties\DelaySign.cs" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\Runtime\Runtime.csproj" PrivateAssets="all" />
<BuildOutputInPackage Include="..\Runtime\bin\$(Configuration)\netstandard2.1\Microsoft.Quantum.MachineLearning.*" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Microsoft.SourceLink.GitHub" Version="1.0.0" PrivateAssets="All" />
</ItemGroup>
</Project>

0
MachineLearning/src/Runtime/Properties/NamespaceInfo.qs → MachineLearning/src/Properties/NamespaceInfo.qs
Просмотреть файл

24
MachineLearning/src/Runtime/RotationSequences.qs → MachineLearning/src/RotationSequences.qs
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@ -7,12 +7,12 @@ namespace Microsoft.Quantum.MachineLearning {
open Microsoft.Quantum.Intrinsic;
open Microsoft.Quantum.Canon;
/// What is the minimum number of qubits
/// to support the subject gate sequence?
/// Find the maximum qubit index m occuring
/// in a gate sequence and return m+1
function NQubitsRequired(seq : GateSequence) : Int {
mutable nQubitsRequired = 0;
/// What is the minimum number of qubits
/// to support the subject gate sequence?
/// Find the maximum qubit index m occuring
/// in a gate sequence and return m+1
function NQubitsRequired(seq : GateSequence) : Int {
mutable nQubitsRequired = 0;
for (gate in seq!) {
set nQubitsRequired = Fold(
MaxI, 0,
@ -23,11 +23,11 @@ namespace Microsoft.Quantum.MachineLearning {
);
}
return nQubitsRequired;
}
}
/// Apply parameterized gate sequence to subject qubit register
///
operation _ApplyGates(parameters : Double[], gates: GateSequence, qubits : Qubit[]) : (Unit) is Adj + Ctl {
/// Apply parameterized gate sequence to subject qubit register
///
operation _ApplyGates(parameters : Double[], gates: GateSequence, qubits : Qubit[]) : (Unit) is Adj + Ctl {
//dumpRegisterToConsole(qubits);
for (gate in gates!) {
// let (gsp,p,ix) = gt!;
@ -44,8 +44,8 @@ namespace Microsoft.Quantum.MachineLearning {
}
}
operation ApplyGates(parameters : Double[], gates: GateSequence): (Qubit[] => Unit is Adj + Ctl) {
operation ApplyGates(parameters : Double[], gates: GateSequence): (Qubit[] => Unit is Adj + Ctl) {
return _ApplyGates(parameters,gates,_);
}
}
}

63
MachineLearning/src/Runtime/Convert.qs
Просмотреть файл

@ -1,63 +0,0 @@
namespace Microsoft.Quantum.MachineLearning {
open Microsoft.Quantum.Intrinsic;
open Microsoft.Quantum.Canon;
open Microsoft.Quantum.Math;
function unFlattenSchedule(sc : Int[][]) : SamplingSchedule
{
mutable ret = new Range[0];
for (flattenedRange in sc) {
set ret += [flattenedRange[0]..flattenedRange[1]..flattenedRange[2]];
}
return SamplingSchedule(ret);
}
function unFlattenLabeledSamples(dat:Double[][], labs:Int[]) : LabeledSample[] {
mutable cnt = MinI(Length(dat), Length(labs));
mutable ret = new LabeledSample[cnt];
for (j in 0..(cnt - 1)) {
set ret w/= j <- LabeledSample(dat[j], labs[j]);
}
return ret;
}
/// Debugging prop
operation unFlattenPauli(p:Int): Pauli
{
if (p==1)
{
return PauliX;
}
if (p==2)
{
return PauliY;
}
if (p==3)
{
return PauliZ;
}
return PauliI;
}
/// Debugging prop
/// upcasting controlled rotation in flat representation (paramIx,pauliIx,gateSpan)
operation unFlattenControlledRotation(cod:Int[]): ControlledRotation {
return ControlledRotation(
GateSpan(
cod[2], cod[3...]
),
unFlattenPauli(cod[1]),
cod[0]
);
}
/// Debugging prop
operation unFlattenGateSequence(seq: Int[][]) : GateSequence {
mutable tmp = new ControlledRotation[Length(seq)];
for (icr in 0..(Length(seq) - 1)) {
set tmp w/= icr <- unFlattenControlledRotation(seq[icr]);
}
return GateSequence(tmp);
}
}

18
MachineLearning/src/Runtime/Runtime.csproj
Просмотреть файл

@ -1,18 +0,0 @@
<Project Sdk="Microsoft.Quantum.Sdk/0.10.1912.2605-alpha">
<PropertyGroup>
<TargetFramework>netstandard2.1</TargetFramework>
<PlatformTarget>x64</PlatformTarget>
<AssemblyName>Microsoft.Quantum.MachineLearning.Runtime</AssemblyName>
<QsharpDocsGeneration>true</QsharpDocsGeneration>
<!--
Include core packages using project references instead, since they get
built at the same time as this project.
-->
<IncludeQsharpCorePackages>false</IncludeQsharpCorePackages>
</PropertyGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\Standard\src\Standard.csproj" />
</ItemGroup>
</Project>

75
MachineLearning/src/Structure.qs Normal file
Просмотреть файл

@ -0,0 +1,75 @@
// Copyright (c) Microsoft Corporation.
// Licensed under the MIT License.
namespace Microsoft.Quantum.MachineLearning {
open Microsoft.Quantum.Arrays;
function _UncontrolledSpanSequence(idxsQubits : Int[]) : GateSpan[] {
return Mapped(
GateSpan(_, new Int[0]),
idxsQubits
);
}
function _CallFlipped<'TInput1, 'TInput2, 'TOutput>(
fn : (('TInput1, 'TInput2) -> 'TOutput),
y : 'TInput2, x : 'TInput1
) : 'TOutput {
return fn(x, y);
}
function _Flipped<'TInput1, 'TInput2, 'TOutput>(
fn : (('TInput1, 'TInput2) -> 'TOutput)
) : (('TInput2, 'TInput1) -> 'TOutput) {
return _CallFlipped(fn, _, _);
}
function LocalRotationsLayer(nQubits : Int, axis : Pauli) : GateSequence {
// [parameterIndex, pauliCode, targetQubit\,sequence of control qubits\]
return GateSequence(Mapped(
_Flipped(ControlledRotation(_, axis, _)),
Enumerated(
_UncontrolledSpanSequence(SequenceI(0, nQubits - 1))
)
));
}
function PartialRotationsLayer(idxsQubits : Int[], axis : Pauli) : GateSequence {
// [parameterIndex, pauliCode, targetQubit\,sequence of control qubits\]
return GateSequence(Mapped(
_Flipped(ControlledRotation(_, axis, _)),
Enumerated(
_UncontrolledSpanSequence(idxsQubits)
)
));
}
function CyclicEntanglingLayer(nQubits : Int, axis : Pauli, stride : Int) : GateSequence {
mutable rotations = new ControlledRotation[0];
for (idxTarget in 0..nQubits - 1) {
set rotations += [ControlledRotation(
GateSpan(
idxTarget,
[(idxTarget + stride) % nQubits]
),
axis, idxTarget
)];
}
return GateSequence(rotations);
}
function CombinedGateSequence(layers : GateSequence[]) : GateSequence {
mutable combined = (Head(layers))!;
mutable offset = Length(combined);
for (layer in Rest(layers)) {
for (gate in layer!) {
set combined += [gate w/ Index <- gate::Index + offset];
}
set offset += Length(layer!);
}
return GateSequence(combined);
}
}

2
MachineLearning/src/Runtime/Training.qs → MachineLearning/src/Training.qs
Просмотреть файл

@ -129,7 +129,7 @@ namespace Microsoft.Quantum.MachineLearning {
}
let stateGenerator = StateGenerator(
nQubits,
NoisyInputEncoder(effectiveTolerance, sample::Features)
ApproximateInputEncoder(effectiveTolerance, sample::Features)
);
let grad = EstimateGradient(
gates, param, stateGenerator,

0
MachineLearning/src/Runtime/Types.qs → MachineLearning/src/Types.qs
Просмотреть файл

4
MachineLearning/src/Runtime/Utils.qs → MachineLearning/src/Utils.qs
Просмотреть файл

@ -5,9 +5,9 @@ namespace Microsoft.Quantum.MachineLearning {
open Microsoft.Quantum.Canon;
open Microsoft.Quantum.Math;
function _AllNearlyEqualD(v1: Double[], v2: Double[]):Bool {
function _AllNearlyEqualD(v1: Double[], v2: Double[]):Bool {
return Length(v1) == Length(v2) and All(NearlyEqualD, Zip(v1, v2));
}
}
operation _TailMeasurement(nQubits : Int) : (Qubit[] => Result) {
let paulis = ConstantArray(nQubits, PauliI) w/ (nQubits - 1) <- PauliZ;

0
MachineLearning/src/Runtime/Validation.qs → MachineLearning/src/Validation.qs
Просмотреть файл

3
MachineLearning/tests/MachineLearningTests.csproj
Просмотреть файл

@ -14,7 +14,6 @@
<ItemGroup>
<ProjectReference Include="..\..\Standard\src\Standard.csproj" />
<ProjectReference Include="..\src\Runtime\Runtime.csproj" />
<ProjectReference Include="..\src\DataModel\DataModel.csproj" />
<ProjectReference Include="..\src\MachineLearning.csproj" />
</ItemGroup>
</Project>