searchindex.js

Search.setIndex({"alltitles": {"API Reference": [[8, null]], "Acknowledgements": [[6, "acknowledgements"]], "Assessing the QNN on IBM Quantum Using the Sampler Primitive": [[3, "Assessing-the-QNN-on-IBM-Quantum-Using-the-Sampler-Primitive"]], "Attributes:": [[9, "attributes"], [32, "attributes"], [33, "attributes"], [42, "attributes"], [44, "attributes"], [45, "attributes"], [46, "attributes"], [47, "attributes"], [48, "attributes"], [49, "attributes"], [50, "attributes"], [51, "attributes"], [70, "attributes"], [71, "attributes"], [72, "attributes"], [92, "attributes"], [93, "attributes"]], "Automatic backend selection (IBM Quantum only)": [[95, "automatic-backend-selection-ibm-quantum-only"]], "Base Classes": [[8, "base-classes"]], "Bayesian Optimization using a Quantum Gaussian Process Surrogate Model": [[4, null]], "Bleeding-edge version": [[7, "bleeding-edge-version"]], "Circuit Design": [[8, "circuit-design"]], "Cite sQUlearn": [[6, "cite-squlearn"]], "Classification": [[0, 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notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.ChebyshevTower", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.EncodingCircuitDerivatives", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.HighDimEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.HubregtsenEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.KyriienkoEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.LayeredEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.MultiControlEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.ParamZFeatureMap", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.PrunedEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.QCNNEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.QiskitEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.RandomEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.RandomLayeredEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.TranspiledEncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.YZ_CX_EncodingCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.automated_pruning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit.encoding_circuit_base</span></code>.EncodingCircuitBase", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.encoding_circuit</span></code>.pruning_from_QFI", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel</span></code>.QGPC", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel</span></code>.QGPR", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel</span></code>.QKODE", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel</span></code>.QKRR", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel</span></code>.QSVC", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel</span></code>.QSVR", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel.loss</span></code>.ODELoss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel.loss.kernel_loss_base</span></code>.KernelLossBase", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel.loss.negative_log_likelihood</span></code>.NLL", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel.loss.target_alignment</span></code>.TargetAlignment", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel.lowlevel_kernel</span></code>.FidelityKernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel.lowlevel_kernel</span></code>.KernelOptimizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel.lowlevel_kernel</span></code>.ProjectedQuantumKernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.kernel.lowlevel_kernel.kernel_matrix_base</span></code>.KernelMatrixBase", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.observables</span></code>.CustomObservable", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.observables</span></code>.IsingHamiltonian", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.observables</span></code>.SinglePauli", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.observables</span></code>.SingleProbability", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.observables</span></code>.SummedPaulis", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.observables</span></code>.SummedProbabilities", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.observables.observable_base</span></code>.ObservableBase", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.observables.observable_derivatives</span></code>.ObservableDerivatives", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.optimizers</span></code>.Adam", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.optimizers</span></code>.LBFGSB", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.optimizers</span></code>.SGLBO", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.optimizers</span></code>.SLSQP", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.optimizers</span></code>.SPSA", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.optimizers.optimizer_base</span></code>.OptimizerBase", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn</span></code>.QNNClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn</span></code>.QNNRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.base_qnn</span></code>.BaseQNN", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.loss</span></code>.CrossEntropyLoss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.loss</span></code>.MeanSquaredError", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.loss</span></code>.ODELoss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.loss</span></code>.ParameterRegularizationLoss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.loss</span></code>.SquaredLoss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.loss</span></code>.VarianceLoss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.loss.qnn_loss_base</span></code>.QNNLossBase", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.lowlevel_qnn</span></code>.LowLevelQNN", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.lowlevel_qnn.lowlevel_qnn_base</span></code>.LowLevelQNNBase", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.lowlevel_qnn.lowlevel_qnn_pennylane</span></code>.LowLevelQNNPennyLane", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.lowlevel_qnn.lowlevel_qnn_qiskit</span></code>.LowLevelQNNQiskit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.lowlevel_qnn.lowlevel_qnn_qulacs</span></code>.LowLevelQNNQulacs", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.util</span></code>.ShotsFromRSTD", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.util</span></code>.get_lr_decay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.util</span></code>.get_variance_fac", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.util.training</span></code>.train", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qnn.util.training</span></code>.train_mini_batch", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qrc</span></code>.QRCClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qrc</span></code>.QRCRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.qrc.base_qrc</span></code>.BaseQRC", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util</span></code>.OpTree", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.executor</span></code>.ExecutorEstimator", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.executor</span></code>.ExecutorSampler", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.optree</span></code>.OpTreeCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.optree</span></code>.OpTreeContainer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.optree</span></code>.OpTreeExpectationValue", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.optree</span></code>.OpTreeList", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.optree</span></code>.OpTreeMeasuredOperator", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.optree</span></code>.OpTreeOperator", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.optree</span></code>.OpTreeSum", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.optree</span></code>.OpTreeValue", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.pennylane</span></code>.PennyLaneCircuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">squlearn.util.qulacs</span></code>.QulacsCircuit", "Quantum Encoding Circuits", "The Executor Class", "Quantum Kernel Methods", "Observables for expectation values", "Quantum Neural Networks", "Quantum Reservoir Computing", "User Guide"], "titleterms": {"In": 95, "The": [0, 95], "acknowledg": 6, "adam": [52, 98], "adjust": 98, "analysi": 1, "ansatz": 2, "api": 8, "ar": [13, 42, 51], "assess": 3, "attribut": [9, 32, 33, 42, 44, 45, 46, 47, 48, 49, 50, 51, 70, 71, 72, 92, 93], "automat": 95, "automated_prun": 27, "backend": [3, 95], "base": 8, "base_qnn": 60, "base_qrc": 80, "baseqnn": 60, "baseqrc": 80, "batch": 98, "bayesian": 4, "bleed": 7, "chebyshevpqc": 10, "chebyshevrx": 11, "chebyshevtow": 12, "circuit": [8, 94], "cite": 6, "class": [8, 95], "classif": [0, 96, 99], "classifi": 8, "combin": 94, "comput": 99, "configur": 3, "contact": 6, "content": 6, "contribut": 6, "core": 8, "creat": 94, "crossentropyloss": 61, "custom": [94, 97], "customobserv": 44, "data": [0, 1, 8], "decoupl": 3, "definit": 0, "depend": 7, "deriv": 97, "descent": 98, "design": 8, "develop": 7, "devic": 3, "differ": 94, "differenti": 2, "digit": 0, "dimension": 0, "disclaim": 6, "document": 6, "dotproduct": 42, "dynam": [3, 98], "edg": 7, "emploi": 96, "encod": [8, 94], "encoding_circuit": [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], "encoding_circuit_bas": 28, "encodingcircuitbas": 28, "encodingcircuitderiv": [13, 94], "equat": 2, "error": 3, "evalu": [3, 96], "exampl": [5, 6], "execut": 8, "executor": [9, 82, 83, 95], "executorestim": 82, "executorsampl": 83, "expec": [], "expect": 97, "expsinesquar": 42, "extrapol": 3, "factor": 98, "featur": 95, "fidel": 96, "fidelitykernel": [40, 96], "first": 2, "follow": 42, "fqk": 96, "free": 3, "gate": 17, "gaussian": [4, 42], "gener": 1, "get_deriv": [13, 51], "get_lr_decai": 74, "get_variance_fac": 75, "gradient": 98, "grid": 1, "guid": 100, "handwritten": 0, "high": [8, 96, 98, 99], "highdimencodingcircuit": 14, "hubregtsenencodingcircuit": 15, "hyperparamet": 1, "ibm": [3, 95], "implement": [8, 42, 94, 97], "import": [0, 1, 3], "initi": 95, "instal": [6, 7], "interfac": 8, "introduct": [], "isinghamiltonian": 45, "kei": 95, "kernel": [0, 1, 8, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 96], "kernel_loss_bas": 37, "kernel_matrix_bas": 43, "kernel_optim": [], "kernel_optimization_bas": [], "kernellossbas": 37, "kernelmatrixbas": 43, "kerneloptim": 41, "kerneloptimizerbas": [], "kyriienkoencodingcircuit": 16, "layeredencodingcircuit": [17, 94], "lbfgsb": 53, "learn": 98, "level": [8, 96, 98, 99], "librari": [1, 3], "licens": 6, "literatur": [], "loss": [36, 37, 38, 39, 61, 62, 63, 64, 65, 66, 67], "lossbas": [], "lowlevel_kernel": [40, 41, 42, 43], "lowlevel_qnn": [68, 69, 70, 71, 72], "lowlevel_qnn_bas": 69, "lowlevel_qnn_pennylan": 70, "lowlevel_qnn_qiskit": 71, "lowlevel_qnn_qulac": 72, "lowlevelqnn": 68, "lowlevelqnnbas": 69, "lowlevelqnnpennylan": 70, "lowlevelqnnqiskit": 71, "lowlevelqnnqulac": 72, "m3": [], "machin": 0, "manag": 95, "map": 97, "matern": 42, "matrix": [], "max": 0, "meansquarederror": 62, "method": [0, 2, 9, 13, 30, 31, 32, 33, 34, 35, 36, 38, 39, 42, 44, 45, 46, 47, 48, 49, 50, 51, 58, 59, 70, 71, 72, 78, 79, 92, 93, 96, 98, 99], "min": 0, "mini": 98, "mitig": 3, "ml": [], "model": 4, "multicontrolencodingcircuit": 18, "negative_log_likelihood": 38, "network": [3, 98], "neural": [3, 98], "nll": 38, "nois": 3, "observ": [44, 45, 46, 47, 48, 49, 50, 51, 97], "observable_bas": 50, "observable_deriv": 51, "observablebas": 50, "observablederiv": 51, "od": [2, 8], "odeloss": [36, 63], "onli": 95, "oper": 8, "optim": [1, 4, 8, 52, 53, 54, 55, 56, 57, 98], "optimizer_bas": 57, "optimizerbas": 57, "option": [7, 95], "optre": [8, 81, 84, 85, 86, 87, 88, 89, 90, 91], "optreecircuit": 84, "optreecontain": 85, "optreeexpectationvalu": 86, "optreelist": 87, "optreemeasuredoper": 88, "optreeoper": 89, "optreesum": 90, "optreevalu": 91, "order": 2, "ordinari": 2, "outer": 42, "overview": [3, 95], "pairwisekernel": 42, "parallel": 95, "parameterregularizationloss": 64, "paramzfeaturemap": 19, "pennylan": [8, 92], "pennylanecircuit": 92, "pipelin": 1, "pqk": 96, "preprocess": 0, "prerequisit": [6, 7], "primit": [3, 95], "process": 4, "project": [0, 96], "projectedquantumkernel": [42, 96], "prune": 94, "prunedencodingcircuit": 20, "pruning_from_qfi": 29, "qcnnencodingcircuit": 21, "qgpc": 30, "qgpr": 31, "qiskit": [94, 95], "qiskitencodingcircuit": 22, "qk": 2, "qkode": 32, "qkrr": 33, "qml": [2, 8], "qnn": [2, 3, 8, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 98], "qnn_loss_bas": 67, "qnnclassifi": 58, "qnnlossbas": 67, "qnnregressor": 59, "qpu": 95, "qrc": [78, 79, 80, 99], "qrcclassifi": 78, "qrcregressor": 79, "qsvc": 34, "qsvr": 35, "quantum": [0, 3, 4, 8, 94, 95, 96, 98, 99], "qubit": [17, 97], "qulac": [8, 93], "qulacscircuit": 93, "randomencodingcircuit": 23, "randomlayeredencodingcircuit": 24, "rate": 98, "rationalquadrat": 42, "real": 97, "recogn": [13, 51], "recognit": 0, "reduct": [0, 98], "refer": [2, 8], "regress": [96, 99], "regressor": 8, "regular": 98, "releas": [6, 7], "represent": 17, "reservoir": 99, "result": [1, 99], "retrain": [], "routin": 95, "run": 1, "sampler": 3, "scale": 0, "schedul": 98, "search": 1, "select": 95, "session": 95, "set": [0, 1, 3, 95], "setup": 99, "sglbo": 54, "shot": 98, "shotsfromrstd": 73, "simul": 3, "singl": 17, "singlepauli": 46, "singleprob": 47, "slsqp": [55, 98], "solv": 2, "solver": 8, "split": 0, "spsa": 56, "squaredloss": 65, "squlearn": [6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "stabl": [6, 7], "string": [13, 17, 51], "structur": 8, "summari": 1, "summedpauli": 48, "summedprob": 49, "support": 0, "surrog": 4, "target_align": 39, "targetalign": 39, "tool": 8, "train": [3, 76, 77, 96, 99], "train_mini_batch": 77, "transpil": 94, "transpiledencodingcircuit": [25, 94], "trex": 3, "two": 17, "up": [1, 3], "us": [2, 3, 4], "user": 100, "util": [73, 74, 75, 76, 77, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95], "valu": 97, "varianc": 98, "varianceloss": 66, "vector": 0, "version": 7, "via": [94, 96], "welcom": 6, "without": 3, "wrap": 94, "your": 94, "yz_cx_encodingcircuit": 26, "zero": 3, "zne": 3}})