From 91294a66889b8b2efde68f230954f6a4c30a93dc Mon Sep 17 00:00:00 2001 From: Steinkirch <1130416+bt3gl@users.noreply.github.com> Date: Tue, 31 Mar 2020 19:24:18 -0700 Subject: [PATCH] Update README.md --- README.md | 38 ++++++++++++++++++++++++++++++-------- 1 file changed, 30 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 5ff2528..c738abc 100644 --- a/README.md +++ b/README.md @@ -18,22 +18,44 @@ * TFQ allows researchers to construct quantum datasets, quantum models, and classical control parameters as tensors in a single computational graph. The outcome of quantum measurements, leading to classical probabilistic events, is obtained by TensorFlow Ops. Training can be done using standard Keras functions. +* A key feature of TensorFlow Quantum is the ability to simultaneously train and execute many quantum circuits. This is achieved by TensorFlow’s ability to parallelize computation across a cluster of computers, and the ability to simulate relatively large quantum circuits on multi-core computers. + --- -### Steps: +## Steps: -- Prepare a quantum dataset - Quantum data is loaded as tensors (a multi-dimensional array of numbers). Each quantum data tensor is specified as a quantum circuit written in Cirq that generates quantum data on the fly. The tensor is executed by TensorFlow on the quantum computer to generate a quantum dataset. +### Prepare a quantum dataset -- Evaluate a quantum neural network model - The researcher can prototype a quantum neural network using Cirq that they will later embed inside of a TensorFlow compute graph. Parameterized quantum models can be selected from several broad categories based on knowledge of the quantum data's structure. The goal of the model is to perform quantum processing in order to extract information hidden in a typically entangled state. In other words, the quantum model essentially disentangles the input quantum data, leaving the hidden information encoded in classical correlations, thus making it accessible to local measurements and classical post-processing. +- Quantum data is loaded as tensors (a multi-dimensional array of numbers). +- Each quantum data tensor is specified as a quantum circuit written in Cirq that generates quantum data on the fly. +- The tensor is executed by TensorFlow on the quantum computer to generate a quantum dataset. -- Sample or Average - Measurement of quantum states extracts classical information in the form of samples from a classical random variable. The distribution of values from this random variable generally depends on the quantum state itself and on the measured observable. As many variational algorithms depend on mean values of measurements, also known as expectation values, TFQ provides methods for averaging over several runs involving steps (1) and (2). +### Evaluate a quantum neural network model -- Evaluate a classical neural networks model - Once classical information has been extracted, it is in a format amenable to further classical post-processing. As the extracted information may still be encoded in classical correlations between measured expectations, classical deep neural networks can be applied to distill such correlations. +- The researcher can prototype a quantum neural network using Cirq that they will later embed inside of a TensorFlow compute graph. +- Parameterized quantum models can be selected from several broad categories based on knowledge of the quantum data's structure. +- The goal of the model is to perform quantum processing in order to extract information hidden in a typically entangled state. -- Evaluate Cost Function - Given the results of classical post-processing, a cost function is evaluated. This could be based on how accurately the model performs the classification task if the quantum data was labeled, or other criteria if the task is unsupervised. +### Sample or Average -- Evaluate Gradients & Update Parameters - After evaluating the cost function, the free parameters in the pipeline should be updated in a direction expected to decrease the cost. This is most commonly performed via gradient descent. +- Measurement of quantum states extracts classical information in the form of samples from a classical random variable. +- The distribution of values from this random variable generally depends on the quantum state itself and on the measured observable. +- As many variational algorithms depend on mean values of measurements, also known as expectation values, TFQ provides methods for averaging over several runs involving steps (1) and (2). + +### Evaluate a classical neural networks model + +- Once classical information has been extracted, it is in a format amenable to further classical post-processing. +- As the extracted information may still be encoded in classical correlations between measured expectations, classical deep neural networks can be applied to distill such correlations. + +### Evaluate Cost Function + +- Given the results of classical post-processing, a cost function is evaluated. +- This could be based on how accurately the model performs the classification task if the quantum data was labeled, or other criteria if the task is unsupervised. + +### Evaluate Gradients & Update Parameters + +- After evaluating the cost function, the free parameters in the pipeline should be updated in a direction expected to decrease the cost. +- This is most commonly performed via gradient descent. -- A key feature of TensorFlow Quantum is the ability to simultaneously train and execute many quantum circuits. This is achieved by TensorFlow’s ability to parallelize computation across a cluster of computers, and the ability to simulate relatively large quantum circuits on multi-core computers. -----