John Martinis, Former Head of Google’s Quantum Initiative and Tony Uttley, President of Honeywell Quantum Solutions discuss where the industry is heading

Quantum pioneers say it’s a great time to go quantum. (Credit: Honeywell)

Quantum computing pioneer John Martinis may have moved on from Google’s quantum computing project, but he hasn’t moved on from his belief that quantum computers will take on real world challenges.

Martinis quantum
Credit: Matt Perko/UCSB

The University of California Santa Barbara physicist told the participants of the BCI Summit, a virtual conference on the latest advances in quantum computing held yesterday, that, while challenges remain, he was optimistic.

“This is an exciting time for many groups who are working on quantum computing,” said Martinis. “We are now building machines and building algorithms. Of course, having real applications is difficult because classical computers are so good. It’s a very healthy time right now.”


What’s After Quantum Supremacy?

Martinis was one of the researchers that spearheaded work that led to Google’s quantum supremacy announcement, alluding to this being helpful in getting Google executives belief and buy-in for further development. He added that the team has studies currently waiting to be published that may demonstrate that quantum computers can solve real-world problems.

‘Since then we have submitted two new papers in review using the sycamore chip where they are looking to solve real world problems,” said Martinis. “These are significant improvements of what anyone has been doing. One of the papers has to deal with optimization, second one has to do with quantum chemistry. The important result here is that even though we used a NISQ-era computer, we used techniques that improved the fidelity of the operations by more than a factor of 100. The paper will be coming out shortly.”

Error correction remains an area of considerable interest for the researcher.

“I am trying to work on a timeline of building a big error corrected machine,” said Martinis. “There are a lot of unknowns there. The machines have low enough errors that you will be able to do some significantly good science. When it becomes useful may take 10 years or more. If we’re lucky it could be 3-5 years. The quantum supremacy announcement represented a good milestone to get to the error corrected machine. We just need to get in the lab and get all that to work.”

Martinis has been a professor at UC Santa Barbara since 2004 and joined Google as a research scientist at Google in 2014. He recently announced that he resigned from Google’s quantum computing project.


Honeywell in The Emergent Era

Tony Uttley, president of Honeywell Quantum Solutions, who also spoke at the event, agreed with Martinis’ optimism and said the quantum era has begun.

“Do Quantum Computers work? Yes,” said Uttley. “We have gone past this stage where they did not exist. We are in the emergent era where we have crossed the divide from quantum computers not existing to existing.”

Honeywell, a dark horse that has emerged in the quantum computing race, recently announced its advances in creating a trapped ion-based quantum computer, one that is rumored to rival any current quantum device. The company remain confident on officially launching this in the coming months. Watch this space.

The summit also included Thierry L. Kahane, AI & Analytics practice leader, North America Fujitsu America and Bill Reichert, co-founder, MDGarage Tech Ventures and partner, Pegasus Tech Ventures.

The BCI network comprises top tier banks, active venture investors, Fortune 100s, leading corporations and government entities.

Quantum Workshop – 2 (4/12/2020)


上次讨论会我们聊了量子计算的硬件的介绍,这次我们来聊一聊量子计算几个基本的算法:主要从量子傅里叶变换和Grover’s search两个算法来展开。大家如果有什么想要聊的其他话题,或者是想要跟大家分享的内容,也欢迎在这里提出来。考虑到特殊时期,我们这一次会在网上进行。


会议号 306 887 583



+8675536550000 (中国大陆)

+85230018898 (中国香港)

开始时间 2020年04月12日 14:00 EST


话题按知识进阶顺序排序 同一个话题下阅读材料按详细程度由简到繁排序


1. unitary operations

2. quantum logic gates

– Nielsen & Chuang Ch. 1.3

3. universal quantum gates

– classical universal gates

– Nielsen & Chuang Ch. 4.5

4. Quantum Fourier Transform (QFT)

– Nielsen & Chuang Ch. 5.1

5. Grover’s Search

– Nielsen & Chuang Ch. 6.1


1. Clifford group & Gottesman – Knill theorem[paper].pdf

2. Stabilizer Formalism

3. Classical simulation of Clifford (+T) circuits

4. Magic state distillation –

5. Quantum Amplitude Estimation

Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning

“Nature isn’t classical, damnit, so if you want to make a simulation of nature, you’d better make it quantum mechanical.” — Physicist Richard Feynman

Machine learning (ML), while it doesn’t exactly simulate systems in nature, has the ability to learn a model of a system and predict the system’s behavior. Over the past few years, classical ML models have shown promise in tackling challenging scientific issues, leading to advancements in image processing for cancer detectionforecasting earthquake aftershockspredicting extreme weather patterns, and detecting new exoplanets. With the recent progress in the development of quantum computing, the development of new quantum ML models could have a profound impact on the world’s biggest problems, leading to breakthroughs in the areas of medicine, materials, sensing, and communications. However, to date there has been a lack of research tools to discover useful quantum ML models that can process quantum data and execute on quantum computers available today.

Today, in collaboration with the University of WaterlooX, and Volkswagen, we announce the release of TensorFlow Quantum (TFQ), an open-source library for the rapid prototyping of quantum ML models. TFQ provides the tools necessary for bringing the quantum computing and machine learning research communities together to control and model natural or artificial quantum systems; e.g. Noisy Intermediate Scale Quantum (NISQ) processors with ~50 – 100 qubits.

Under the hood, TFQ integrates Cirq with TensorFlow, and offers high-level abstractions for the design and implementation of both discriminative and generative quantum-classical models by providing quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators.

What is a Quantum ML Model?
A quantum model has the ability to represent and generalize data with a quantum mechanical origin. However, to understand quantum models, two concepts must be introduced – quantum data and hybrid quantum-classical models.

Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. Quantum data, which can be generated / simulated on quantum processors / sensors / networks include the simulation of chemicals and quantum matterquantum controlquantum communication networksquantum metrology, and much more.

A technical, but key, insight is that quantum data generated by NISQ processors are noisy and are typically entangled just before the measurement occurs. However, applying quantum machine learning to noisy entangled quantum data can maximize extraction of useful classical information. Inspired by these techniques, the TFQ library provides primitives for the development of models that disentangle and generalize correlations in quantum data, opening up opportunities to improve existing quantum algorithms or discover new quantum algorithms.

The second concept to introduce is hybrid quantum-classical models. Because near-term quantum processors are still fairly small and noisy, quantum models cannot use quantum processors alone — NISQ processors will need to work in concert with classical processors to become effective. As TensorFlow already supports heterogeneous computing across CPUs, GPUs, and TPUs, it is a natural platform for experimenting with hybrid quantum-classical algorithms.

TFQ contains the basic structures, such as qubits, gates, circuits, and measurement operators that are required for specifying quantum computations. User-specified quantum computations can then be executed in simulation or on real hardware. Cirq also contains substantial machinery that helps users design efficient algorithms for NISQ machines, such as compilers and schedulers, and enables the implementation of hybrid quantum-classical algorithms to run on quantum circuit simulators, and eventually on quantum processors.

We’ve used TensorFlow Quantum for hybrid quantum-classical convolutional neural networks, machine learning for quantum control, layer-wise learning for quantum neural networks, quantum dynamics learning, generative modeling of mixed quantum states, and learning to learn with quantum neural networks via classical recurrent neural networks. We provide a review of these quantum applications in the TFQ white paper; each example can be run in-browser via Colab from our research repository.

How TFQ works
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.

To provide some intuition on how to use quantum data, one may consider a supervised classification of quantum statesusing a quantum neural network. Just like classical ML, a key challenge of quantum ML is to classify “noisy data”. To build and train such a model, the researcher can do the following:

  1. 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.
  2. 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.
  3. 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).
  4. 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.
  5. 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.
  6. 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 high-level abstract overview of the computational steps involved in the end-to-end pipeline for inference and training of a hybrid quantum-classical discriminative model for quantum data in TFQ. To see the code for an end-to-end example, please check the “Hello Many-Worlds” example, the quantum convolutional neural networks tutorial, and our guide.

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. To achieve the latter, we are also announcing the release of qsim (github link), a new high performance open source quantum circuit simulator, which has demonstrated the ability to simulate a 32 qubit quantum circuit with a gate depth of 14 in 111 seconds on a single Google Cloud node (n1-ultramem-160) (see this paper for details). The simulator is particularly optimized for multi-core Intel processors. Combined with TFQ, we have demonstrated 1 million circuit simulations for 20 qubit quantum circuit at a gate depth of 20 in 60 minutes on a Google Cloud node (n2-highcpu-80). See the TFQ white paper, Section II E on the Quantum Circuit Simulation with qsim for more information.

Looking Forward
Today, TensorFlow Quantum is primarily geared towards executing quantum circuits on classical quantum circuit simulators. In the future, TFQ will be able to execute quantum circuits on actual quantum processors that are supported by Cirq, including Google’s own processor Sycamore.

To learn more about TFQ, please read our white paper and visit the TensorFlow Quantum website. We believe that bridging the ML and Quantum communities will lead to exciting new discoveries across the board and accelerate the discovery of new quantum algorithms to solve the world’s most challenging problems.

This open source project is led by the Google AI Quantum team, and was co-developed by the University of Waterloo, Alphabet’s X, and Volkswagen. A special thanks to the University of Waterloo, whose students made major contributions to this open source software through multiple internship projects at the Google AI Quantum lab.