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This article was published as a part of the Data Science Blogathon.

How did you find this blog? You typed some keywords related to data science in your browser.  you arch engines. The search engines we are using today can handle billions of data flow within milliseconds. So what about a new search engine that can beat the current search engines in speed and semantic accuracy? The whole natural language processing and machine learning communities are discussing the possibilities of such search engines, which they named vector search engines. One of the most using vector search engines nowadays is Weaviate. In this blog, I will introduce the vector search engine – Weaviate to you. In this blog, we are going to cover these topics.

                                  • Sample queries

Introduction to Weaviate

Weaviate is a vector database and search engine. It is a low-latency vector search engine that supports various media types(text, images, etc.). Weaviate uses machine learning to vectorize and store data and find responses to natural language questions. It includes semantic search, Question-Answer Extraction, Classification, and Customizable Models (PyTorch/TensorFlow/Keras). You may also use Weaviate to scale up your custom machine learning models in production.

Weaviate stores both media (text, images. etc.)objects and their corresponding vectors, allowing for combining vector search with structured filtering with the fault-tolerance of a cloud-native database. Weaviate search can perform through different methods such as GraphQL, REST, and various language clients. Python, Javascript, Go, and Go are popular programming languages that support Weaviate clients.

Nowadays, Weaviate is used by software engineers as an ML-first database for their applications, Data engineers to use a vector database built up from the ground with ANN at its core, Data scientists to deploy their search applications with MLOps.

Some major features of Weaviate are

Fast queries – In less than 100 milliseconds, Weaviate runs a 10 closest neighbour (NN) search over millions of items.

Different media support – Use State-of-the-Art AI model inference (e.g. Transformers) for Images, Text, etc.

Combining scalar and vector search – Weaviate saves both your objects and your vectors, ensuring that retrieval is always quick. A third-party object storage system is not required.

Horizontal scalability– Weaviate can scale horizontally in production depending on the use case.

Graph-like connections – Make graph-like connections between data items to mimic real-life connections between the data points. GraphQL is used to traverse those connections.

Why a vector search engine?

Consider the following data object.


The above data object in the traditional search engine may store as an inverted search index. So for retrieving the data, we need to search with keywords such as “Charminar” or “ monument“, etc., to find it. But what if you have a lot of data and you’re looking for a document about the Charminar, but you’re looking for “landmarks in Telangana” instead? Traditional search engines cannot assist you in this situation, which is where vector search engines come in. To represent the data, Weaviate relies on vector indexing methods. The above-mentioned data object is vectorized in a vector space near the text “landmarks in Telangana” by the vectorization modules. Weaviate can’t produce a perfect match, but it can make a pretty good one to show you the results.

Like it vectorized the text, weaviate can vectorize any media such as image, video..etc., and perform the search. For the same reason that you use a standard search engine for your machine learning, you should use a vector search engine. It may be a strong production environment because it allows you to grow quickly, search, and classify in real-time.

How does Weaviate work? 

Weaviate is a persistent and fault-tolerant database. Internally, each class in Weaviate’s user-defined schema results in developing an index. A wrapper type that consists of one or more shards is called an index, and shards are self-contained storage units within an index. Multiple shards can be utilized to automatically divide load among multiple server nodes, acting as a load balancer. Each shard house consists of three main components object store, Inverted index and vector index store. 

Important: Object/Inverted Storage employs a segmentation-based LSM technique. The Vector Index, on the other hand, is unaffected by segmentation and is independent of those object storage.

HNSW vector index storage

Hierarchical Navigable Small-World graph, or HNSW for short, is one of the faster approximate nearest neighbour search algorithms widely used in data science applications. HNSW is the first vector index type supported by Weaviate act as a multilayered graph.  In addition to the hierarchical stores stated above. On the other hand, the vector store is unconcerned about the object storage’s internals, and as a result, it doesn’t have any segmentation issues. Weaviate can ensure that each shard is a fully self-contained unit that can serve requests for the data it holds by grouping a vector index with object storage within a shard. Weaviate can avoid the drawbacks of a segmented vector index by locating the Vector index next to (rather than within) the object-store.

Every object in the database is collected (layer 0 in the picture). These data elements are inextricably linked. There are fewer data points represented on each layer above the lowest layer, and these data points correspond to lower layers. However, the number of points in each higher layer decreases exponentially. If a search query is submitted, the nearest data points in the topmost layer will be identified. There is only one extra data point in the image. Then it goes down a layer, finding the closest data points from the initial data point identified in the highest layer and searching for nearest neighbours from there. The closest data object to the search query will be discovered in the deepest layer.


Text2vec-contextionary is Weaviate’s own language vectorizer module. It puts the words in your dataset into perspective (there are Contextionary versions available for multiple languages). The Contextionary is a vectorizer module that can interact with common models like fastText and GloVe and uses the Weighted Mean of Word Embeddings (WMOWE). FastText on Wiki and CommonCrawl data was used to train the most recent text2vec-contextionary. Weaviate developers want to make the Contextionary available for use cases in every domain, whether they’re business-related, academic-related, or something else entirely. However, if needed, you can make your own vectorizer.

The text2vec-contextionary is a 300-dimensional space where data is placed. A vector of 300 numbers will be assigned to each data point. This vector is calculated using the Contextionary, which has already been trained (no training is required).

The Contextionary calculates the position in the vector space that represents the real-world entity when you add data. The conversion from a data object to a vector position is calculated using the centroid of the words, weighted by the number of times each word appears in the original training text corpus.

Extending the Contextionary


"concept"    : A string with the word, compound word or abbreviation

"definition" : A clear description of the concept, which will be used to create the context of the concept and place it in the high dimensional Contextionary space.

"weight"     : A floating-point number with the relative weight of the concept (default concepts in the Contextionary weight 1.0)

Persistence and Crash Recovery

Weaviate setup

There are several ways to set up a Weaviate instance. It is better to start with docker-compose when setting up the trial version. Cloud deployments can be used for small and large projects. For production environments and large projects, it is recommended to use Kubernetes. Here I am going to describe how to setup weaviate using docker-compose

To start Weaviate with docker-compose, we need a docker-compose configuration file. Docker-compose file is YAML file(.yml)

An instance docker-compose setup document with the transformer model sentence-transformers/msmarco-distilroberta-base-v2 is:

version: '3.4' services: weaviate: image: semitechnologies/weaviate:1.9.0 restart: on-failure:0 ports: - "8080:8080" environment: QUERY_DEFAULTS_LIMIT: 20 AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true' PERSISTENCE_DATA_PATH: "./data" DEFAULT_VECTORIZER_MODULE: text2vec-transformers ENABLE_MODULES: text2vec-transformers t2v-transformers: image: semitechnologies/transformers-inference:sentence-transformers-msmarco-distilroberta-base-v2 environment: ENABLE_CUDA: 0 # set to 1 to enable # NVIDIA_VISIBLE_DEVICES: all # enable if running with CUDA

Using a pre-trained language transformer model as a Weaviate vectorization module with the text2vec-transformers module. Transformer models differ from Contextionary models in that they allow you to plug in a ready-to-use NLP module tailored to your use case.

Save the above snippet as chúng tôi and run docker-compose up from within the same folder to run any of the examples below.

Create a data schema

import weaviate class_obj = { "class": "Wine", "properties": [ { "name": "title", "dataType": ["text"] }, { "name": "description", "dataType": ["text"] } ] } new_class = client.schema.create_class(class_obj)

Upload data

Now the real wine data must be uploaded to Weaviate.

import pandas as pd import weaviate # initiate the Weaviate client # open wine dataset df = pd.read_csv('winemag-data-130k-v2.csv', index_col=0) def add_wines(data, batch_size=20): no_items_in_batch = 0 for index, row in data.iterrows(): wine_object = { "title": row["title"] + '.', "description": row["description"], } client.batch.add_data_object(wine_object, "Wine") no_items_in_batch += 1 results = client.batch.create_objects() no_items_in_batch = 0 client.batch.create_objects() add_wines(df.head(2500)) Sample queries

After all of the objects have been successfully uploaded, you can begin querying the data. We can develop simple and complicated queries with GraphQL using weaviate client.  We can use the following graphQL queries to retrieve the top 20 semantically matching results

near text

import weaviate import pprint # initiate the Weaviate client near_text_filter = { "concepts": ["wine that fits with chicken"], "certainty": 0.5 } query_result = client.query .get("Wine", ["title","description"]) .with_near_text(near_text_filter) .with_limit(20) .do() pprint.pprint(query_result)


move away from

Let’s assume we are looking for healthy wines related to chardonnay but not much spicy and not much related to Aeration.

import weaviate import pprint # initiate the Weaviate client near_text_filter = { "concepts": ['healthy wine Chardonnay"], "certainty": 0.5, "moveAwayFrom":{ "concepts":["spicy and Aeration"], "force":0.6 } } query_result = client.query .get("Wine", ["title","description"]) .with_near_text(near_text_filter) .with_limit(20) .do() pprint.pprint(query_result)



Let’s assume we have fish tonight and want to know which wines go nicely with it but not much spicy and not much related to Aeration and more related to Chardonnay and corked.

import weaviate import pprint # initiate the Weaviate client near_text_filter = { "concepts": ["wine that fits with fish"], "certainty": 0.5, "moveAwayFrom": { "concepts": ["spicy and Aeration"], "force": 0.45 }, "moveTo": { "concepts": ["Chardonnay with Corked"], "force": 0.85 } } query_result = client.query .get("Wine", ["title","description"]) .with_near_text(near_text_filter) .with_limit(20) .do() pprint.pprint(query_result)


We can use the certainty and force parameters in the above examples for result tuning purposes.

Wrapping up

So That’s it. Now you learned a little bit about semantic search and vector storage in this blog. In this article, I tried to demonstrate how to create your own vector database using your own data in Weaviate and how to retrieve them semantically in step by step manner.  In the future, vector search engines like weaviate and semantic search will become an inextricable part of our everyday lives.

Happy coding..🤗

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Preparing For The Future: The New Era Of Talent Management

blog / Human Resources Preparing for the Future: The New Era of Talent Management

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According to a report by Mercer, 54% of employees say they work for more than just the money. This, among many other statistics, represents the significant transformation in the outlook that people have toward work and life. Organizations are being required to work hard on talent management as shifting priorities create new challenges for talent management.

So, are you an HR professional looking to hone your skills in talent management? This article shows you what it constitutes, and how conscious efforts in that direction can transform your organization in the long run.

What is Talent Management?

Talent management is the strategic process of hiring the right talent and helping them achieve their true potential while keeping the organization’s objectives in mind. It is a fairly long process that involves several steps:

Identifying the talent requirements of the organization

Sourcing and onboarding suitable candidates

Nurturing them within the organizational system 

Developing and honing the necessary skills

Training them for the future to achieve long-term organizational goals

Talent Management Examples

Every organization, whether big or small, has to undertake some or the other form of talent management on a daily basis. For example, imagine a business-to-consumer (B2C) edtech company has decided to adopt a business-to-business (B2B) model. The current employees may not always be able to shift gears and work toward achieving the new set of goals. The business will, therefore, approach the HR department to help them source the right talent. And simply posting jobs on employment portals cannot really get you the desired results. It involves a carefully planned talent management process. The following are some important processes:

Talent strategy

Talent sourcing

Employer branding

Onboarding process

Career planning and development

Employee relations

Goal setting and performance management

Why is Talent Management Important? 1. Helps the Company Stay Competitive and Drive Innovation 2. Helps Build Productive Teams

Effective management helps employees feel valued. In turn, they become more productive and collaborate better, resulting in better outcomes for the organization.

3. Helps Establish Strong Employer Branding

Well-implemented talent management helps spread the word about an organization. Everyone wants to work in companies that have good employee and talent management policies in place. Building a brand takes time. The right talent management practices can help you expedite the process and sustain the reputation.

The Talent Management Process

Talent management is a continuous process that involves the following key steps:

Specifying the Skill Sets Required: You must identify the gaps and understand the kind of talent you need to achieve organizational goals. It’s also a good idea to consider nurturing your existing talent by getting them upskilled

Attracting the Right Candidates: Create targeted job posts on top job sites and plan interviews to select suitable candidates

Ensuring a Smooth Onboarding: Conduct orientation sessions for new employees so they can settle faster in their new work environment. Assign buddies to support them in the initial few days

Organizing Learning Activities: Organize learning activities and encourage skill development of the current employees. Promote continuous learning by means of courses and develop a strong learning management system

Conducting Regular Performance Appraisals: Tracking the performance of employees on a regular basis allows you to see their capabilities and gauge if they are ready for additional responsibilities. You can reward them for their hard work and promote them instead of hiring from outside

Talent Management Models

While there is no standard talent model, every model must have some basic component to ensure talent management success:

1. Planning

Before implementing any talent management model, it is essential to carefully plan it out by considering both internal and external factors. This involves the following three key tasks:

Understanding the organizational talent goals

Identifying key evaluation metrics

Developing a hiring plan

2. Attracting

Attracting the right talent requires more than just competitive pay packages. It includes the following three components:

Developing an employee value proposition (EVP)

Creating a marketing plan

Talent acquisition

3. Developing

It involves the following processes:

Onboarding processes

Performance appraisals

Learning and development

Developing career paths

4. Retaining

Talent retention is a result of multiple factors. Prominent among these are:

Company culture

Remuneration strategy

5. Transitioning

Smooth transition processes are an essential part of valuing talent and nurturing them. It involves:

Retirement and succession planning

Internal movement

Knowledge management

Exit processes

Talent Management Strategies

In order to build an effective strategy, you must include the following key components in it:

Defining Goals: Every company has its own goals—exploring new markets, increasing revenue, increasing profits, etc. You must identify what type of talent can help achieve the set goals

Measuring What Matters: Identify metrics that will help you measure and analyze if your strategy is working or needs improvement

Collaboration: A strategy is mostly dependent on HR, but collaboration with other employees is also required. For example, collaborating with C-suite employees is required for succession planning

Communication Regarding Roles and Responsibilities: Ensure that all the employees have a clear understanding of their responsibilities and company goals. You can also discuss their career goals and check if the company is providing the right opportunities for them to grow and excel in their careers

Learning, the Emeritus Way

As more and more employees choose to exercise their privilege of choice, organizations that work together with all stakeholders and implement the right talent management strategy can create a win-win situation for all. If you’re an HR professional and looking to upskill in talent management, check out these exclusive online courses offered by Emeritus.

By Priya S.

Write to us at [email protected]

6 Great Uses Of Visual Search Engines To Find The Images You Want

A visual search engine is a search engine dedicated to search and filter information about images. There are so many different visual search engine providers out there, and each has a different functionality than others. If you have not used a visual search engine before, these are some great uses for one.

1. Reverse Image Search

The basic idea of a reverse search engine is that it should find you the same image or a close match. Some of its applications include finding a higher quality version of an image and checking for copyright infringement.

TinEye is one of the best examples due to its sheer collection of photographs. It offers support for Chrome, Firefox, Safari, Opera and other desktop browsers. TinEye also has a supportive Android app. Alternatively, Pinterest’s mobile app also has a huge collection of images for a thorough reverse search.

2. Metadata Search

Here is how it works. Every photograph taken with a camera or smartphone has metadata attributes such as resolution, device used, make and model number and location attributes. These attributes are recorded even when GPS is turned off! Metapicz is one such metadata search website which tells you everything about a photograph including location coordinates and date/time EXIF information. The following result is for a photograph actually taken on a Bali beach.

Who would have thought this tiny, unknown search engine could return more powerful visual search results than Google? All Google does in its image search is pinpoint to the word, “beach”.

3. Identifying Image Type

Going further on forensics, there are times when we look at an image on a computer and wonder what it is! Describing an image type requires a little text input for Google to understand you well. This is where a search engine provider dedicated to identifying only image types is important.

Launched in May 2023, Wolfram Language Image Identification Project is a sophisticated computer algorithm based on computing and real world data. It can identify the exact image type based on probabilistic algorithms.

4. Identify Object Precisely

While we’re on the topic of identifying image types, a closely-related application identifies the exact object separated from its background. Available on both Android and iOS, CamFind is an object identifying image search tool which gives clear, state-of-the-art image recognition results. Once you start using it, you’re going to love the accurate description consistency.

5. Optical Character Recognition with Visual Search

Did you get your hands on a tattered, hand-written document and want to know what it is? Maybe you want to check out a restaurant online before stepping inside. Optical character recognition features work with your phone’s built-in camera and is one of the best applications for visual search. Use Google Lens instead of Google’s Reverse Image search when you want to scan physical objects located in the real world.

6. Augmented Reality with Visual Search

We have saved the best one for last. An emerging application use is to use visual search engines to oversee an augmented reality (AR) experience. Blippar is one such AR app which uses “cloud search bubbles” to unlock the mystery of real world objects. Even though the app is a work in progress, it is a great way to interact with your surroundings.


While Google’s reverse search engine is thought of as top ranked, there are so many newcomers on the scene now offering enhanced features and greater accuracy. The thing is, visual search engines have evolved a lot more than doing simple reverse image checks. And it’s only going to get better.

Sayak Boral

Sayak Boral is a technology writer with over eleven years of experience working in different industries including semiconductors, IoT, enterprise IT, telecommunications OSS/BSS, and network security. He has been writing for MakeTechEasier on a wide range of technical topics including Windows, Android, Internet, Hardware Guides, Browsers, Software Tools, and Product Reviews.

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The New Faces Of Netbooks

After being panned for having cramped keyboards and “junky” hardware, netbooks evolved over the past month to include bigger screens, better graphics and larger keyboards. Netbooks will now be able to play full high-definition movies with Lenovo’s new IdeaPad S12 netbook. Asustek Computer this month introduced the Eee PC T91, a netbook with a touch screen. Jumping outside Windows, Acer announced plans to put Google’s Android on its netbooks, which should provide an Internet-savvy computing experience.

Lenovo’s great graphics game

One of the knocks against netbooks was poor graphics capabilities, but Lenovo has addressed that concern with its newly announced netbook. The IdeaPad S12 netbook has a 12-inch screen that can play full high-resolution movies, thanks to a powerful chip with an Nvidia graphics processor under the hood. The processor is part of an Nvidia chip package called the Ion platform, which couples the GeForce 9400 graphics core with Intel’s Atom netbook processor.

“For the first time … users will be able to enjoy brilliant 1080p high-definition video with silky smooth playback,” a Lenovo representative said. Strangely enough, Lenovo doesn’t offer an optical drive, such as Blu-ray, with the netbook. But users can buy an external Blu-ray drive or download high-definition content from the Internet.

Before bringing a system with Ion graphics to market, Nvidia will first introduce an S12 netbook with Intel’s integrated graphics. Those systems will become available in June, with prices starting at US$449. Models with Ion will become available a few months later, and pricing for them wasn’t immediately available.

Strong graphics aside, Lenovo has added sundries such as a larger keyboard that make the netbook easier to use. Lenovo claims a six-hour battery life, though it was unclear whether this was for laptops with or without Ion graphics.

Only a few netbooks, such as Intel’s Classmate Convertible, have touch screens. Now Asustek has joined that exclusive group. Asus finally introduced the Eee PC T91 touch-screen netbook, which was originally announced at the Consumer Electronics Show in January. The netbook has an 8.9-inch touch-screen on which users can touch up photos or leave handwritten memos using their fingertips.

Acer’s Android netbook dreams

So what can users expect with an Android netbook? It’s hard to predict, but I’m expecting Android to be a bare-bones OS, much like the version you see on Android smartphones. Users will do most of their tasks, such as word processing, using online applications such as Google Docs.

Top companies including Hewlett-Packard and Dell are already investigating Android for netbooks, so it may well evolve into a OS that competes with Windows. Acer plans to launch its first Aspire One netbooks with Android in the third quarter.

HP’s Minis

The company also launched Mi’s cousin, the Mini 110 XP edition, which comes with Windows XP. It can have as much as 1GB of memory and 160GB of hard drive storage. HP says an optional Broadcom HD video accelerator could become available in July, allowing the laptop to play back full 1080p high-definition content. Pricing for the laptop starts at $329.99

The netbooks weigh about 2.33 pounds and are powered by Intel’s Atom N270 or N280 processors, which run at 1.6GHz and 1.66GHz, respectively.

Fujitsu’s traditional take

The netbook’s price tag of $449 is high, but the company has built-in Bluetooth capabilities and three USB ports, which is unusual for a netbook. However, vendors such as Dell, Asus and Acer offer cheaper netbooks with integrated features such as webcams, which could be a better option for those who don’t need Bluetooth or extra USB ports.

End Of An Era: Microsoft Ended Windows Phone Support

End of an era: Microsoft ended Windows phone support




Update: Well, the big day has arrived. Microsoft officially ended Windows 10 Mobile support today, December 10, 2023. The company pushed the last ever Windows 10 Mobile update, KB4522812, to mark the event.

As Microsoft explains:

Windows 10 Mobile, version 1709 will reach end of service on December 10, 2023. Devices running Windows 10 Mobile and Windows 10 Mobile Enterprise will no longer receive monthly security and quality updates that contain protection from the latest security threats.

There you have it, folks. This is the end of an era. Let’s wish Microsoft good luck in their efforts to make the new Surface Duo and Surface Neo foldable devices successful.

You can read the original story below.

Folks, 318 Days To Go. The hyper for the Windows Phone is over now! It has almost lost its charm as only a few people are using the Windows Phone. The decline in popularity is blamed to be the reason behind the official end of support by Microsoft on December 10, 2023. It is a perfect time for the Windows Phone users to consider upgrading to a new phone. Why not choose iOS or Android this time?

The Windows Phone was launched more than 2 years ago. We have already seen a huge decline in support offered for the Windows Phone by third-party developers. There were a lot of speculations regarding the end of Windows 10 Phone since then. The Windows Phone has received very few features during the last four months.

However, it is worth mentioning that the stability of the mobile OS has been improved through a regular collection of tweaks and fixes during that time frame. Microsoft ensured the timely release of security patches and updates. But the company is soon going to discontinue the updates as the end of support date has been officially announced.

The start of the countdown clock for Windows Phone has begun

Notably, the last update to Windows Phone is declared to be the final update for Windows 10 Mobile (version 1709). Although the official support is not provided to most of the mobile phones for so long.

So, is Windows 10 Mobile still supported? The tech giant has announced to continue releasing security patches for the Windows Phones until December 10, 2023. Those who continue to use Windows Phone after the deadline should be prepared to face the consequences.

The company has no plans to provide support and monitor vulnerabilities for the users beyond December 10.

How did users respond on social media?

Following the announcement, Windows phone fans used social media to express their opinions. A mixes reaction has been seen from the users, but most of them are not satisfied with Microsoft’s end of support decision. Some of them even referred the switching from Windows Phone to iOS or Android as a downgrade.

All Microsoft products including Windows 10 Mobile and Windows 10 Mobile Enterprise will fall under the end of support deadline. The mobile phone enthusiasts have recommended the Windows Phone users to upgrade to iOS or Android. It is yet to be seen that when the long-rumoured Andromeda or Surface Phone will be launched by Microsoft.

If you are a loyal Windows user, switching to either iOS or Android is the best alternative for you at the moment.


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The Quantum Era Has Begun, This Ceo Says

Quantum computing’s full potential may still be years away, but there are plenty of benefits to be realized right now.

Launched 17 years ago by a team with roots at Canada’s University of British Columbia, D-Wave introduced what it called “the world’s first commercially available quantum computer” back in 2010. Since then the company has doubled the number of qubits, or quantum bits, in its machines roughly every year. Today, its D-Wave 2X system boasts more than 1,000.

The company doesn’t disclose its full customer list, but Google, NASA and Lockheed-Martin are all on it, D-Wave says. In a recent experiment, Google reported that D-Wave’s technology outperformed a conventional machine by 100 million times.

“We’re at the dawn of this quantum computing age,” Brownell said. “We believe we’re right on the cusp of providing capabilities you can’t get with classical computing.”

While the bits used by traditional computers represent data as 0s or 1s, qubits can simultaneously be 0 and 1 through a state known as superposition, enabling new levels of performance and efficiency. Equipped with that power, researchers can solve problems they couldn’t solve before — or so the thinking goes.

“In almost every discipline you’ll see these types of computers make this kind of impact,” Brownell said, citing examples like drug discovery and climate modeling. “It opens up a completely new tool chest for scientists and developers.”

IBM recently announced its own quantum capabilities that are available via the cloud.

That’s not to say there aren’t challenges.

Making those chips is no walk in the park, and neither is operating the resulting quantum systems. To achieve quantum effects, the D-Wave 2X’s lattice of 1,000 qubits is cooled to 0.015 degrees Kelvin — 180 times colder than interstellar space. The processor is shielded from almost all of Earth’s magnetic field and is kept in a vacuum, with pressure 10 billion times lower than that of the air around us.

“There are lots of folks around the world doing research projects at this level, where they run for a few minutes and then write up their results, but we’ve had to run 24×7 for years at a time,” Brownell said. “Lockheed Martin, our first customer, came on in 2010. There are a lot of challenges in combining ultralow temperatures with enterprise quality levels.”

In general, when a user models a problem using D-Wave’s technology, the processor considers all possibilities simultaneously. Multiple solutions are returned to the user, scaled to show optimal answers.

D-Wave has just a handful of reference applications that can show customers how a particular task can be accomplished, but it hopes to expand that number significantly.

“People shouldn’t have to understand physics at all to use these tools,” Brownell said.

With a focus on bringing a product to market as quickly as possible, D-Wave opted early on for a model focused on what’s known as quantum annealing, in which the technology uses quantum fluctuations to solve a particular type of problem. IBM uses what’s known as a “gate” or “circuit” model, Brownell said.

That model is “reasonably elegant and makes a lot of sense,” he said. It could also be more broadly applicable.

“The gotcha is that it’s super hard to do,” he said. “I admire the research by IBM and others, but it’s going to take at least a decade before there’s a product that does anything useful.”

“Will anyone ever be able to build a gate model with 10,000 qubits? That’s an open question,” Brownell said. “When and if that model becomes implementable, we’ll have the building blocks in place and will have tackled the hard problems before anyone else.”

Yet another approach is known as the topological model of quantum computing, and that’s the one Microsoft has taken, he said.

“It’s actually more elegant from a theory point of view, but it will require the discovery of new kind of particle that no physicist has ever seen before,” he said.

“We’re at the bleeding edge today,” he said. “It’s a very exciting time to be in the middle of all this.”

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