Quick Quantum Quips: Vendors roll out software applications to increase customer connections through partnerships and internal innovation

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Welcome to TBR’s monthly newsletter on the quantum computing market: Quick Quantum Quips (Q3). This market changes rapidly, and the hype can often distract from the realities of the actual technological developments. This newsletter will keep the community up to date on recent announcements while stripping away the hype around developments.

For more details, reach out to Stephanie Long or Geoff Woollacott to set up a time to chat.

July 2020 Developments

Tying systems and software together has been a general focus of July quantum computing activity. These ties increase quantum computing vendors’ ability to more adequately address and meet the emerging needs of their customers. The finance and banking industry remains a key customer base for quantum as more financial customers partner to develop industry-specific applications for the technology.

  1. Cambridge Quantum Computing (CQC) and IBM partnered to make CQC the first startup-based hub in IBM’s Q Network. This move grants CQC cloud-based access to IBM’s army of 20 commercially available quantum computers. Leveraging this cloud-based access and Qiskit, CQC along with members of its hub will work on advancing quantum capabilities for specialized use in areas such as chemistry, finance and machine learning.
  2. D-Wave has expanded its Leap quantum cloud service into India and Australia, increasing the global footprint of its quantum technology. D-Wave’s quantum cloud service is now available in 37 countries. In addition to the Leap Quantum Cloud Service, customers in India and Australia will now also have access to D-Wave’s Hybrid Solver Service, Integrated Developer Environment and Problem Inspector solutions as well as access to flexible increments of computing time in a hybrid computing model. D-Wave offers this flexible access in free and paid plans.
  3. Atos unveiled its Quantum Annealing Simulator, which is compatible with Atos’ Quantum Learning Machine and enables the company to provide customers with access to quantum capabilities via a simulator as well as gate quantum computing through its existing portfolio. TBR believes this approach is strategically advantageous for Atos, as quantum annealing gives customers access to a quantum-like solution that achieves a lower error rate faster than a traditional system, enabling Atos customers to become familiar with the technology while the system developments continue to reduce error rates and expand capabilities.
  4. Atos also unveiled an open innovation accelerator program — called Scaler, the Atos Accelerator — which is geared toward vertical-centric experts and startups. As part of this program, 15 startups and vertical-specific experts will be selected annually to participate in developing quantum-specific projects fueled by customer interest. The research will further support the development and enrichment of Atos’ existing quantum computing offerings and also reinforce, in TBR’s view, Atos’ ability to provide quantum services. TBR notes that this approach to innovation is similar to that of other services firms involved in quantum computing, where innovation is largely customer driven to address specific demands.
  5. Standard Chartered Ventures unveiled its commitment to researching potential uses for quantum computing in the finance and banking industry through its academic partnership with Universities Space Research Association (USRA). USRA is a U.S.-based nonprofit with 49 university members. Standard Chartered Ventures noted that some use cases being explored through quantum computing include simulating portfolios and significantly increasing the speed of market data generation.

If you would like more detailed information around the quantum computing market, please inquire about TBR’s Quantum Computing Market Landscape, a semiannual deep dive into the quantum computing market. Our most recent version was released in June.

Amid a consolidating market, vendors adopt creative initiatives to fight for mission-critical cloud workloads

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Public cloud growth leaders

While Amazon Web Services (AWS) continues to dominate the public cloud IaaS market, its rivals continue to expand in the space and even collaborate to take market share. Microsoft and Oracle added a new data center interconnection in Amsterdam, deepening the ties between the vendors as they enable customers to run Oracle workloads on Azure and integrate workloads between the vendors’ clouds. TBR believes Microsoft and Oracle will continue to improve their competitive position against AWS as more data center interconnections are added. In addition, TBR expects Alibaba will become a growing threat to AWS and other U.S.-based vendors as it builds out data centers in APAC and EMEA.

Public cloud remains the largest and fastest growing segment of the cloud market. Changes in customer acceptance, data integrations and innovation have combined to sustain the rapid growth of public cloud adoption. The Public Cloud Benchmark details how hybrid deployments, new use cases for enterprise apps, and trends in emerging technology will make public cloud even more relevant in the future.

IT majors may have incurred higher costs for insurance, health benefits for staff in June quarter

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“Rewarding employees, especially the over-performing ones, with higher variable pay during times of crisis is typically a strong morale booster. While attrition also came down across all vendors, vendors know that retaining highly-skilled, loyal personnel can be a hard task, said Boz Hristov, Professional Services Senior Analyst, Technology Business Research Inc.” — The Hindu Business Line

East meets West: A comparative tale of two e-commerce giants placing big bets on the cloud

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Alibaba Cloud, AWS focus on build-outs of global footprints via infrastructure investments in 5G and expansion of data center and edge locations

While the growth of the two globally dispersed e-commerce giants Alibaba and Amazon is largely fueled by retail, both businesses have showed marked dedication to the growth of their respective cloud empires, focusing on infrastructure to fuel global expansion and investment in augmenting their respective portfolios. The investment in cloud is evident as the backbone driving each business as they compete on the global stage to become leaders in digital transformation (DT).

Alibaba Group’s aforementioned profit margins, fueled by its B2B operating model, have enabled a hefty investment of $28 billion dedicated to the cloud business. As the world was gripped by the initial effects of the COVID-19 pandemic back in April, the parent company announced the allocation of the sum, which stands as a massive proclamation of the company’s dedication to cloud and related technologies as the core drivers toward the enablement of DT. The sweeping investment, coupled with new leadership and an expanding partnership strategy, solidifies Alibaba’s intent to position its cloud business as a viable contender against AWS, especially in APAC. Alibaba is clearly placing a large majority of its bets on cloud and the future of DT, as the investment equates to 40% of its total 2019 revenue and is 5.7x the revenue of Alibaba Cloud.

The investment will have a profound impact on Alibaba Cloud’s ability to execute on strategies around DT, infrastructure build-out and R&D, and came at a time when the world could not have been in more need of capabilities such as increased bandwidth and enterprise and SME support. The focus in the medium term is a multifaceted push to gain scale globally, attract new customers and expand wallet share with existing customers, and much of this growth will be propelled by the expansion of Alibaba Cloud’s infrastructure backbone with the build-out of data centers and investment in 5G across EMEA and APAC.

Since solidifying its dominance in China and garnering competitive positioning on the global stage, Alibaba has been frequently referred to as the “Amazon of China.” Both companies have anchored their businesses as e-commerce platforms and have demonstrated parallel growth trajectories, becoming mainstays in the lives and businesses of customers globally. The uniqueness of their respective journeys, which have been significantly shaped by their foundations as e-commerce giants, does not overshadow the companies’ similar strategies. Over time, Alibaba and Amazon have evolved rapidly into diversified companies with a distinct focus on technology and digital transformation. While the companies are in different phases of their growth, in terms of size and global footprint, and have different operating models, the investments in and focus on their respective cloud businesses to drive growth are evident when comparing their evolutions and forward-looking growth strategies.  

Unprecedented government support will help CSPs deploy 5G more quickly and broadly than originally anticipated

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CSP spend on 5G infrastructure will scale faster and peak higher than originally anticipated due to the vast amount of support by governments in a range of countries, including but not limited to China, the U.S., the U.K., Japan, South Korea and Singapore. Due to this, typical historical deployment curves for cellular technologies will not apply to the 5G market, which is now expected to be widely deployed globally by the middle of this decade instead of in the later years of the decade. This pull forward and broadening of infrastructure investment are primarily due to attempts by leading countries to support their economies amid the COVID-19 crisis as well as to keep pace with China’s aggressive and broad investment initiative for competitive reasons. Over the past 12 months, 5G has become a highly political issue, and this unprecedented government involvement and funding are being justified on national security, economic competitiveness and public health grounds.

The 5G Telecom Market Forecast details 5G trends among the most influential market players, including both suppliers and operators. This research includes current-year market sizing and a five-year forecast by multiple 5G market segments and by geographies well as examines growth drivers, top trends and leading market players.

IT services revenue retained its growth trajectory in 1Q20, but the negative effect from the pandemic will intensify in 2Q20

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IT services trailing 12-month (TTM) revenue growth, at 1.5% in U.S. dollars (USD), was down 20 basis points sequentially and 170 basis points year-to-year in 1Q20 as the COVID-19 pandemic began to negatively affect vendors’ revenue growth during March. At every level of every organization, the pandemic forced massive changes in human resources management, pushing vendors to quickly reorganize service delivery to work-from-home models and proactively pursue similar activities with clients as they strive to keep operations running. While vendors are strengthening relationships with existing clients, the pandemic disrupted traditional sales motions, making attracting and landing new logos more difficult in an all-virtual environment, and challenging IT services vendors to develop novel ways to promote new offerings to clients. The pandemic substantially boosted demand for cloud and cybersecurity as all-remote working and delivery necessitated massive changes and brought in new risks.

The IT Services Vendor Benchmark details and compares the initiatives of and track the revenue and performance of the largest global IT services vendors. The report includes information on market leaders, vendor positioning, the IT services market outlook, key deals, acquisitions, alliances, new services and solutions, and personnel developments.

Mipsology’s Zebra looks like a winner

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Mipsology is a 5-year-old company, based in France and California, with a differentiated product that solves a real problem for some customers. The company’s product, Zebra, is a deep learning compute engine for neural network inference. While these engines are not uncommon, Zebra unlocks a potentially important platform for inference using the field programmable gate array (FPGA). There are two parts to this story, which is one of the challenges Mipsology faces.

Inference — the phase where deep learning goes to work

Deep learning has two phases: training and inference. In training, the engine learns to do the task for which it is designed. In inference, the operational half of deep learning, the engine performs the task, such as identifying a picture or detecting a computer threat or fraudulent transaction. The training phase can be expensive, but once the engine is trained it performs the inference operation many times, so optimizing inference is critical for containing costs in using deep learning. Inference can be performed in the cloud, in data centers or at the edge. The edge, however, is where there is the greatest growth because the edge is where data is gathered, and the sooner that data can be analyzed and acted upon, the lower the cost in data transmission and storage.

Specialized AI chips are hot, but the mature FPGA is a player too

For both training and inference, specialized processors are emerging that reduce the cost of using deep learning. The most popular deep learning processor is the graphics processing unit (GPU), principally Nvidia’s GPUs. GPUs rose to prominence because Nvidia, seeing the computational potential of its video cards, created a software platform, CUDA, that made it easy for developers and data scientists to use the company’s GPUs in deep learning applications. The GPU is better suited to training than inference, but Nvidia has been enhancing its GPUs’ inference capabilities. Other specialized processors for deep learning inference include Google’s Tensor Processing Unit (TPU) and FPGAs.

FPGAs have been around since the 1980s. They are chips that can be programmed so the desired tasks are implemented in electronic logic, allowing very efficient repetitive execution, which is ideal for some deep learning inference tasks. Mipsology lists several advantages of FPGAs over GPUs for inference, including a lower cost of implementation, a lower cost of ownership and greater durability. While FPGAs have been used in some implementations, including on Microsoft’s Azure platform, these chips have not received the attention that GPUs have.    

Zebra is where inference meets FPGAs

Mipsology’s Zebra compute engine makes it easy for deep learning developers to use FPGAs for inference. Zebra is a software package that provides the interface between the deep learning application and the FPGA, so that specialized FPGA developers do not to have to be brought in to exploit the benefits of the processors. Zebra is analogous to nVidia’s CUDA software; it removes a barrier to implementation.

Bringing together the puzzle pieces

FPGAs are mature and powerful potential solutions that lower the cost of inference, a key to expanding the role of deep learning. However, the programming of FPGAs is often a barrier to their adoption. Zebra is an enabling technology that lowers that barrier. In the world of specialized solutions based on broadly applicable technologies such as deep learning, there are opportunities for products and services to make it easier to assemble the pieces and lower the cost of development. Zebra is exploiting one of these opportunities.

Buoyed by Red Hat profits, IBM’s CEO sees ‘progress’ in shift to cloud and AI

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“‘A year into becoming part of IBM, Red Hat has not disappointed and is a major component of the new and diversified life that has been breathed into the IBM portfolio,’ said  said Nicki Catchpole, senior analyst at TBR Cloud and Software.” — WRAL TechWire

COVID-19 is driving IBM, IT industry to deliver faster ‘edge’ computing

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“The use cases for edge computing were already vast and varied prior to the pandemic. Self-driving cars, heartmonitoring devices, crop-sensing machinery and inventory-management sensors are examples that scratch the surface of how the low latency, bandwidth management and advanced analytics afforded by edge computing are valuable in a variety of industries.” — WRAL TechWire

Informatica acquires Compact Solutions to improve metadata management across the enterprise

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Enterprises are developing hybrid IT environments at an increasing rate, leveraging cloud-based technologies to achieve flexibility and scalability while keeping workloads on premises that contain sensitive information or require low latency. This creates complex IT environments that need to be integrated, governed and made compliant with local regulations.

Informatica has positioned itself as a leading provider of data integration tools and metadata management offerings, the latter of which provide higher-level information about data, such as where it lives, how it moves and what the data set contains. This better enables IT departments to discover, inventory and organize their data sets across the IT landscape, and allows them to better serve their business stakeholders that need easy access to relevant data. Informatica’s Enterprise Data Catalog (EDC) has already been able to connect metadata from sources like on-premises and cloud-based applications, databases, data lakes, data warehouses and cloud platforms. However, while EDC has been able to incorporate the majority of data sources, it was not an all-ecompassing solution. The vendor recently filled EDC’s portfolio gaps with its acquisition of Compact Solutions, which brings the acquired company’s MetaDex technology into EDC. TBR spoke with Informatica’s Senior Director of Product Marketing Dharma Kuthanur, who noted that the acquisition will help Informatica “extract metadata and lineage from the most complex data sources across the enterprise as well as legacy sources (e.g., mainframes). Other sources include code include code and stored procedures that you would find inside databases and data warehouses like Teradata or IBM Db2 warehouse or Oracle warehouse — so really being able to parse static and dynamic code that’s inside the databases that transform the data — and often this used to be a black box, limiting full understanding of data across the enterprise.”

In particular, the integration of MetaDex capabilities into EDC further enhances the ability of customers to catalog and govern their data from a broader range of sources, including mainframes; multivendor extract, transform, load (ETL) tools; code; and statistical and business intelligence tools such as SAS. Adding these capabilities makes Informatica a one-stop shop for metadata connectivity and data lineage across the entire data landscape spanning hybrid IT environments. Providing customers with visibility and control over all of their data better positions Informatica as the foundation of customers’ AI, machine learning and analytics-related initiatives. In addition, this increases the value proposition of the company’s broader suite of data management products and solutions like Axon Data Governance.

MetaDex rounds out EDC’s capabilities, enabling end-to-end metadata connectivity and data lineage

Large vendors such as IBM, Oracle and SAP have competing metadata tools such as Watson Knowledge Catalog, Oracle Metadata Management and SAP Data Hub, respectively, which are mostly used by the vendors’ existing customer bases. These offerings do have some overlap with Informatica’s portfolio, but Kuthanur noted that “for pure stand-alone opportunities we don’t really [compete with] them because we have a much stronger set of capabilities.”

TBR believes Informatica’s strategy around enterprise data management separates it from the larger incumbent vendors like IBM, Oracle and SAP, due to the vendor-agnostic nature of Informatica’s portfolio. According to Kuthanur, “We position our catalog as a ‘catalog of catalogs’ because we can scan all types of data sources across the enterprise, across multiple clouds, including from some of these third-party catalogs like AWS Glue, and then combine that metadata from metadata across the rest of the enterprise.” There are smaller vendors with vendor-agnostic portfolios for enterprise data management such as Collibra and Alation, but these competitors have smaller global footprints and limited metadata connectivity and lineage capabilities compared to Informatica. Informatica’s acquisition of Compact Solutions also puts the vendor roughly 12 to 18 months ahead of Collibra and Alation in regard to portfolio innovation.