Most quantum computers rely on quantum bits, or “qubits,” which may individually represent two numbers, 0 or 1. Researchers have now created a quantum computer based on quantum digits, also known as “qudits,” each of which can encode seven numbers. In addition to offering more processing capability with fewer parts, this computer may be more effective at solving complicated problems than qubit computers.

Qudit computers encode 7 numbers instead of 1s and 0s

Most quantum computers use qubits, whereas classical computers store data as bits (1s and 0s). Qubits can become both 1 and 0 simultaneously in a state known as superposition. In essence, this enables each qubit to carry out two calculations simultaneously. Its processing capability can increase exponentially with the number of quantum-mechanically connected or entangled qubits.

The easiest method of completing computations is to encode data as 0s or 1s. According to the study’s principal author Martin Ringbauer, a quantum physicist at the University of Innsbruck in Austria, limiting these devices to binary data stops them from reaching their full potential.

In other words, 2x computations may be performed on a quantum computer having x qubits. A machine having x qudits, where D is the number of states per qudit, may, nonetheless, do Dx numbers of calculations.

“This means you can encode the same information in fewer quantum particles when using qudits,” Ringbauer explains.

Ringbauer says that qudits “can be entangled in many different ways that are not possible for qubit systems. This is an important advantage, since it allows us to do computations more efficiently.”

For example, the chemistry of innovative battery designs or new pharmaceuticals are complicated quantum systems that scientists believe quantum computers might help them understand. This is where employing qudits may have the most potential for usefulness. Using equally complex quantum components to describe these intricate interactions is optimal. According to Ringbauer, computing these systems with qubits may out to be less effective than doing so with qudits.

P-computers are the future for developing efficient AI and ML systems

An eight-qudit quantum processor, with each qudit being an electromagnetically trapped calcium ion, has been created by Ringbauer and his colleagues. Up to seven of an ion’s states are suitable for computation, while an additional eighth state is used for readout. In the journal Nature Physics, they published a summary of their findings online on July 21.

Qudits have only been the subject of minimal proof-of-concept study in the past. Given that qudits have more intricate structures than qubits, Ringbauer noted that more sophisticated quantum computing gear was required before he and his colleagues could experimentally manipulate qudits.

“Each of the qudit states responds differently to external influences, and many of the tools we commonly use to manipulate qubits do not work the same way in a qudit. You need to find ways to control the qudits and interact with them to create entanglement in an efficient way,” Ringbauer says.

In theory, these computers can run on the majority of current quantum computing systems, according to Ringbauer. Extending the level of control attained with two states to “higher dimensions,” or more states, is a barrier.

“Over the past 10 years, I have been exploring qudits in different experimental platforms, which showed me that there is a lot of unused potential in today’s quantum hardware. When I changed to trapped ions, with their exquisite control and natural high-dimensional structure, I was convinced that this platform was ready for unlocking this potential for quantum computing,” Ringbauer explains.

The quantum boost to AI paves the way for AGI

Quantum components with more than two states can also be used in photonic quantum computers.

“Photonic systems interact very little with their environment. This is a benefit, since it makes them extremely stable against noise, but also a challenge since it makes it quite difficult to entangle them. Trapped ions, on the other hand, are quite sensitive to external influences, so they need proper shielding, but they can be controlled, manipulated, and entangled with very high precision,” Ringbauer says.

We anticipate some degree of inaccuracy in any quantum computer. As a result, researchers will need to put procedures in place to stop or lessen these mistakes. With their more complicated structure, qudits are actually anticipated to be more noise-resistant than the simpler qubits, according to Ringbauer, “If we are able to achieve this experimentally, this would be an important step towards fault-tolerant quantum computers.”

Ringbauer claims that although the new platform “opens a new world of possibilities for quantum technology.”

“What we are still lacking to a large extent at this stage is the software and algorithms that make best use of this added potential. I think qudit quantum-software development will be an exciting field in the near term,” he adds.

P-computers might change the future of information technologies for good. There is an urgent demand for additional technology that is scalable and energy-efficient due to the advent of artificial intelligence (AI) and machine learning (ML). Making judgments based on insufficient data is a crucial step in both AI and ML, and the optimal strategy is to output a probability for each potential response.

P-computers are powered by probabilistic bits

Due of the inability of current classical computers to do that task in an energy-efficient manner, researchers are looking for new computing paradigms. Qubit-based quantum computers may be able to assist in overcoming these difficulties, but they are still in the early phases of research and are very sensitive to their environment.

It is an inevitable fact that artificial intelligence will completely change the future. Apart from scientific developments, legal regulations seem to pave the way for the use of artificial intelligence, for instance, UK eases restrictions on data mining laws to facilitate AI industry growth.

Kerem Camsari, an assistant professor of electrical and computer engineering (ECE) at UC Santa Barbara, believes that probabilistic computers (p-computers) are the solution. P-computers are powered by probabilistic bits (p-bits), which interact with other p-bits in the same system. Unlike the bits in classical computers, which are in a 0 or a 1 state, or qubits, which can be in more than one state at a time, p-bits fluctuate between positions and operate at room temperature. In an article published in Nature Electronics, Camsari and his collaborators discuss their project that demonstrated the promise of p-computers.

“We showed that inherently probabilistic computers, built out of p-bits, can outperform state-of-the-art software that has been in development for decades,” said Camsari.

Researchers from the University of Messina in Italy, vice chair of the UCSB ECE department Luke Theogarajan, and physics professor John Martinis, who oversaw the group that created the first quantum computer to attain quantum supremacy, all worked with Camsari’s team. Together, the researchers produced their encouraging results utilizing domain-specific architectures built on traditional hardware. They created a special sparse Ising machine (sIm), a cutting-edge computing system designed to address optimization issues and reduce energy usage.

According to Camsari, the sIm is a group of probabilistic bits that may be compared to individuals. Additionally, each individual only has a tiny group of close friends, or “sparse” relationships, in the system.

“The people can make decisions quickly because they each have a small set of trusted friends and they do not have to hear from everyone in an entire network. The process by which these agents reach consensus is similar to that used to solve a hard optimization problem that satisfies many different constraints. Sparse Ising machines allow us to formulate and solve a wide variety of such optimization problems using the same hardware,” explained Camsari.

Field-programmable gate arrays (FPGAs), a potent piece of hardware that offers far more flexibility than application-specific integrated circuits, were a component of the team’s prototyped design.

“Imagine a computer chip that allows you to program the connections between p-bits in a network without having to fabricate a new chip,” said Camsari.

The researchers demonstrated that their sparse design on FPGAs has boosted sampling speed five to eighteen times quicker than those attained by optimized methods employed on conventional computers, which was up to six orders of magnitude faster.

Additionally, they stated that their sIm achieves huge parallelism where the number of p-bits grows linearly with the number of flips per second, the fundamental metric used to determine how rapidly a p-computer can make an educated decision. Camsari returns to the image of two reliable friends attempting to decide.

“The key issue is that the process of reaching a consensus requires strong communication among people who continually talk with one another based on their latest thinking. If everyone makes decisions without listening, a consensus cannot be reached and the optimization problem is not solved,” added Camsari.

In other words, it is important to increase the flips per second while making sure that everyone listens to each other since the faster the p-bits communicate, the faster a consensus may be formed.

“This is exactly what we achieved in our design. By ensuring that everyone listens to each other and limiting the number of ‘people’ who could be friends with each other, we parallelized the decision-making process,” explained Camsari.

While acknowledging that their ideas are only one part of the p-computer jigsaw, Camsari finds their work incredibly promising because it demonstrated the capacity to grow p-computers up to 5,000 p-bits.

“To us, these results were the tip of the iceberg. We used existing transistor technology to emulate our probabilistic architectures, but if nanodevices with much higher levels of integration are used to build p-computers, the advantages would be enormous. This is what is making me lose sleep,” added Camsari.

The device’s potential was originally demonstrated by an 8 p-bit p-computer created by Camsari and his partners while he was a graduate student and postdoctoral researcher at Purdue University. Their article, which was published in 2019 in Nature, detailed a ten-fold decrease in the energy it used and a hundred-fold decrease in the area footprint. Camsari and Theogarajan were able to further their p-computer research thanks to seed funding from UCSB’s Institute for Energy Efficiency, which supported the study published in Nature Electronics.

“The initial findings, combined with our latest results, mean that building p-computers with millions of p-bits to solve optimization or probabilistic decision-making problems with competitive performance may just be possible,” said Camsari.

The study team anticipates that one day, p-computers will be more quicker and more effective at handling a certain class of tasks, ones that are inherently probabilistic. If you liked this article check out how the latest study showed it is possibe to improve the interpretability of ML features for end-users.

When it comes to this years big data funding stories, the first thing that probably comes to your mind is Intel’s $740 million investment in Cloudera, or MapR’s $110 million financing round led by Google Capital. You’ll also probably recall funding rounds of companies like Hotonworks, MongoDB, and DataStax too. But aside from these popular Hadoop and database vendors, there has been a lot of interesting funding activity in the big data space.

Take Alteryx, for example, who raised a whopping $60 million in funding last month; or ContextRelevant who went and raised $28 million in just 28 months. Undoubtedly, 2013 was an extremely busy year when it came VC activity in the big data space. This year, however, has been equally exciting!

Below are 7 significant funding stories you may have missed so far this year:

1) InsideSales.com Raises $100M to Predict Sales

InsideSales.com, a company specialising in could-based sales acceleration technology, announced in April this year that it has raised $100 million in series C funding. Polaris Partners and Kleiner Perkins Caufield & Byers led the funding round, with participation from Salesforce, Acadia Woods, EPIC Ventures, Hummer Winbald, U.S. Venture Partners and Zetta Venture Partners.

InsideSales.com provides software-as-a-service (SaaS) to more than 1,000 customers, including large enterprises like Microsoft, Fidelity, Groupon and Marketo. The company sits in between marketing automation services like Eloqua and Marketo and CRM vendors like Salesforce and Microsoft. The aim of InsideSales.com is to use resources from both of these fields to help sales teams become more effective in their operations.

2) Kreditech Raises $40 Million at $190 Million Valuation

The big data finance company Kreditech secured $40 million Series B investment from new and existing global investors in June. It was the largest funding round ever for a German financial services technology company and one of the largest rounds in Germany in 2014. The lead investor is Värde Partners, a global investment manager with fund assets in excess of 8.5 billion USD. Co-lead investor is existing shareholder Blumberg Capital. Other shareholders, including Point Nine Capital, also participated in the round.

Based on automated big data and machine learning credit scoring, Kreditech extends loans to individual customers across the globe in real-time using a fully automated credit scoring system and banking backend infrastructure. The company operates independently from credit bureaus and traditional banking infrastructure. In just 20 months, Kreditech has scored more than 1.5 million individual loans – using a current average of 15,000 data points per application.

3) Big Data Ecommerce Outfit Qubit Scoops Up $26m in Series B

E-commerce analytics startup Qubit secured $26 million in Series B funding in late September, with Accel Partners leading the round. Original investors Salesforce Ventures and Balderton Capital, also participated.

Qubit specialises in tools which assist online retailers to monitor and optimize sales through A/B testing and user centric personalization of content. TechCrunch reports that, with approximately 150 customers in the U.K. and the U.S., including Hilton Hotel, Jimmy Choo, Staples, Farfetch, Topshop and Uniqlo, the company revealed a 260% year on year growth in sales in the six months to June 2014.

4) Sumo Logic Plan to Globalise, Following $30 Million Funding Round

Sumo Logic, whose software manages and analyses IT log files, raised $30 million in its latest round of funding (May this year), bringing their total funding to $80.5 million. The funding round was led by Sequoia Capital, with exisiting investors Greylock Partners, Sutter Hill Ventures, and Accel Partners also participating.

Sumo Logic is a cloud-based software solution which mangages system log files to give companies a greater insight into the functioning of their IT systems. The software offers an insight across a company’s IT infrastructure, including the company’s apps, servers and network. Sumo Logic also uses machine learning in its LogReduce feature to sort log file data into patterns, meaning anomalies and errors and can be identified before the become major issues.

5) GoodData’s BI Platform Rakes in $25.7M in Series E Funding

GoodData, a Cloud based Business Intelligence startup, landed $25.7 million in Series E funding just last month, led by Intel Capital and existing investors Andreessen Horowitz, General Catalyst, Tenaya Capital, TOTVS, Next World Capital, Windcrest, and Pharus Capital. The total funding received so far has now gone up to $101.2 million.

According to Ben Kepes, contributor at Forbes, their analytics platform “allows companies to manage, analyze and visualize all from one environment. He adds that “this approach really democratizes data, allowing managers and business people to generate their own insights rather than having to wait for IT departments and data scientists to run queries for them”

6) SiSense Gains Momentum with $30m Series C Funding

SiSense, a business intelligence startup based in Tel Aviv, announced in June that it had raised $30 million in Series C funding. The round was led by DFJ Growth and included participation from existing backers Battery Ventures, Genesis Partners and Opus Capital.

The company’s software is designed to make big data analytics easier for ordinary, non-technical business users. Workers can combine data from multiple sources for analysis, create visualisations and web-based dashboards, and gain insights through both mobile and desktop devices.

Among the 500 customers of SiSense are eBay, Target, Samsung Electronics and ESPN. The company said that the money from the funding round would be used to grow its workforce, expand into new markets and build its customer base.

7) Marketing Intelligence Startup Radius Picks Up $54.7M in a Bid to Change Conventional Marketing

It was announced in September that Radius, a marketing intelligence platform, secured $54.7 million in series C funding. The funding came from companies and individuals such as Founders Fund, Glynn Capital Management, John Mack formerly of Morgan Stanley, Charles Songhurst formerly of Microsoft, and actor and entrepreneur Jared Leto.

Radius provides sales and marketing teams with access to accurate, updated business information on 30+ million small-and-medium-sized businesses, with a team of more than 50, working out of San Francisco. The new funding will be used to expand its data science initiatives with intentions to remodel market and customer engagement strategies used by companies.

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Ecommerce analytics startup Qubit has secured $26 million in Series B funding on Monday, with Accel Partners leading the round. Original investors Salesforce Ventures and Balderton Capital, also participated. The funding will be used to expand Qubit’s US and European operations and development of the ongoing product innovation pipeline.

Qubit specialises in tools which assist online retailers to monitor and optimize sales through A/B testing and user centric personalization of content. Mark Choueke, the global communications director at Qubit, wrote in a blog post, “To receive backing from such experienced heavyweights in the market; to have their confidence that what we do and how we do it is worth investing in is amazing. ”

“It shows us that others are seeing what we have known for a long time: that personalization and web optimization are increasingly going to be among the marketing department’s most important growth levers for their businesses,” he added.

TechCrunch reports that, with approximately 150 customers in the U.K. and the U.S., including Hilton Hotel, Jimmy Choo, Staples, Farfetch, Topshop and Uniqlo, the company revealed a 260% year on year growth in sales in the six months to June 2014.

As a result of the funding, Bruce Golden of Accel Partners, will join the Qubit board. Founded in 2010 by four ex-Googlers, Qubit has raised $36.5 million in funding so far.

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