D-Wave is the only company selling large quantum computers. It sold its first system in 2011 . The earlier D-Wave 2X™ quantum computing system featured a 1000+ qubit quantum processor and numerous design improvements that resulted in larger problem sizes, faster performance and higher precision. “Breaking the 1000 qubit barrier marks the culmination of years of research and development by our scientists, engineers and manufacturing team,” said D-Wave CEO Vern Brownell. “It is a critical step toward bringing the promise of quantum computing to bear on some of the most challenging technical, commercial, scientific, and national defense problems that organizations face.”
Later Canadian firm D-Wave quantum computer was able to handle some 2,000 quantum bits (qubits), roughly double the usable number found in the processor in the existing D-Wave 2X system, and be capable of solving certain problems 1,000x faster than its predecessor.
In Sep 2020, D-Wave Systems Inc., announced the general availability of its next-generation quantum computing platform, incorporating new hardware, software, and tools to enable and accelerate the delivery of in-production quantum computing applications. Available today in the Leap™ quantum cloud service, the platform includes the Advantage™ quantum system, with more than 5000 qubits and 15-way qubit connectivity, in addition to an expanded hybrid solver service that can run problems with up to one million variables. The combination of the computing power of Advantage and the scale to address real-world problems with the hybrid solver service in Leap enables businesses to run performant, real-time, hybrid quantum applications for the first time.
“We’ve been on a trajectory, which has been doubling the number of qubits pretty much every year,” said Colin Williams, director of business development and strategic partnerships at D-Wave. D-Wave’s quantum computers are being already used by the Los Alamos National Laboratory, Google, NASA, and Lockheed Martin. D-Wave’s goal is to upgrade all those systems.
The next-gen adiabatic annealing quantum computing platform which will feature a new underlying fab technology, reduced noise, increased connectivity, 5000-qubit processors, and an expanded toolset for creation of hybrid quantum-classical applications. The company plans to “incrementally” roll out platform elements over the next 18 months.
One major change is implementation of a new topology, Pegasus, in which each qubit is connected to 15 other qubits making it “the most connected of any commercial quantum system in the world,” according to D-Wave. In the current topology, Chimera, each qubit is connected to six other qubits. The roughly 2.5x jump in connectivity will enable users to tackle larger problems with fewer qubits and achieve better performance reports D-Wave.”
“The combination of the number of qubits and the connectivity between those qubits determines how large a problem you can solve natively on the quantum computer,” Baratz said. “With the 2,000-qubit processor, we could natively solve problems within 100- to 200-variable range. With the Advantage quantum computer, having twice as many qubits and twice as much connectivity, we can solve problems more in the 600- to 800-variable range. As we’ve looked at different types of problems, and done some rough calculations, it comes out to generally we can solve problems about 2.6 times as large on the Advantage system as what we could have solved on the 2000-qubit processor. But that should not be mistaken with the size problem you can solve using the hybrid solver backed up by the Advantage quantum computer.”
The company calls Advantage “the first quantum computer built for business.” “We engineered it to be able to deal with large, complex commercial applications and to be able to support the running of those applications in production environments. There is no other quantum computer anywhere in the world that can solve problems at the scale and complexity that this quantum computer can solve problems. It really is the only one that you can run real business applications on. The other quantum computers are primarily prototypes. You can do experimentation, run small proofs of concept, but none of them can support applications at the scale that we can,” said D-Wave CEO Alan Baratz .
“The dirty little secret of the internet is that these conventional computers are one of the largest energy usages in the world. So it’s important that we offset that by technologies like ours,” said D-Wave CEO Vern Brownell. “The particular type of quantum computing that we do is very energy efficient, and so will make a big impact on lowering the energy usage in computing over time, and eventually maybe even help on issues like climate change.” There’s a little bit of power used to cool the system down to that level, but it’s not much. It’s about 20 kilowatts, which is pretty trivial in today’s data centres,” he said. “As the power of this computer grows over time, it won’t generate any more heat, so as more and more applications move toward our form of quantum computers, they will achieve a real energy advantage.”
D-Wave Two is not a universal quantum computer
Quantum computing and quantum information processing are next revolutionary technology expected to have immense impact. Quantum computers will be able to perform tasks too hard for even the most powerful conventional supercomputer and have a host of specific applications, from code-breaking and cyber security to medical diagnostics, big data analysis and logistics. Quantum computers could accelerate the discovery of new materials, chemicals and drugs. They could dramatically reduce the current high costs and long lead times involved in developing new drugs.
There are two major approaches to quantum computing systems – annealing and the gate model. Gate model systems actually require longer coherence times, more qubit control, etc. than D-Wave does. D-Wave forces the quantum states into a digital state with each annealing cycle, some 5000 – 10,000 times per second.
Currently IBM, Google and Rigetti have created or are creating about 50 qubit gate systems with no error correction. They are not universal quantum computing systems but approximate gate model systems. These system may be good at one or two applications, that’s unknown so far, said Bo Ewald, who is D-Wave System’s President of International Business. Bo previously worked at Cray, Silicon Graphics and Los Alamos National Laboratory.
D-Wave Two is not a universal quantum computer, It has been designed specifically to perform a process called “quantum annealing”, which is a technique for finding the global minimum of a complicated mathematical expressions hence expected to be capable of solving optimization and sorting problems exponentially faster than a classical computer. It is especially useful for solving problems can be expressed as an energy landscape, and the solution to the problem is the lowest point in that landscape such as minimizing error in a voice recognition system, controlling risk in a financial portfolio, or reducing energy loss in an electrical grid. In comparison, a universal quantum computer is one that, in theory, can perform any computation exponentially faster than a classical computer.
One thing D-Wave’s computer can’t do is run something called “Shor’s Algorithm,” a hot topic in the quantum computing world because it has the potential to break all modern encryption.
While some argue that the scope of the problems that can be resolved by the technology is limited, quantum annealing processors are easier to control and operate than their gate-based equivalents, which is why D-Wave’s technology has already reached much higher numbers of qubits than can be found in the devices built by big players like IBM or Google.
In 2014, a group of researchers from EHT Zurich, Google, Microsoft, the University of Southern California and the University of California at Santa Barbara subjected D-Wave machine to 1,000 randomly chosen cost-function problems as well as to the classical annealer. They found that, overall, the D-Wave did not exhibit quantum speedup on the set of problems used. However D-Wave asserts that Troyer’s group chose an inappropriate set of problems to perform this test, and that the D-Wave machine would distinguish itself if subjected to the harder problems.
Unlike “conventional” quantum computers – which are kept in a fragile quantum state throughout the calculation – quantum annealing involves making a transition from a quantum to classical system which results in very short coherence time, on the order of a few nanoseconds, while the total time to perform of one annealing run is 20 microseconds. Since the qubits are coherent for only a fraction of the total time, hence they could not show a quantum speedup.
In Feb 2021 it was reported that Scientists from quantum computing company D-Wave have demonstrated that, using a method called quantum annealing, they could simulate some materials up to three million times faster than it would take with corresponding classical methods. Together with researchers from Google, the scientists set out to measure the speed of simulation in one of D-Wave’s quantum annealing processors, and found that performance increased with both simulation size and problem difficulty, to reach a million-fold speedup over what could be achieved with a classical CPU.
To simulate exotic magnetism, King and his team used the D-Wave 2,000-qubit system, which was recently revised to reduce noise, to model a programmable quantum magnetic system, just like Berezinskii, Kosterlitz and Thouless did in the 1970s to observe the unusual states of matter. The researchers also programmed a standard classical algorithm for this kind of simulation, called a “path-integral Monte Carlo” (PIMC), to compare the quantum results with CPU-run calculations. As the numbers show, the quantum simulation outperformed classical methods by a margin. “What we see is a huge benefit in absolute terms,” said King. “This simulation is a real problem that scientists have already attacked using the algorithms we compared against, marking a significant milestone and an important foundation for future development. This wouldn’t have been possible today without D-Wave’s lower noise processor.”
Dr. Gabriel Aeppli, professor of physics at ETH Zürich and EPF Lausanne, and head of the Photon Science Division of the Paul Scherrer Institute. “This comes as a surprise given the belief of many that quantum annealing has no intrinsic advantage over path integral Monte Carlo programs implemented on classical processors.”
Instead of proving quantum supremacy, which happens when a quantum computer runs a calculation that is impossible to resolve with classical means, D-Wave’s latest research demonstrates that the company’s quantum annealing processors can lead to a computational performance advantage. “This work is the clearest evidence yet that quantum effects provide a computational advantage in D-Wave processors,” said Andrew King, director of performance research at D-Wave.
“Successfully demonstrating such complex phenomena is, on its own, further proof of the programmability and flexibility of D-Wave’s quantum computer,” said D-Wave CEO Alan Baratz. “But perhaps even more important is the fact that this was not demonstrated on a synthetic or ‘trick’ problem. This was achieved on a real problem in physics against an industry-standard tool for simulation—a demonstration of the practical value of the D-Wave processor. We must always be doing two things: furthering the science and increasing the performance of our systems and technologies to help customers develop applications with real-world business value. This kind of scientific breakthrough from our team is in line with that mission and speaks to the emerging value that it’s possible to derive from quantum computing today.”
D-Wave Embraces Gate-Based Quantum Computing
In Oct 2021, -Wave Systems, the quantum computing pioneer that has long championed quantum annealing-based quantum computing, announced it was expanding into gate-based quantum computing.
Today, buoyed by its experience and success in building semiconductor-based superconducting qubits for annealing systems – notably its systems engineering and on-chip control circuitry expertise, according to Johnson – D-Wave seeks to enter the gate-based market. At its recent annual user meeting, Qubits (virtual), the company spelled out its plans under the banner of Clarity Roadmap.
Much of the roadmap is a continuation of D-Wave’s quantum annealing systems. While makers of gate-based quantum computers struggle to get to 100 qubits, D-Wave has a 5000-qubit system, with 15-way qubit interconnect technology, and is planning a 7000-qubit system. However, quantum annealing and gate-based systems are very different beasts. So-called universal gate-based system quantum computers of the kind being pursued by IBM, Rigetti, Google and others are more flexible and can handle wide range of applications They are the end game.
Quantum annealing systems, such as D-Wave’s Advantage system, have been shown they can solve select optimization problems very well, and in a more direct, less-complicated way than gate-based systems. D-Wave is hardly walking away from quantum annealing computing.
“There’s a growing body of theoretical work now highlighting the fact that quantum annealing is likely always going to be the best approach towards optimization and that’s not what I think we all thought five or ten years ago,” said Johnson, “That’s significant to us and we’ve seen that with a broad range of actually useful customer applications.” In this context, useful customer applications are really more proto-applications (logistics, biopharma, etc.) and no one is using them in production. Still, Johnson believes some apps will be in production in the 1-to-2-year timeframe.
D-Wave contends its work building larger many-thousand-qubit quantum annealing systems has uniquely prepared it for tackling the scale-up problems bogging down gate-based quantum development. Most observers agree that cramming thousands (or millions) of cables into icy-cold dilution refrigerator to control qubits is impractical long-term, and there are many efforts (Intel and Microsoft are just two) to develop cryo-controller chips.
“It looks like we’re going to need hundreds of thousands of qubits and I don’t see how we can connect all the qubits to all the room temperature electronics [with cables]. The only practical, scalable way to develop a gate model system is with some kind of integrated fabrication stack. We think of superconducting-based qubits and a multilayer (3D), integrated circuit fabrication stack, that requires also a lot of investment and understanding of materials and understanding of how to improve coherence. This is exactly what we’re doing for quantum annealing,” said Johnson.
“When we started digging into it, there was a lot of overlap. Many of the problems were very similar. A lot of the technology that we had developed around on-chip integrated control circuitry – for example, the cold superconducting classical circuits necessary for doing things like homogenizing manufacturing variations across multiple qubits that allowed us to scalable IO so you don’t have several wires for every qubit. The ability to do this sort of de-multiplexed, efficient control of many, thousands or tens of thousands of devices with a just few hundred wires. All of that was necessary to integrate and develop quantum annealing on a [large] scale,” said Johnson
D-Wave Quantum Computer Applications
From improving the logistics of retail supply chains to simulating new proteins for therapeutic drugs, through optimizing vehicles’ routes through busy city streets, D-Wave is currently counting 250 early quantum annealing applications from various different customers.
The D-Wave quantum annealer has been employed in solving the coloring problem, analyzing optimization of traffic flow, computing small molecules, and simulating real materials, among several other relevant problems. And Brownell described how Volkswagen is using the $15-million D-Wave 2000Q computer to route taxis in Beijing. “They started with data from Beijing and they developed technology that will basically send a command to a particular taxicab to take an alternate route with the goal of sort of smoothing out the traffic,” Brownell said.
Some applications for D-Wave’s quantum computer include machine learning, financial simulations, and coding optimization. For example, the quantum computers could be used to build classifiers for better speech recognition or labeling of images, Vern Brownell, D-Wave’s CEO said. Algorithms play a big role in making D-Wave’s quantum computers effective. “Our belief is that machine learning is the killer app for quantum computing,” Brownell said.
Protein design pioneer Menten AI has developed the first process using hybrid quantum programs to determine protein structure for de novo protein design with very encouraging results often outperforming classical solvers. Menten AI’s unique protein designs have been computationally validated, chemically synthesized, and are being advanced to live-virus testing against COVID-19.
Western Canadian grocery retailer Save-On-Foods is using hybrid quantum algorithms to bring grocery optimization solutions to their business, with pilot tests underway in-store. The company has been able to reduce the time an important optimization task takes from 25 hours to a mere 2 minutes of calculations each week. Even more important than the reduction in time is the ability to optimize performance across and between a significant number of business parameters in a way that is challenging using traditional methods.
Accenture, a leading global professional services company, is exploring quantum, quantum-inspired, and hybrid solutions to develop applications across industries. Accenture recently conducted a series of business experiments with a banking client to pilot quantum applications for currency arbitrage, credit scoring, and trading optimization, successfully mapping computationally challenging business problems to quantum formulations, enabling quantum readiness.
Volkswagen, an early adopter of D-Wave’s annealing quantum computer, has expanded its quantum use cases with the hybrid solver service to build a paint shop scheduling application. The algorithm is designed to optimize the order in which cars are being painted. By using the hybrid solver service, the number of color switches will be reduced significantly, leading to performance improvements.
In Nov 2019, at the WebSummit conference in Lisbon, D-Wave and Volkswagen teamed up to manage a fleet of buses using a new system that, among other things, used D-Wave’s quantum technology to help generate the most efficient routes. Unlike other players in the quantum computing market, D-Wave always bet on quantum annealing as its core technology. This technology lends itself perfectly to optimization problems like the kind of routing problem the company tackled with VW, as well as sampling problems, which, in the context of quantum computing, are useful for improving machine learning models, for example.
D-Wave Systems Inc. says their quantum computers can help solve climate change, too. In Dec 2018, the British Columbia government invested $2 million in Burnaby-based D-Wave, through the province’s Innovative Clean Energy fund, and Sustainable Technologies Development Canada gave the company $10 million to continue developing quantum computers, on the basis that they could save energy.
D-Wave, NASA and DOD explore massive potential of Quantum Computers
One of the toughest problems in mathematics is known as the traveling salesperson problem, which asks to find the shortest route between a list of cities.The traveling salesperson problem is also pervasive. Practically anytime you want to make a complex process more efficient, you need to do this kind of combinatorial optimization. Logistics businesses need to solve a version of it every time they plan a route. Semiconductor manufacturers encounter similar issues when they design and manufacture chips.
“D-Wave has begun to work with investment managers on the related problem of designing portfolios. In order to generate the maximum returns for a given risk profile, a fund manager needs to not only choose among the thousands of available securities, but also minimize transaction costs by achieving the most optimal portfolio in the minimum number of trades,” writes Greg Satell in Forbes.
In each case, D-Wave’s quantum systems allow us to swallow complexity whole, rather than using shortcuts that reduce efficiency. Jeremy Hilton, Senior Vice President, Systems, at D-Wave says “Complex processes are all around us. By using quantum computing to operate them more effectively, we can make just about everything we do run more smoothly.”
“Scientists at Harvard have found that quantum computers will allow us to map proteins much as we do genes today. D-Wave has also formed a partnership with DNA-SEQ to use its quantum computers to explore how to analyze entire genomes to create more effective therapies,” writes Greg Satell.
Current machine learning algorithms generate misclassification errors because of the limited capacity of conventional computers, data is lost in the training process. D-Wave is working with a number of partners, such as NASA, to help train artificial intelligence systems to reflect human thought processes far more completely than is possible with conventional computers, which will help to minimize mistakes.
Scientists at Google, NASA and USRA have been using it to explore the potential for quantum computing and its applicability to a broad range of complex problems such as web search, speech recognition, planning and scheduling, air-traffic management and robotic missions to other planets.
Computing giants believe quantum computers could make their artificial-intelligence software much more powerful and unlock scientific leaps in areas like materials science, according to MIT Technology Review. NASA hopes quantum computers could help schedule rocket launches and simulate future missions and spacecraft.
NASA’s QuAIL team aims to demonstrate that quantum computing and quantum algorithms may someday dramatically improve the agency’s ability to solve difficult optimization problems for missions in aeronautics, Earth and space sciences, and space exploration.
D-Wave Systems Inc., the leader in quantum computing systems, software, and services, announced in Mar 2019 that Los Alamos National Laboratory has upgraded their D-Wave quantum computer to the D-Wave 2000Q system. The Laboratory, a multidisciplinary research institution engaged in strategic science to address national security priorities, acquired the 1000+ qubit D-Wave 2X™ quantum computer in 2015. Now, with on-premises access to more than 2000 qubits on the D-Wave 2000Q system, Los Alamos researchers and partners can leverage its advanced hardware, features, and functionality, such as faster performance, improved annealing controls, and the ability to embed and solve larger and more complex problems.
Los Alamos will have the option to upgrade to D-Wave’s upcoming next-generation architecture and state-of-the-art quantum computing platform, which will encompass a new topology, more qubits, and lower noise, and will feature expanded software and tools. To date, Los Alamos and its research collaborators have built over 60 early quantum applications and conducted essential research in domains ranging from quantum mechanics, linear algebra, computer science, and machine learning, to earth science, biochemistry, sociology, and more.
“Quantum computers enable us to use the laws of physics to solve intractable mathematical problems,” said Marcos de López de Prado, Senior Managing Director at Guggenheim Partners and a Research Fellow at Lawrence Berkeley National Laboratory’s Computational Research Division. “This is the beginning of a new era, and it will change the job of the mathematician and computer scientist in the years to come.”
The Space and Naval Warfare Systems Center Pacific in San Diego is working with one of the few quantum computers in existence to assess its applicability to military computing problems. “Some of those problems would be cooperative communication and ad hoc networks, time division multiple access message scheduling, or algorithms for data storage and energy data retrieval with underwater autonomous robots—optimization-type problems,”said Dr. Joanna Ptasinski, an electronics engineer at SPAWAR. SPAWAR provides the Navy and other military branches with essential capabilities in the areas of command and control, communications, computers, intelligence, surveillance, and reconnaissance.
D-Wave Launches Leap, the First Real-Time Quantum Application Environment
As part of its commitment to enabling businesses to build in-production quantum applications, the company announced D-Wave Launch™, a jump-start program for businesses who want to get started building hybrid quantum applications today but may need additional support. Bringing together a team of applications experts and a robust partner community, the D-Wave Launch program provides support to help identify the best applications and to translate businesses’ problems into hybrid quantum applications. The extra support helps customers accelerate designing, building, and running their most important and complex applications, while delivering quantum acceleration and performance.
The company also announced a new hybrid solver. The discrete quadratic model (DQM) solver gives developers and businesses the ability to apply the benefits of hybrid quantum computing to new problem classes. Instead of accepting problems with only binary variables (0 or 1), the DQM solver uses other variable sets (e.g. integers from 1 to 500, or red, yellow, and blue), expanding the types of problems that can run on the quantum computer. The DQM solver will be generally available on October 8.
D-Wave Systems Inc., the leader in quantum computing systems and software, in Oct 2018 announced the immediate availability of free, real-time access to the D‑Wave Leap™ Quantum Application Environment (QAE). Leap is the first cloud-based QAE providing real-time access to a live quantum computer. In addition to access, Leap provides open-source development tools, interactive demos and coding examples, educational resources, and knowledge base articles. Designed for developers, researchers, and forward-thinking enterprises, Leap enables collaboration through its online community, helping Leap users write and run quantum applications to accelerate the development of real-world applications.
To‑date, D‑Wave customers have developed 100 early applications for problems spanning airline scheduling, election modeling, quantum chemistry simulation, automotive design, preventative healthcare, logistics, and more. Many have also developed software tools that make it easier to develop new applications. These existing applications and tools, along with access to a growing community, give developers a wealth of examples to learn from and build upon.
Our job is to sift through the sands of data to find the gold—information that will help our manufacturing customers increase equipment efficiency and reduce defects. With D‑Wave Leap, we are showing we can solve computationally difficult problems today, while also learning and preparing for new approaches to AI and machine learning that quantum computing will allow,” said Abhi Rampal, CEO of Solid State AI. “We started with quantum computing on D-Wave because we knew we wanted to be where the market was going, and we continue because we want to be a leader in finding commercial applications for the technology. With Leap, D‑Wave is making systems, software, and support available to help developers and innovators commercialize quantum applications.“
“We are developing innovative new materials to solve large-scale industrial problems using our proprietary Materials Discovery Platform. Part of our platform relies on first-principles materials simulations, requiring exceptional amounts of computational processing power. By providing access to a live quantum computer, D‑Wave Leap provides a robust environment for developers to learn, code, and teach, furthering the quantum ecosystem,” said Michael Helander CEO, OTI Lumionics. “Today, we are able to use the D‑Wave 2000Q as a powerful optimizer to help calculate the electronic structure of industrially-relevant sized molecules, a first for a quantum computer. As the community grows, shares, and innovates, the possibilities for materials discovery are endless. I expect D‑Wave to continue to innovate with us, enabling the discovery of countless new materials using quantum computing.”
“Leap can enable hundreds of thousands of developers to write and run quantum applications, without having to learn the complex physics that underpins quantum computers. Any one of these developers could write the first killer quantum application, solving complex global problems with quantum computing.”
“The next frontier of quantum computing is quantum application development. While we continue to advance our industry-leading quantum technology, our goal with Leap is to ignite a new generation of developers who will explore, experiment, and ultimately build our quantum application future,” said Vern Brownell, D‑Wave CEO.
D-Wave’s release of QBSOLV
D-Wave Systems Inc., has released an open-source, quantum software tool as part of its strategic initiative to build and foster a quantum software development ecosystem. The new tool, qbsolv, enables developers to build higher-level tools and applications leveraging the quantum computing power of systems provided by D-Wave, without the need to understand the complex physics of quantum computers. The promise of qbsolv and quantum acceleration is to enable faster solutions to larger and more complex problems.
Qbsolv is used to solve large optimization problems, useful in a wide range of important applications. Qbsolv handles large problems by automatically breaking them down into smaller segments that can run individually on D-Wave’s quantum processor, then combining the individual answers into one overall solution. To date, users have shown that qbsolv enables solution of problems up to twenty times larger than could be solved on a D-Wave processor without using qbsolv. As the power of the D-Wave system continues to increase, the size of the individual problem segments will increase, allowing solution of even larger problems in less time.
D-Wave’s release of qbsolv adds to a growing software ecosystem that enables application developers to use D-Wave’s quantum systems more quickly and easily.
Users given early access to qbsolv have already validated its use in several domains, including:
- Scientists at Los Alamos National Laboratory used qbsolv with a D-Wave system to find better ways of splitting the molecules on which they performed electronic structure calculations, among the most computationally intensive of all scientific calculations. In some cases this new method gave better results than the industry-standard graph partitioner and the winner of last year’s graph-partitioning challenge.
- Scientists at a research institute are using qbsolv to find a faster solution to the multiple-sequence-alignment (MSA) problem from genomics, a computationally hard problem used to study the evolution and function of DNA, RNA and protein
“Just as a software ecosystem helped to create the immense computing industry that exists today, building a quantum computing industry will require software accessible to the developer community,” said Bo Ewald, president, D-Wave International Inc. “D-Wave is building a set of software tools that will allow developers to use their subject-matter expertise to build tools and applications that are relevant to their business or mission. By making our tools open source, we expand the community of people working to solve meaningful problems using quantum computers.”
Scott Pakin, a computer scientist at Los Alamos National Laboratory, has built a quantum macro assembler, available on GitHub, that leverages qbsolv to create programs that would otherwise be too large to implement on the D-Wave system. Other prominent national labs are also using qbsolv to develop quantum computing frameworks that they hope to open source.
In order to encourage widespread use of the tool, qbsolv is designed to ingest a quadratic unconstrained binary optimization (QUBO) format that is familiar and accessible to application developers. The QUBO form has been used with classical computing to solve many different kinds of problems. As an example, in one clinical trial an application developed in QUBO form was successful in predicting epileptic seizures 20-40 minutes in advance of their occurrence. Another study using the QUBO form explored grouping machines and parts together in a flexible manufacturing system in order to facilitate economies in time and cost.
The Advantage quantum computer and the Leap quantum cloud service include:
New Topology: The topology in Advantage makes it the most connected of any commercial quantum system in the world. In the D-Wave 2000Q™ system, qubits may connect to 6 other qubits. In the new Advantage system, each qubit may connect to 15 other qubits. With two-and-a-half times more connectivity, Advantage enables the embedding of larger problems with fewer physical qubits compared to using the D-Wave 2000Q system. The D-Wave Ocean™ software development kit (SDK) includes tools for using the new topology. Information on the topology in Advantage can be found in this white paper, and a getting started video on how to use the new topology can be found here.
Increased Qubit Count: With more than 5000 qubits, Advantage more than doubles the qubit count of the D-Wave 2000Q system. More qubits and richer connectivity provide quantum programmers access to a larger, denser, and more powerful graph for building commercial quantum applications.
Greater Performance & Problem Size: With up to one million variables, the hybrid solver service in Leap allows businesses to run large-scale, business-critical problems. This, coupled with the new topology and more than 5000 qubits in the Advantage system, expands the complexity and more than doubles the size of problems that can run directly on the quantum processing unit (QPU). In fact, the hybrid solver outperformed or matched the best of 27 classical optimization solvers on 87% of 45 application-relevant inputs tested in MQLib. Additionally, greater connectivity of the QPU allows for more compact embeddings of complex problems. Advantage can find optimal solutions 10 to 30 times faster in some cases, and can find better quality solutions up to 64% percent of the time, when compared to the D-Wave 2000Q LN QPU.
Expansion of Hybrid Software & Tools in Leap: Further investments in the hybrid solver service, new solver classes, ease-of-use, automation, and new tools provide an even more powerful hybrid rapid development environment in Python for business-scale problems.
Flexible Access: Advantage, the expanded hybrid solver service, and the upcoming DQM solver are available in the Leap quantum cloud service. All current Leap customers get immediate access with no additional charge, and new customers will benefit from all the new and existing capabilities in Leap. This means that developers and businesses can get started today building in-production hybrid quantum applications. Flexible purchase plans allow developers and forward-thinking businesses to access the D-Wave quantum system in the way that works for them and their business.
Ongoing Releases: D-Wave continues to bring innovations to market with additional hybrid solvers, QPUs, and software updates through the cloud. Interested users and customers can get started today with Advantage and the hybrid solver service, and will benefit from new components of the platform through Leap as they become available.
D-Wave Quantum technology
The D-Wave quantum processor is built from a lattice of tiny loops of the metal niobium, each of which is one quantum bit, or qubit. When niobium is cooled down below 9.2 Kelvin it becomes a superconductor and starts to exhibit quantum mechanical effects. By circulating current either clockwise or counter-clockwise, the superconducting qubit emits a magnetic field pointing downward or upward, encoding a logical 1 or 0. During quantum annealing, current flows clockwise and counter-clockwise simultaneously.
The annealing, is performed by adjusting the coupling between the rings, the coupling between the bits represents an energy, so the sum of all these can be measured. The smaller the sum is, the better your solution. D-Wave chip optimizes all of its bits at the same time as the couplings between them sweep in an analog fashion between pre-programmed beginning and endpoints.
Every additional qubit doubles the search space of the processor. At 1000 qubits, the new processor considers (2)^1000 possibilities simultaneously, a search space which dwarfs the (2)^512 possibilities available to the 512-qubit D-Wave Two. In fact, the new search space contains far more possibilities than there are particles in the observable universe.
D-Wave does not have error correction and neither will the current generation of quantum gate systems. D-Wave repeatedly solves problems to get around the lack of error correction. 1000 problem runs generates a distribution of solutions. Those solutions are easily checked to see which is the best solution.
Research on quantum error correction suggests that for 2000 qubits one would need 1 million error correcting qubits and another paper said for 1000 qubits one would need 6.3 million error correcting qubits. The vast number of qubits needed for an error corrected system is why no error correcting system has been implemented for any quantum gate.
“There has been a fair amount of discussion in both the media and the scientific community about the impact of environmental noise (heat, vibration, magnetism etc) and the need for quantum error correction in quantum computing, and what that means for the D-Wave technology. One of the most attractive characteristics of quantum annealing systems such as the D-Wave system, is that they are more robust against decoherence from certain types of environmental noise than other quantum systems, such as those built on a gate model, would be,” according to Ewald.
D-Wave’s quantum computer runs a quantum annealing algorithm to find the lowest points, corresponding to optimal or near optimal solutions, in a virtual “energy landscape.” While there are different ways in which users can submit problems to the system, at the level of the machine instruction of the quantum processor the system solves a Quadratic Unconstrained Binary Optimization Problem (QUBO), where binary variables are mapped to qubits and correlations between variables are mapped to couplings between qubits.
Annealing relies on “quantum tunnelling” and lets an initially simple system evolve very slowly towards the desired result. This involves encoding a problem into the states of some quantum bits (qubits) that have specifically assigned interactions. These interactions are traditionally classical, in that they are either on or off. The qubits are then put in a superposition of states and the system gradually evolves – ensuring that all the qubits always remain in the lowest energy “valley” or ground state – until the global minimum of the function is found.
The success of classical heuristic search algorithms often depends on the balance between global search for good regions of the solution space (exploration) and local search that refines known good solutions (exploitation). While local refinement of known solutions is not available to the canonical forward quantum annealing algorithm, D-Wave has recently developed a reverse annealing feature that makes this possible by annealing backward from a specified state, then forward to a new state.
Reverse annealing allows users to start systematic searches for the best answer based upon their understanding of the solution space. It circumvents limitations on the number of qubits or the entanglement time by starting new searches in different locations. This enables the use of quantum annealing for the refinement of classical states via local search, making it possible to use quantum annealing as a component in more sophisticated hybrid algorithms.
D-Wave has modifications to their architecture which COULD convert their annealing system into a general purpose quantum computing system. It will require more control over the qubits. It would still be annealing. It would be programmable and allow for multiple quantum programs at the same time and allow for repeatable answer runs, said Marcos de López
D-Wave quantum processor breaks 1000 qubit barrier to Address Larger and More Complex Problems
The laws of quantum mechanics including “superposition” of states, along with the quantum effects of entanglement and quantum tunneling, enable quantum computers to consider and manipulate many combinations of bits simultaneously.
At 1000+ qubits, the D-Wave 2X quantum processor evaluates all 2 exp(1000) possible solutions simultaneously as it converges on optimal or near optimal solutions, more possibilities than there are particles in the observable universe.
The D-Wave 2X demonstrates a factor of up to 15x gains over highly specialized classical solvers in nearly all classes of problems examined. Measuring only the native computation time of the D-Wave 2X quantum processor shows performance advantages of up to 600x over these same solvers, according to D-Wave.
“For the high-performance computing industry, the promise of quantum computing is very exciting. It offers the potential to solve important problems that either can’t be solved today or would take an unreasonable amount of time to solve,” said Earl Joseph, IDC program vice president for HPC.
Beyond the much larger number of qubits, other significant innovations include:
- Lower Operating Temperature: While the previous generation processor ran at a temperature close to absolute zero, the new processor runs 40% colder. The lower operating temperature enhances the importance of quantum effects, which increases the ability to discriminate the best result from a collection of good candidates.
- Reduced Noise: Through a combination of improved design, architectural enhancements and materials changes, noise levels have been reduced by 50% in comparison to the previous generation. The lower noise environment enhances problem-solving performance while boosting reliability and stability.
- Increased Control Circuitry Precision: In the testing to date, the increased precision coupled with the noise reduction has demonstrated improved precision by up to 40%. To accomplish both while also improving manufacturing yield is a significant achievement.
- Advanced Fabrication: The new processors comprise over 128,000 Josephson junctions (tunnel junctions with superconducting electrodes) in a 6-metal layer planar process with 0.25μm features, believed to be the most complex superconductor integrated circuits ever built.
- New Modes of Use: The new technology expands the boundaries of ways to exploit quantum resources. In addition to performing discrete optimization like its predecessor, firmware and software upgrades will make it easier to use the system for sampling applications.
The physical footprint of the system is approximately 10′ x 7′ x 10′ (L x W x H). It houses a sophisticated cryogenic refrigeration system, shielding and I/O systems that support a single thumbnail-sized quantum processor. Most of the physical volume of the current system is due to the size of the refrigeration system and to provide easy service access.
In order for the quantum effects to play a role in computation, the quantum processor must operate in an extreme isolated environment. The refrigerator and many layers of shielding create an internal environment with a temperature close to absolute zero that is isolated from external magnetic fields, vibration, and external RF signals of any form.
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