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Collaboration is at the core of any important technological development. From the steam engine to the internet, humanity’s innovations interweave themselves between seemingly disparate communities. 

That said, new technologies don’t always work together. There are many who still remember how Mac floppy disks were incompatible with PC machines, and vice versa. 

Quantum computing is no different, which is why ҹɫֱ�� is a founding member of the new  announced today by the Linux Foundation. The QIR alliance is working hard to ensure this technology reaches its full potential.

The siloed nature of early quantum computing developments has protected vital intellectual property, but it has also created a separation of resources. Quantum software from one organization may not work on the hardware of another, which can be an enormous obstacle for researchers. 

The QIR Alliance is solving this problem by establishing an intermediate representation to enable interoperability within the quantum ecosystem. Based on the open source intermediate language, the QIR Alliance will create a standard set of rules for representing quantum constructs consistent with LLVM data model. 

In doing so, the QIR Alliance hopes to enable wider collaboration and a quantum community built around principals of interoperability. 

How does intermediate representation (IR) work? 

Although programming languages may look like machine speak to the untrained eye, these languages are for the human programmers. Intermediate representation approach splits the compilation process into two parts. A user language compiler converts human-readable program representation into IR. A hardware-specific compiler takes the IR and converts it into a set of machine-level instructions that the computer can understand. 

This approach allows a hardware-specific compiler to work with many different source languages and still give the machine adequate instructions that it can comprehend. Conversely, quantum programming language developers only need to compile their new languages to one IR representation to run on many different machines. This enables innovation on both sides of the ecosystem while avoiding duplication of effort.

Therefore, a compiler-level solution makes sense to achieve the collaborative goals the QIR Alliance has set out. 

LLVM is a collection of compiler and toolchain technologies that are designed around a language-independent intermediate representation. This common platform allows many source languages to share optimizers and executable generators, which enables a large amount of re-use in compiler machinery. 

In short, this should allow quantum hardware to work with more varieties of software than they previously could. Rather than having to rewrite software based on the specific machine researchers want to use, the QIR Alliance will allow much more collaboration from previously disparate organizations. 

An additional interesting part of LLVM is that it also facilitates integration with many languages and tools built for classical computation environments. While quantum and classical computers may seem like competing technologies, many researchers expect to see quantum and classical computing resources working together in the future. The use of LLVM will facilitate quantum and classical computations interaction at the hardware level. 

What’s the benefit? 

For an organization like ҹɫֱ��, the QIR Alliance offers several enticing advantages. 

To begin, this initiative will benefit the current quantum ecosystem. As the reality of quantum machines begins to truly materialize, it is no longer feasible for researchers to work with systems that are not interoperable. Much like how Mac floppy disks were once not compatible with PC machines, the quantum industry will need to come together to create a valuable product for the consumer. 

On top of this, the quantum sector must be constantly looking to the future and how this technology could improve and change in the coming years. All the major players within the quantum ecosystem must adopt a forward-thinking approach to intermediate representation that will fulfill the needs of current machines while also staying mindful of yet-to-be-developed hardware. 

Keeping an eye on the horizon is a goal of the QIR Alliance, and ҹɫֱ�� is fortunate to be a part of such an important step in quantum computing’s history. 

• Quantum Origin is the world’s first commercial product built using quantum computers that delivers an outcome that classical computers could not achieve
• Quantum Origin is the first platform to derive cryptographic keys using the output of a quantum computer to ensure data is protected at foundational level against evolving attacks
• It provides immediate protection to enterprises and governments from current security issues, arising from the use of weaker random number generators (RNGs)
• Quantum Origin also helps protect against ‘hack now, decrypt later’ attacks, which are already happening and will have future implications
• The quantum-enhanced cryptographic keys generated by Quantum Origin are based on verifiable quantum randomness and can be integrated into existing systems. The protocol relies on “entanglement”, a unique feature of quantum mechanics.
• Quantum Origin supports traditional algorithms, such as RSA or AES, as well as post-quantum cryptography algorithms currently being standardized by the National Institute for Standards and Technology (NIST)

Cambridge Quantum (CQ), the global leader in quantum software, and a wholly owned subsidiary of ҹɫֱ��, the world’s leading integrated quantum computing company, is pleased to announce that it is launching Quantum Origin – the world’s first commercially available cryptographic key generation platform based on verifiable quantum randomness. It is the first commercial product built using a noisy, intermediate-scale quantum (NISQ) computer and has been built to secure the world’s data from both current and advancing threats to current encryption.

Randomness is critical to securing current security solutions as well as protecting systems from the future threat of quantum attacks. These attacks will further weaken deterministic methods of random number generation, as well as methods that are not verifiably random and from a quantum source.

Today’s systems are protected by encryption standards such as RSA and AES. Their resilience is based on the inability to “break” a long string from a random number generator (RNG). Today’s RNGs, however, lack true, verifiable randomness; the numbers being generated aren’t as unpredictable as thought, and, as a result, such RNGs have been the point of failure in a growing number of cyber attacks. To add to this, the potential threat of quantum attacks is now raising the stakes further, incentivizing criminals to steal encrypted data passing over the internet, with a view to decrypting it later using quantum computers. So-called “hack now, decrypt later” attacks.

Quantum Origin is a cloud-hosted platform that protects against these current and future threats. It uses the unpredictable nature of quantum mechanics to generate cryptographic keys seeded with verifiable quantum randomness from ҹɫֱ��’s H-Series quantum computers, Powered by Honeywell. It supports traditional algorithms, such as RSA or AES, as well as post-quantum cryptography algorithms currently being standardized by the National Institute for Standards and Technology (NIST).

“We have been working for a number of years now on a method to efficiently and effectively use the unique features of quantum computers in order to provide our customers with a defense against adversaries and criminals now and in the future once quantum computers are prevalent,” said Ilyas Khan, CEO of ҹɫֱ�� and Founder of Cambridge Quantum. He added “Quantum Origin gives us the ability to be safe from the most sophisticated and powerful threats today as well threats from quantum computers in the future.”

Duncan Jones, head of cybersecurity at Cambridge Quantum, said: “When we talk about protecting systems using quantum-powered technologies, we’re not just talking about protecting them from future threats. From large-scale takedowns of organizations, to nation state hackers and the worrying potential of ‘hack now, decrypt later’ attacks, the threats are very real today, and very much here to stay. Responsible enterprises need to deploy every defense possible to ensure maximum protection at the encryption level today and tomorrow.”

Quantum-enhanced keys on demand

With Quantum Origin, when an organization requires quantum-enhanced keys to be generated, it can now make a call via an API. Quantum Origin generates the keys before encrypting them with a transport key and securely relaying them back to the organization. To give organizations a high-level of assurance that their encryption keys are as unpredictable as possible, Quantum Origin tests the entire output from the quantum computers, ensuring that each key is seeded from verifiable quantum randomness.

These keys are then simple and easy to integrate within customers' existing systems because they’re provided in a format that can be consumed by traditional cybersecurity systems and hardware. This end-to-end approach ensures key generation is on-demand and is capable of scaling with use, all while remaining secure.

Quantum Origin in practice

Quantum Origin keys should be used in any scenario where there is a need for strong cybersecurity. At launch, Cambridge Quantum will offer Quantum Origin to financial services companies and vendors of cybersecurity products before expanding into other high priority sectors, such as telecommunications, energy, manufacturing, defense and government.

The technology has already been used in a series of projects with launch partners. Axiom Space used Quantum Origin to conduct a test of post-quantum encrypted communication between the ISS and Earth — sending the message “Hello Quantum World” back to earth encrypted with post-quantum keys seeded from verifiable quantum randomness. Fujitsu integrated Quantum Origin into its software-defined wide area network (SDWAN) using quantum-enhanced keys alongside traditional algorithms.

For more information:
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— Learn more about Quantum Origin
— Discover our partners
— Explore our case studies
— Book a demo with our experts

Honeywell Quantum Solutions notched another important milestone this week with its trapped-ion quantum computing technology.

The System Model H1 became the first quantum computer to pass the Quantum Volume 1024 benchmark, a metric introduced by IBM to measure the overall capability and performance of a quantum computing system regardless of technology. (Calculating quantum volume requires a complex set of statistical tests.)

The achievement marks a new record for performance in terms of quantum volume, and the third set by the System Model H1 since it was launched in fall 2020. It also fulfills a promise made last summer that Honeywell Quantum Solutions would increase the quantum volume of its commercial offerings by an order of magnitude each year for the next five years.

“We achieved what we set out to do,” said Tony Uttley, president of Honeywell Quantum Solutions. “Our goal is to provide users with the most powerful hardware as they work on solving real world problems. We believe that being able to quantify the increases in capability is important."

This is the latest in a string of accomplishments for Honeywell Quantum Solutions, which recently announced it was combining with Cambridge Quantum Computing to form the largest stand-alone quantum computing company in the world.

Over the past year, the Honeywell team:

• Launched two commercial computing systems. The System Model H0 was released in June 2020 followed by the System Model H1 four months later.
• Set four industry records for quantum volume. The System Model H0 debuted with a then-record quantum volume of 64. The System Model H1 launched with a quantum volume of 128, a new record, and through system upgrades, passed the quantum volume benchmarks of 512 in March and now 1024 in July.
• Developed and demonstrated the holographic quantum dynamics (holoQUADS) algorithm, which can accurately simulate a quantum dynamics model with fewer qubits than traditional methods. The algorithm could lead to quantum computers running more complex scientific simulations sooner than expected.
• Completed repeated rounds of quantum error correction and demonstrated it can detect and correct quantum errors in real-time while a computation is running.
• Forged several new collaborations with enterprise partners and businesses, including DHL, BMW, Nippon Steel, Samsung, and others.
  

The new software development toolkit for quantum natural language processing tested and benchmarked on System Model H1 technology

Telling Alexa to play “Schrodinger’s Cat” by Tears for Fears. Asking Siri for directions to a quantum-themed bar or restaurant. A smart phone autocorrecting a word in a text message.

These are everyday applications of natural language processing – NLP for short – a field of artificial intelligence that focuses on training computers to understand words and conversations with the same reasoning as humans.

NLP technologies have advanced rapidly in recent years with the help of increasingly powerful computing clusters that can run language models that examine reams of text and count how often certain words appear. These models train devices to retrieve information, annotate text, translate words from one language to another, answer questions, and perform other tasks.

The next step is to “teach” computers to infer meaning, understand nuance, and grasp the context of conversations. To do that, however, requires massive computational resources and multiple algorithms or data structures.

A United Kingdom-based quantum computing company believes the answer lies with qubits, superposition, and entanglement.

Cambridge Quantum recently released , a new open-source software development toolkit, that enables researchers to convert sentences into quantum circuits that can be run on quantum computers. It is the first toolkit developed specifically for quantum natural language processing – or QNLP - and was tested on System Model H1 technology before it was released.

The software takes the text, parses it, and then uses linguistics and mathematics to differentiate between a verb, noun, preposition, adjectives, etc., and label them to understand the relationships between words.

Cambridge Quantum researchers tested 30 sentences on the System Model H1, which was able to classify words correctly 87 percent of the time.

“We deem that a success,” said Konstantinos Meichannetzidis, a member of the CQ team. “We found that our software works well with the Honeywell technology and were able to benchmark the performance of this quantum device.”

The lambeq project also represented a first for Honeywell Quantum Solutions. It was the first QNLP problem run on the System Model H1 hardware.

“We are really excited to be a part of this work and contribute to the development of this important toolkit,” said Tony Uttley, president of Honeywell Quantum Solutions. “Applications like this help us test our system and understand how well it performs solving different problems.”

(Honeywell Quantum Solutions and Cambridge Quantum have a long-standing history of partnering together on research and other projects that benefit end-customers. The two entities announced in June they are seeking regulatory approval to combine to form a new company.)

Why QNLP?

For humans, decoding conversations to understand meaning is a complex process. We infer meaning through tone of voice, body language, context, location, and other factors. For computers, which do not rely on heuristics, decoding language is even more complex.

The only way to create some sort of “meaning-aware” NLP is to explicitly encode compositional, semantic sentence structure into language models. To do this on a classical computer, however, requires massive computational resources, which are costly, and would likely still take months to process.

Quantum computers, on the other hand, run calculations and crunch data very differently.

They harness unique properties of quantum physics, specifically superposition and entanglement, to store and process information. Because of that, these systems can examine problems with multiple states and evaluate a large space of possible answers simultaneously.

What this means in terms of natural language processing is that quantum computers are likely to go beyond counting how often certain words appear or are used together. As noted above, quantum computers can identify words, label them as a noun, verb, preposition, etc., and understand the relationship between words. (lambeq uses the Distributional Compositional Categorical – or DisCoCat – model to do this.)

This enables the computer to infer meaning, and also provides insight into how and why the computer made connections between words. The latter is important for validating data and also expanding the use of QNLP in regulated sectors such as finance, legal, and medicine where transparency is critical.

Built upon previous work

The Cambridge Quantum team has long explored how quantum computing can advance natural language processing, and has published extensively on the topic.

In , researchers released two foundational papers that demonstrated that QNLP is inherently meaning-aware and can successfully interpret questions and respond.

Earlier this year, the team performed conducted on a quantum computer by converting more than 100 sentences into quantum circuits using an IBM technology. Researchers successfully trained two NLP models to classify words in sentences.

The release of lambeq and the testing of the open-source toolkit on the Honeywell System Model H1 represents the next steps in their QNLP efforts.

“Our team has been involved in foundational work that explores how quantum computers can be used to solve some of the most intractable problems in artificial intelligence,” said Bob Coecke, Cambridge Quantum’s chief scientist.

“In various papers published over the course of the past year,” Coecke added, “We have not only provided details on how quantum computers can enhance NLP but also demonstrated that QNLP is ‘quantum native,’ meaning the compositional structure governing language is mathematically the same as that governing quantum systems. This will ultimately move the world away from the current paradigm of AI that relies on brute force techniques that are opaque and approximate.”

An algorithm developed by researchers at Honeywell Quantum Solutions could lead to quantum computers running more complex scientific simulations sooner than expected.

The Honeywell team that its holographic quantum dynamics (holoQUADS) algorithm accurately simulated a quantum dynamics model with fewer qubits than traditional methods. The algorithm used nine qubits to simulate 32 “spins” – or localized electrons. Traditional methods require one qubit per spin.

The demonstration, led by Eli Chertkov, has important implications. Simulating quantum dynamics is a promising application for quantum computers. However, many predict quantum computers will need hundreds or thousands of qubits to run simulations too complex for classical computers.

The holoQUADS algorithm could change that.

“This algorithm allows us to run more complex simulations with less than a third of the qubits,” said Tony Uttley, president of Honeywell Quantum Solutions. “This is an exciting achievement that gets us closer to quantum computers solving real-world problems that classical computers cannot.”

Borrowed from the classical world

Scientists have long sought to better understand how atoms and subatomic particles move, behave, and interact (known as quantum mechanics) and react when disturbed (quantum dynamics).

Such knowledge is critical to the development of new vaccines and gene therapies, and the discovery of novel materials that are stronger, longer lasting, or better conductors of heat or electricity.

Currently, it is impossible to fully simulate the quantum dynamics of systems larger than a few atoms, and many believe it always will be. Classical computers crunch data by manipulating ones and zeroes and represent states as “off” or “on.” Atoms and subatomic particle exist in multiple states and move and behave in different ways.

This is what led to famed American physicist Richard Feynman postulating in the 1980s that only computers that are quantum in nature can adequately simulate quantum dynamics. 

That is not to say computational scientists do not have tricks to model some aspects of quantum dynamics on classical computers. They have developed powerful algorithms such as tensor networks to approximate quantum states.

In fact, the holoQUADS algorithm is based on tensor networks. These mathematical tools compress data and scientists use them to study the quantum nature of different materials.

The Honeywell team published a paper last May detailing the steps necessary to adapt tensor networks for a quantum computer and how to extend them to simulate dynamics. They published a second paper explaining how quantum tensor networks can measure the degree to which parts of a system are entangled, or entanglement entropy, which is used for studying topological properties of materials. 

The recent demonstration showed the dynamics algorithm described in the original paper is not only efficient but can return quantitatively accurate results with trapped-ion hardware available right now. 

Tested and verified

The Honeywell team tested the algorithm by simulating the chaotic dynamics of the “kicked” Ising model, a mathematical framework used to study chaos and thermalization in strongly interacting quantum systems. The results mirrored those generated by simulations on classical computers.

The demonstration served as an important benchmark and will help the team verify performance and accuracy as they scale the algorithm and quantum hardware.

“The model we simulated is a perfect test of the algorithm because it behaves in many ways like a typical chaotic quantum system, but it has a very special feature that lets us check the results classically,” said Dr. Michael Foss-Feig, a physicist who helped develop the algorithm.

Chertkov, Foss-Feig, and the other co-authors are excited by how well the algorithm worked in the real world, and by the performance of the System Model H1. The algorithm relies on mid-circuit measurement and qubit reuse, techniques first demonstrated by Honeywell. The H1 is adept at both.  And because of the H1’s high fidelities, the raw data had less “noise” than other state-of-the art simulations.

“The QCCD architecture at the heart of System Model H1 enables high-fidelity qubit reset and mid-circuit measurements with very low crosstalk errors,” said Justin Bohnet, one of the co-authors who led the hardware team. “Those features, along with the long coherence times and high-fidelity gates provided by trapped-ion qubits, are enabling creative advances in the study of quantum systems, as shown by this the holoQUADS demonstration.”

The online tool adapts quantum circuits to run optimally on different quantum computing technologies, with easy switching between Honeywell’s system and other hardware platforms.

Cambridge Quantum recently announced it has made the source code for , its quantum software development kit, fully open to the quantum software community.

The move, which comes just months after the company began providing free access to , is expected to benefit software developers as well as Honeywell Quantum Solutions and other hardware providers.

Most of the programming languages or quantum software development kits available were designed initially to run on certain hardware platforms, creating compatibility issues. Software developers who wanted to test circuits or algorithms on different quantum technologies had to rewrite or tweak code to run on a new system.

Providing access to and its source code makes it easier for developers to do that.

“Users need only to focus on developing their quantum applications, not rewriting code around the idiosyncrasies of any particular hardware,” said Dr. Ross Duncan, head of software at Cambridge Quantum.

And for Honeywell Quantum Solutions and other hardware providers, broadens access to their technologies by enabling developers to move more seamlessly between systems. The software development kit is optimized for each commercial hardware system, including Honeywell’s trapped-ion quantum computer.

“We want to make it as easy as possible for the quantum software community to run circuits and algorithms on our trapped-ion quantum computers,” said Tony Uttley, president of Honeywell Quantum Solutions. “The System Model H1 technology is the highest performing quantum system available, and we want them to experience that.”

Honeywell Quantum Solutions and Cambridge Quantum have a long-standing history of partnering together for the benefit of end-customers. (The two entities announced in June they are seeking regulatory approval to combine to form a new company.)

"Having TKET fully open-sourced provides an incredible tool to the world’s quantum algorithm developers, including Honeywell," Uttley said.

“Our products and offerings have always been complementary and continue to be,” he said. “(Cambridge Quantum) has developed a suite of tools and programs that interface well with our hardware.”

How TKET works

If you have ever traveled to another country and tried plugging something in, you’ve likely discovered the need for an adapter. Electrical outlets vary and the plug-ins used in the United States don't always work in Europe or other countries and vice versa.

The same is true with today’s early-stage quantum computers. Each technology has its own performance specifications, API (an interface that enables different computing systems to talk to one another), and compiler (a program that translates code written in one computing language to another).

is versatile. Developers can use it to create circuits or algorithms and also to serve as a universal connector or adapter between hardware and software platforms.

Cambridge Quantum has developed extensions, which are Python modules, for each commercial quantum hardware platform available. These extensions enable developers to code in Qiskit, Cirq, or another language and automatically adapt their circuits or algorithms to work on different quantum devices or simulators without having to tweak it themselves.

And now that is open source, developers can create their own extensions to the codebase and bridge between platforms.