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Communication is the connective tissue of society, weaving individuals into groups and communities and mediating the progress and development of culture. The technology of communications evolves continuously, occasionally undergoing paradigm shifts such as those brought about by the Gutenberg press and broadcast television.

From historical examples such as the proliferation of fast merchant trading ships, to the modern telecommunications networks spread across the world via a web of cables buried under the sea floor and satellites thousands of kilometres high, the need for better communication infrastructure has driven some of our most ambitious technologies to date.��

Today, emerging quantum technologies are poised to revolutionise the field of communication once again. They promise new and incredibly valuable opportunities for dependable and secure communications between people, communities, companies, and governments everywhere. Our ability to understand and control quantum systems has opened a new world of exciting possibilities. Soon we might build long-distance quantum communication links and networks, eventually leading to what is known as the quantum internet.��

While some embryonic quantum communication systems are already in place, realisation of their full potential will require significant technological advances. With engineering teams around the world working at pace to deliver this promise across industrial sectors, the need to invest in expert knowledge is rising.��

NASA has been a pioneer in space-based communication over many decades, and more recently has emerged as a leader in space-based quantum communication, dedicating new resources for scientists, engineers and communication systems experts to learn about the field.

Recently, NASA’s Space Communications and Navigation (SCaN) program commissioned a booklet titled , authored by several of our team at ҹɫֱ��. This will be a go-to resource for the global community of scientists and experts that NASA supports, but importantly it has been written so that it requires almost no prior technical knowledge while providing a rigorous account of the emerging field of quantum communications.

What follows is a taster of what’s in Quantum Communication 101.

What is quantum communication?

For the words I am typing now to reach your computer screen, I need to rely on modern communication networks. My laptop memory, Wi-Fi router and communication channels rely on the physics of things like transistors, currents, and radio waves which obey the more familiar, “classical" laws of physics.��

The field of quantum communication, however, relies on the counterintuitive rules of quantum physics. Thanks to incredible feats of engineering, in place of continuous beams of light from diodes, we can now control individual photons to send and receive quantum information. By taking advantage of the peculiar quantum phenomena that they exhibit, like superposition and entanglement, new possibilities are emerging which were previously unimaginable.��

Cutting-edge applications 

In the growing landscape of potential applications in quantum communication, cybersecurity is already deeply rooted. At ҹɫֱ��, for example, quantum computers are used to generate randomness, the fundamental building block of secure encryption. Elsewhere, prototype quantum networks for secure communications already span metropolitan areas.��

As our techniques in quantum communication advance, we may unlock new possibilities in quantum computing, which promises to solve problems too difficult even for supercomputers, and quantum metrology, which will enable measurements at an unprecedented precision. Quantum states of light have already been used in LIGO - a large-scale experiment operated by CalTech and MIT to detect ripples in the fabric of space-time itself.

Connecting the dots: towards a quantum internet 

The end goal of quantum communication is what many refer to as the quantum internet, through which we will seamlessly send quantum signals across many quantum networks. This will be an enormous engineering challenge, requiring international collaboration and the evolution of our existing infrastructure.

Although the exact form that this network will take is yet unknown, we can say with confidence that it will need to pass through space. Much like satellites help to globally connect the Internet, the launch of quantum-capable satellites will play a vital role in a global quantum internet.��

Building a quantum ecosystem

The path to a quantum internet will depend on growing a diverse and expert workforce. This is well understood by bodies such as the National Science Foundation who recently announced a $5.1M Center for Quantum Networks aimed at architecting the quantum internet. Over the last few years, we have seen growing investment worldwide, such as the $1.1B Quantum Technology Flagship in Europe and the $11B Chinese National Laboratory for Quantum Information Science. Important industrial investments are being made by large corporations such as IBM, Google, Intel, Honeywell, Cisco, Amazon, and Microsoft.

Amongst this surge in interest, NASA’s SCaN program has proposed a series of mission concepts for building and testing infrastructure for space-based quantum communication. These include launching satellites capable of sending and receiving quantum signals between ground stations and eventually other satellites.��These quantum signals may be entangled photons – a feature that will play an extremely important role in future networks. One such mission concept is shown below, where a quantum-capable satellite with a source of entangled photons connects an intercontinental quantum network.

The second quantum revolution is at an exciting precipice where our ability to transmit quantum information, both on Earth and in space, will be pivotal. Whilst our evolving quantum technologies already show a great deal of promise, it is perhaps the ground-breaking applications that we are yet to discover which will ultimately determine our success.��

It is more important than ever that we support education and collaboration in advancing quantum technologies. Quantum Communication 101 aims to be a starting point for a general audience looking to learn about the topic for the first time, as well as those who wish to explore in detail the technologies that will make the first quantum networks a reality.

‍If you would like to better understand the exciting prospects of quantum communication, you can find the Quantum Communication 101 booklet on the NASA SCaN website.��

Quantum computing promises to help us understand chemistry in its purest form – ultimately leading to a better understanding of everything from drug development to superconductors. But before we can do any of that, researchers in computational quantum chemistry have to create the basic building blocks for understanding a chemical system: they must prepare the initial state of a system, apply various effects to the system through time, then measure the resulting output.��

The first problem, called “state preparation” is a tricky one – researchers have been leaning heavily on “variational” techniques to do this, but those techniques come with huge optimization costs in addition to serious scaling issues for larger systems. An older technique, called “adiabatic state preparation” promises significant speedups on quantum computers vs classical computers, but has been mostly abandoned by researchers because the typical method used for time evolution is costly and introduces too much noise. This method, called “Trotterized adiabatic time evolution”, involves splitting up time into discrete steps, which requires many, many gates, and ultimately needs error rates well out of reach for any near-term quantum computer.

Recently, researchers at ҹɫֱ�� found a way around that roadblock – they eliminated the noisy time evolution in favor of a clever averaging approach. Rather than taking a bunch of discrete time steps they simulate different interactions such that on average you get exactly the right time evolution. A nice aspect of this approach is that it has guaranteed “convergence” – ultimately this means that, unlike other approaches, it works all the time. This new approach has also been shown to be possible on near-term quantum computers: it does not require too many gates or computational time, and it scales well with the system size.��

This algorithm is designed with ҹɫֱ��’s world-leading hardware in mind, as it requires all-to-all connectivity. Combined with our industry-leading gate fidelities, this new approach is opening the door to many fascinating applications in chemistry, physics, and beyond.

The marriage of AI and quantum computing holds big promise: the computational power of quantum computers could lead to huge breakthroughs in this next-gen tech. A team led by Stephen Clark, our Head of AI, has just helped us move towards unlocking this incredible potential.

A key ingredient in contemporary classical AI is the “transformer”, which is so important it is actually the “T” in ChatGPT. Transformers are machine learning models that do things like predict the next word in a sentence, or determine if a movie review is positive or negative. Transformers are incredibly well-suited to classical computers, taking advantage of the massive parallelism afforded by GPUs. These advantages are not necessarily present on quantum computers in the same way, so successfully implementing a transformer on quantum hardware is no easy task.

Until recently, most attempts to implement transformers on quantum computers took a sort of “copy-paste” approach – taking the math from a classical implementation and directly implementing it on quantum circuits. This “copy-paste” approach fails to account for the considerable differences between quantum and classical architectures, leading to inefficiencies. In fact, they are not really taking advantage of the ‘quantum’ paradigm at all.

This has now changed. In a new paper on the arXiv, our team introduces an explicitly quantum transformer, which they call “Quixer” (short for quantum mixer). Using quantum algorithmic primitives, the team created a transformer implementation that is specially tailored for quantum circuits, making it qubit efficient and providing the potential to offer speedups over classical implementations.

Critically, the team then applied it to a practical language modelling task (by simulating the process on a classical computer), obtaining results that are competitive with an equivalent classical baseline. This is an incredible milestone achievement in and of itself.

This paper also marks the first quantum machine learning model applied to language on a realistic rather than toy dataset. This is a truly exciting advance for anyone interested in the union of quantum computing and artificial intelligence. About a week ago when we announced that our System Model H2 has bested the quantum supremacy experiments first benchmarked by Google, we promised a summer of important advances in quantum computing. Stay tuned for more disclosures!

For a quantum computer to be useful, it must be universal, have lots of qubits, and be able to detect and correct errors. The error correction step must be done so well that in the final calculations, you only see an error in less than one in a billion (or maybe even one in a trillion) tries. Correcting errors on a quantum computer is quite tricky, and most current error correcting schemes are quite expensive for quantum computers to run.

We’ve teamed up with researchers at the University of Colorado to – bringing the era of quantum ‘fault tolerance’ closer to reality. Current approaches to error correction involve encoding the quantum information of one qubit into several entangled qubits (called a “logical” qubit). Most of the encoding schemes (called a “code”) in use today are relatively inefficient – they can only make one logical qubit out of a set of physical qubits. As we mentioned earlier, we want lots of error corrected qubits in our machines, so this is highly suboptimal – a “low encoding rate” means that you need many, many more physical qubits to realize a machine with lots of error corrected logical qubits.

Ideally, our computers will have “high-rate” codes (meaning that you get more logical qubits per physical qubit), and researchers have identified promising schemes known as “non-local qLDPC codes”. This type of code has been discussed theoretically for years, but until now had never been realized in practice. In a , the joint team has implemented a high rate non-local qLDPC code on our H2 quantum processor, with impressive results.��

The team used the code to create 4 error protected (logical) qubits, then entangled them in a “GHZ state” with better fidelity than doing the same operation on physical qubits – meaning that the error protection code improved fidelity in a difficult entangling operation. The team chose to encode a GHZ state because it is widely used as a system-level benchmark, and its better-than-physical logical preparation marks a highly mature system.

It is worth noting that this remarkable accomplishment was achieved with a very small team, half of whom do not have specialized knowledge about the underlying physics of our processors. Our hardware and software stack are now so mature that advances can be achieved by “quantum programmers” who don’t need advanced quantum hardware knowledge, and who can run their programs on a commercial machine between commercial jobs. This places us bounds ahead of the competition in terms of accessibility and reliability.

This paper marks the first time anyone has entangled 4 logical qubits with better fidelity than the physical analog. This work is in strong synergy with our recent announcement in partnership with Microsoft, where we demonstrated logical fidelities better than physical fidelities on entangled bell pairs and demonstrated multiple rounds of error correction.��These results with two different codes underscore how we are moving into the era of fault-tolerance ahead of the competition.

The code used in this paper is significantly more optimized for architectures capable of moving the qubits around, like ours. In practice, this means that we are capable of “non-local” gates and reconfigurability. A big advantage in particular is that some of the critical operations amount to a simple relabeling of the individual qubits, which is virtually error-free.

The biggest advantage, however, is in this code’s very high encoding rate. Unlike many codes in use today, this code offers a very high rate of logical qubits per physical qubit – in fact, the number of logical qubits is proportional to the number of physical qubits, which will allow our machines to scale much more quickly than more traditional codes that have a hard limit on the number of logical qubits one can get in each code block. This is yet another proof point that our machines will scale effectively and quickly.

The Artificial Intelligence (AI) systems that have recently permeated our lives have a serious problem: they are built in a way that makes them very hard - and sometimes impossible - to understand or interpret. Luckily, our team is tackling this problem, and we’ve just published that covers the issue in detail.

It turns out that the lack of explainability in machine learning (ML) models, such as ChatGPT or Claude, comes from the way that the systems are built. Their underlying architecture (a neural network) lacks coherent structure. While neural networks can be trained to effectively solve certain tasks, the way they do it is largely (or, from a practical standpoint, almost wholly) inaccessible. This absence of interpretability in modern ML is increasingly a major concern in sensitive areas where accountability is required, such as in finance and the healthcare and pharmaceutical sectors. The “interpretability problem in AI” is therefore a topic of grave worry for large swathes of the corporate and enterprise sector, regulators, lawmakers, and the general public.��

These concerns have given birth to the field of eXplainable AI, or XAI, which attempts to solve the interpretability problem through so-called ‘post-hoc’ techniques (where one takes a trained AI model and aims to give explanations for either its overall behavior or individual outputs). This approach, while still evolving, has its own issues due to the approximate nature and fundamental limitations of post-hoc techniques.�� 

The second approach to the interpretability problem is to employ new ML models that are, by design, inherently interpretable from the start. Such an interpretable AI model comes with explicit structure which is meaningful to us “from the outside”. Realizing this in the tech we use every day means completely redesigning how machines learn - creating a new paradigm in AI. As Sean Tull, one of the authors of the paper, stated: “In the best case, such intrinsically interpretable models would no longer even require XAI methods, serving instead as their own explanation, and one of a deeper kind.”

At ҹɫֱ��, we’re continuing work to develop new paradigms in AI while also working to sharpen theoretical and foundational tools that allow us all to assess the interpretability of a given model. In , we present a new theoretical framework for both defining AI models and analyzing their interpretability. With this framework, we show how advantageous it is for an AI model to have explicit and meaningful compositional structure.

The idea of composition is explored in a rigorous way using a mathematical approach called “category theory”, which is a language that describes processes and their composition. The category theory approach to interpretability can be accomplished via a graphical calculus which was also developed in part by ҹɫֱ�� scientists, and which is finding use cases in everything from gravity to quantum computing.��

A fundamental problem in the field of XAI has been that many terms have not been rigorously defined, making it difficult to study - let alone discuss - interpretability in AI. Our paper presents the first known theoretical framework for assessing the compositional interpretability of AI models.��With our team’s work, we now have a precise and mathematically defined definition of interpretability that allows us to have these critical conversations.    

After developing the framework, our team used it to analyze the full spectrum of ML approaches. We started with Transformers (the “T” in ChatGPT), which are not interpretable – pointing to a serious issue in some of the world’s most widely used ML tools. This is in contrast with (sparse) linear models and decision trees, which we found are indeed inherently interpretable, as they are usually described.  

Our team was also able to make precise how other ML models were what they call 'compositionally interpretable'. These include models already studied by our own scientists including models, causal models, and .��������

Many of the models discussed in this paper are classical, but more broadly the use of category theory and string diagrams makes these tools very well suited to analyzing quantum models for machine learning. In addition to helping the broader field accurately assess the interpretability of various ML models, the seminal work in this paper will help us to develop systems that are interpretable by design.��

This work is part of our broader AI strategy, which includes , and – in this case - using the tools of category theory and compositionality to help us better understand AI.��

“Computers are useless without error correction”
- Anonymous

If you stumble while walking, you can regain your balance, recover, and keep walking. The ability to function when mistakes happen is essential for daily life, and it permeates everything we do. For example, a windshield can protect a driver even when it’s cracked, and most cars can still drive on a highway if one of the tires is punctured. In fact, most commercially operated planes can still fly with only one engine. All of these things are examples of what engineers call “fault-tolerance”, which just describes a system’s ability to tolerate faults while still functioning.

When building a computer, this is obviously essential. It is a truism that errors will occur (however rarely) in all computers, and a computer that can’t operate effectively and correctly in the presence of faults (or errors) is not very useful. In fact, it will often be wrong - because errors won’t be corrected.

In from ҹɫֱ��’s world class quantum error correction team, we have made a hugely significant step towards one of the key issues faced in quantum error correction – that of executing fault-tolerant gates with efficient codes.��

This work explores the use of “genon braiding” – a cutting-edge concept in the study of topological phases of matter, motivated by the mathematics of category theory, and both related to and inspired by our prior groundbreaking work on .��

The native fault tolerant properties of braided toric codes have been theoretically known for some time, and in this newly published work, our team shares how they have discovered a technique based on “genon braiding” for the construction of logical gates which could be applied to “high rate” error correcting codes – meaning codes that require fewer physical qubits per logical qubit, which can have a huge impact on scaling.

Stepping along the path to fault-tolerance

In classical computing, building in fault-tolerance is relatively easy. For starters, the hardware itself is incredibly robust and native error rates are very low. Critically, one can simply copy each bit, so errors are easy to detect and correct.��

Quantum computing is, of course, much trickier with challenges that typically don’t exist in classical computing. First off, the hardware itself is incredibly delicate. Getting a quantum computer to work requires us to control the precise quantum states of single atoms. On top of that, there’s a law of physics called the no cloning theorem, which says that you can’t copy qubits. There are also other issues that arise from the properties that make quantum computing so powerful, such as measurement collapse, that must be considered.

Some very distinguished scientists and researchers have thought about quantum error correcting including Steane, Shor, Calderbank, and Kitaev [ ].�� They realized that you can entangle groups of physical qubits, store the relevant quantum information in the entangled state (called a “logical qubit”), and, with a lot of very clever tricks, perform computations with error correction.

There are many different ways to entangle groups of physical qubits, but only some of them allow for useful error detection and correction. This special set of entangling protocols is called a “code” (note that this word is used in a different sense than most readers might think of when they hear “code” - this isn’t “Hello World”).��

A huge amount of effort today goes into “code discovery” in companies, universities, and research labs, and a great deal of that research is quite bleeding-edge. However, discovering codes is only one piece of the puzzle: once a code is discovered, one must still figure out how to compute with it. With any specific way of entangling physical qubits into a logical qubit you need to figure out how to perform gates, how to infer faults, how to correct them, and so on. It’s not easy!

ҹɫֱ�� has one of the world’s leading teams working on error correction and has broken new ground many times in recent years, often with industrial or scientific research partners. Among many firsts, . This included many milestones: repeated real-time error correction, the ability to perform quantum "loops" (repeat-until-success protocols), and real-time decoding to determine the corrections during the computation. In one of our most recent demonstrations, in partnership with Microsoft, we supported the use of error correcting techniques to achieve , confirming our place at the forefront of this research – and indeed confirming that ҹɫֱ��’s H2-1 quantum computer was the first – and at present only – device in the world capable of what Microsoft characterizes as Level 2 Resilient quantum computing.��

Introducing new, exotic error correction codes

While codes like the Steane code are well-studied and effective, our team is motivated to investigate new codes with attractive qualities. For example, some codes are “high-rate”, meaning that you get more logical qubits per physical qubit (among other things), which can have a big impact on outlooks for scaling – you might ultimately need 10x fewer physical qubits to perform advanced algorithms like Shor’s.��

Implementing high-rate codes is seductive, but as we mentioned earlier we don’t always know how to compute with them. A particular difficulty with high-rate codes is that you end up sharing physical qubits between logical qubits, so addressing individual logical qubits becomes tricky. There are other difficulties that come from sharing physical qubits between logical qubits, such as performing gates between different logical qubits (scientists call this an “inter-block” gate).

One well-studied method for computing with QEC codes is known as “braiding”. The reason it is called braiding is because you move particles, or “braid” them, around each other, which manipulates logical quantum information. In , we crack open computing with exotic codes by implementing “genon” braiding. With this, we realize a paradigm for constructing logical gates which we believe could be applied to high-rate codes (i.e. inter-block gates).

What exactly “genons” are, and how they are braided, is beautiful and complex mathematics - but the implementation is surprisingly simple. Inter-block logical gates can be realized through simple relabeling and physical operations. “Relabeling”, i.e. renaming qubit 1 to qubit 2, is very easy in ҹɫֱ��’s QCCD architecture, meaning that this approach to gates will be less noisy, faster, and have less overhead. This is all due to our architectures’ native ability to move qubits around in space, which most other architectures can’t do.��

Using this framework, our team delivered a number of proof-of-principle experiments on the H1-1 system, demonstrating all single qubit Clifford operations using genon braiding. They then performed two kinds of two-qubit logical gates equivalent to CNOTs, proving that genon braiding works in practice and is comparable to other well-researched codes such as the Steane code.

What does this all mean? This work is a great example of co-design – tailoring codes for our specific and unique hardware capabilities. This is part of a larger effort to find fault-tolerant architectures tailored to ҹɫֱ��'s hardware. ҹɫֱ�� scientist and pioneer of this work, Simon Burton, put it quite succinctly: “Braiding genons is very powerful. Applying these techniques might prove very useful for realizing high-rate codes, translating to a huge impact on how our computers will scale.”