Quantum computers might eventually be able to handle some AI applications that currently require huge amounts of conventional computing power. Such a development would be a major boost to machine learning and similar artificial intelligence algorithms. Quantum computers hold the promise of eventually being able to complete certain calculations that are impossible for conventional computers. For years, researchers have been debating whether these advantages over conventional computers extend to tasks that involve lots of data, and the algorithms that learn from them - in other words, the machine learning that underlies many AI programs. Now, Hsin-Yuan Huang at the quantum computing firm Oratomic and his colleagues argue that the answer ought to be "yes". Their mathematical work aims to lay the foundations for a future where quantum computers offer a broad boost to AI. "Machine learning is really utilised everywhere in science and technology and also everyday life.

In quantum computers, the information is contained in qubits that can work through vast numbers of different outcomes, which is not possible with classical computers. In quantum computers, the information is contained in qubits that can work through vast numbers of different outcomes, which is not possible with classical computers. The UK will not let quantum computing talent slip through its fingers and must learn lessons from US dominance of the AI race, the technology secretary has said, as the government announced a £1bn quantum funding pledge. Liz Kendall said the government hoped to retain homegrown quantum startups, engineers and researchers rather than lose them to competing countries, with the US stealing a march on its western rivals in AI. "I do look at what's happened on AI," said Kendall. "I do think we need to learn the lessons and make sure we give our brilliant scientists, spinouts and startups the ability to stay here and make it happen. And that requires a government that is bold and ambitious and confident in these technologies of the future."

Do quantum computers offer a way to vastly improve agriculture? As quantum computers continue to advance, identifying problems they can solve faster than the world's best conventional computers is becoming increasingly important - but it turns out that a key task held up as a future goal by quantum proponents may not need a quantum computer at all. The task in question involves a molecule called FeMoco, which plays a vital role in making life on Earth possible. That is because it is part of the process of nitrogen fixation, in which microbes convert atmospheric nitrogen into ammonia, making it biologically accessible to most other living organisms. How exactly FeMoco works during this process is complicated and not fully understood, but if we could crack it and replicate it on an industrial scale, it could drastically cut the energy involved in producing fertilisers, potentially leading to a boost in crop yields.

One of the most complex quantum networks built to date would allow 18 people to communicate securely thanks to the power of quantum physics. The researchers behind the work say it offers a practical path to building a global quantum internet, but others are sceptical. The long-promised quantum internet would allow quantum computers to communicate at distance by exchanging particles of light called photons that have been linked together by quantum entanglement . It would also allow networks of quantum sensors to be linked, or classical computers to send and receive unhackable communications. But wiring together a quantum world isn't as simple as laying down cables, because ensuring that one node of the network can be entangled with another is a challenge.

Quantum computing technology is advancing rapidly. Yet, even accounting for these trends, a quantum leap would be needed for quantum computers to meaningfully impact deep learning over the coming decade or two. We arrive at this conclusion based on a first-of-its-kind survey of quantum algorithms and how they match potential deep learning applications. This survey reveals three important areas where quantum computing could potentially accelerate deep learning, each of which faces a challenging roadblock to realizing its potential. First, quantum algorithms for matrix multiplication and other algorithms central to deep learning offer small theoretical improvements in the number of operations needed, but this advantage is overwhelmed on practical problem sizes by how slowly quantum computers do each operation. Second, some promising quantum algorithms depend on practical Quantum Random Access Memory (QRAM), which is underdeveloped. Finally, there are quantum algorithms that offer large theoretical advantages, but which are only applicable to special cases, limiting their practical benefits. In each of these areas, we support our arguments using quantitative forecasts of quantum advantage that build on the work by Choi et al. [2023] as well as new research on limitations and quantum hardware trends. Our analysis outlines the current scope of quantum deep learning and points to research directions that could lead to greater practical advances in the field.

What makes a quantum computer good? Claims that one quantum computer is better than another rest on terms like quantum advantage or quantum supremacy, fault-tolerance or qubits with better coherence - what does it all mean? Eleven years ago, I was just getting a start on my PhD in theoretical physics, and to be honest with you I never thought about quantum computers, or writing about them, at all. Meanwhile, staff were hard at work putting together the world's first " Quantum computer buyer's guide " (we've always been ahead of the curve). Looking through it reveals what a different time it was - John Martinis at University of California, Santa Barbara got a shout out for working on an array of only nine qubits, and just last week he was awarded the Nobel Prize in Physics .

It measures just 4cm squared but it possesses almost inconceivable speed. That's 10 septillion years, a number that far exceeds the age of our known universe and has the scientists behind the latest quantum computing breakthrough reaching for a distinctly non-technical term: "mindboggling". The new chip, called Willow and made in the California beach town of Santa Barbara, is about the dimensions of an After Eight mint, and could supercharge the creation of new drugs by greatly speeding up the experimental phase of development. Reports of its performance follow a flurry of results since 2021 that suggest we are only about five years away from quantum computing becoming powerful enough to start transforming humankind's capabilities to research and develop new materials from drugs to batteries, one independent UK expert said. Governments around the world are pouring tens of billions of dollars into research.

Based on the linearity of quantum unitary operations, we propose a method that runs the parameterized quantum circuits before encoding the input data. It enables a dataset owner to train machine learning models on quantum cloud computation platforms, without the risk of leaking the information of the data. It is also capable of encoding a huge number of data effectively at a later time using classical computations, thus saving the runtime on quantum computation devices. The trained quantum machine learning model can be run completely on classical computers, so that the dataset owner does not need to have any quantum hardware, nor even quantum simulators. Moreover, the method can mitigate the encoding bottom neck by reducing the required circuit depth from $O(2^{n})$ to $n/2$. These results manifest yet another advantage of quantum and quantum-inspired machine learning models over existing classical neural networks, and broaden the approaches for data security.

It is a well-known story that theoretical computer science and biology have been drawing inspiration from each other for decades. While computer science has tried to mimic the functioning of living systems to develop computing models, including automata, artificial neural networks, and evolutionary algorithms, biology has used computing as a metaphor to explain the functioning of living systems.4 For example, biologists have used Boolean logic to conceptualize gene regulation since early 1970s, when Jacques Monod wrote the inspirational statement "… like the workings of computers."40 This article contends that information processing is the link between computer science and molecular biology. In computer science, a model of computation such as finite state machines or Turing machines defines how to generate output from a set of inputs and a set of rules or instructions.

We show that language model finetuning can be improved, sometimes dramatically, with a simple augmentation. NEFTune adds noise to the embedding vectors during training. Standard finetuning of LLaMA-2-7B using Alpaca achieves 29.79% on AlpacaEval, which rises to 64.69% using noisy embeddings. NEFTune also improves over strong baselines on modern instruction datasets. Models trained with Evol-Instruct see a 10% improvement, with ShareGPT an 8% improvement, and with OpenPlatypus an 8% improvement. Even powerful models further refined with RLHF such as LLaMA-2-Chat benefit from additional training with NEFTune. The ability of LLMs to follow detailed instructions is vital to their usefulness. Generative language models are typically trained on raw web data, and then subsequently fine-tuned on a comparatively small but carefully curated set of instruction data. Instruction fine-tuning is crucial to taming the power of LLMs, and the usefulness of a model is largely determined by our ability to get the most out of small instruction datasets. In this paper, we propose to add random noise to the embedding vectors of the training data during the forward pass of fine-tuning. We show that this simple trick can improve the outcome of instruction fine-tuning, often by a large margin, with no additional compute or data overhead. Noisy Embedding Instruction Fine Tuning (NEFTune), while simple, has a strong impact on downstream conversational quality. When a raw LLM like LLaMA-2-7B is finetuned with noisy embeddings, its performance on AlpacaEval improves from 29.8% to 64.7% (Figure 1) - an impressive boost of around 35 percentage points (Touvron et al., 2023b; Dubois et al., 2023). NEFTune leads to this surprising and large jump in performance on conversational tasks, maintaining performance on factual question answering baselines. This technique seems to be a free lunch for LLM fine-tuning. NEFTune leads to massive performance boosts across all of these datasets, showcasing the increased conversational quality of the generated answers. The earliest forms of instruction finetuning such as FLAN and T0 (Sanh et al., 2021; Wei et al., 2021) focused on cross-task generalization in language models. Encoder-decoder language models were finetuned on a broad range of NLP tasks (about 100) and then evaluated on a set of different tasks. This was later scaled up to include thousands of tasks, seeing further improvement over the original FLAN (Chung et al., 2022; Xu et al., 2022). Although these works showed that LLMs could be easily adapted to solve simple and classical NLP tasks, real-world scenarios require LLMs to provide free-form answers to open-ended queries. InstructGPT (Ouyang et al., 2022) was the first model to tackle open-ended queries with impressive performance. OpenAI further trained GPT-3 (Brown et al., 2020) using reinforcement learning from human feedback (RLHF) to align the model.