Monthly Archives: November 2022

Posted by Alexander Zlokapa, Student Researcher, and Hartmut Neven, VP of Engineering, Quantum AI Team

Wormholes — wrinkles in the fabric of spacetime that connect two disparate locations — may seem like the stuff of science fiction. But whether or not they exist in reality, studying these hypothetical objects could be the key to making concrete the tantalizing link between information and matter that has bedeviled physicists for decades.

Surprisingly, a quantum computer is an ideal platform to investigate this connection. The trick is to use a correspondence called AdS/CFT, which establishes an equivalence between a theory that describes gravity and spacetime (and wormholes) in a fictional world with a special geometry (AdS) to a quantum theory that does not contain gravity at all (CFT).

In “Traversable wormhole dynamics on a quantum processor”, published in Nature today, we report on a collaboration with researchers at Caltech, Harvard, MIT, and Fermilab to simulate the CFT on the Google Sycamore processor. By studying this quantum theory on the processor, we are able to leverage the AdS/CFT correspondence to probe the dynamics of a quantum system equivalent to a wormhole in a model of gravity. The Google Sycamore processor is among the first to have the fidelity needed to carry out this experiment.

Background: It from Qubit

The AdS/CFT correspondence was discovered at the end of a series of inquiries arising from the question: What’s the maximum amount of information that can fit in a single region of space? If one asked an engineer how much information could possibly be stored in a datacenter the answer would likely be that it depends on the number and type of memory chips inside it. But surprisingly, what is inside the data center is ultimately irrelevant. If one were to cram more and more memory chips with denser and denser electronics into the datacenter then it will eventually collapse into a black hole and disappear behind an event horizon.

When physicists such as Jacob Bekenstein and Stephen Hawking tried to compute the information content of a black hole, they found to their surprise that it is given by the area of the event horizon — not by the volume of the black hole. It looks as if the information inside the black hole was written on the event horizon. Specifically, a black hole with an event horizon that can be tiled with A tiny units of area (each unit, called a “Planck area,” is 2.6121×10⁻⁷⁰ m²) has at most A/4 bits of information. This limit is known as the Bekenstein-Hawking bound.

This discovery that the maximum amount of information that could fit in a region was proportional not to its volume, but to the surface area of the region’s boundary hinted at an intriguing relationship between quantum information and the three-dimensional spatial world of our everyday experience. This relationship has been epitomized by the phrase “It from qubit,” describing how matter (“it”) emerges from quantum information (“qubit”).

While formalizing such a relationship is difficult for ordinary spacetime, recent research has led to remarkable progress with a hypothetical universe with hyperbolic geometry known as “anti-de Sitter space” in which the theory of quantum gravity is more naturally constructed. In anti-de Sitter space, the description of a volume of space with gravity acting in it can be thought of as encoded on the boundary enclosing the volume: every object inside the space has a corresponding description on the boundary and vice versa. This correspondence of information is called the holographic principle, which is a general principle inspired by Bekenstein and Hawking’s observations.

Schematic representation of anti-de Sitter space (interior of cylinder) and its dual representation as quantum information on the boundary (surface of cylinder).

The AdS/CFT correspondence allows physicists to connect objects in space with specific ensembles of interacting qubits on the surface. That is, each region of the boundary encodes (in quantum information) the content of a region in spacetime such that matter at any given location can be “constructed” from the quantum information. This allows quantum processors to work directly with qubits while providing insights into spacetime physics. By carefully defining the parameters of the quantum computer to emulate a given model, we can look at black holes, or even go further and look at two black holes connected to each other — a configuration known as a wormhole, or an Einstein-Rosen bridge.

Experiment: Quantum Gravity in the Lab

Implementing these ideas on a Sycamore processor, we have constructed a quantum system that is dual to a traversable wormhole. Translated from the language of quantum information to spacetime physics via the holographic principle, the experiment let a particle fall into one side of a wormhole and observed it emerging on the other side.

Traversable wormholes were recently shown to be possible by Daniel Jafferis, Ping Gao and Aron Wall. While wormholes have long been a staple of science fiction, there are many possible spacetime geometries in which the formation of a wormhole is possible, but a naïvely constructed one would collapse on a particle traveling through it. The authors showed that a shockwave — i.e., a deformation of spacetime that propagates at the speed of light — of negative energy would solve this problem, propping open the wormhole long enough to enable traversability. The presence of negative energy in a traversable wormhole is similar to negative energy in the Casimir effect, where vacuum energy pushes together closely spaced plates. In both cases, quantum mechanics permits the energy density at a given location in space to be either positive or negative. On the other hand, if the wormhole experienced a shockwave of positive energy, no information would be allowed to pass through.

The simplest application of the holographic principle to create a wormhole requires many, many qubits — in fact, to approach the pencil-and-paper solutions given by theoretical physicists, one would need an arbitrarily large number of qubits. As the number of qubits is reduced, additional corrections are required that are still poorly understood today. New ideas were needed to build a traversable wormhole on a quantum computer with a limited number of qubits.

One of us (Zlokapa) adopted ideas from deep learning to design a small quantum system that preserved key aspects of gravitational physics. Neural networks are trained via backpropagation, a method that optimizes parameters by directly computing the gradient through the layers of the network. To improve the performance of a neural network and prevent it from overfitting to the training dataset, machine learning (ML) practitioners employ a host of techniques. One of these, sparsification, attempts to restrict the detail of information in the network by setting as many weights as possible to zero.

Similarly, to create the wormhole, we started with a large quantum system and treated it like a neural network. Backpropagation updated the parameters of the system in order to maintain gravitational properties while sparsification reduced the size of the system. We applied ML to learn a system that preserved only one key gravitational signature: the importance of using a negative energy shockwave. The training dataset compared dynamics of a particle traversing a wormhole propped open with negative energy and collapsed with positive energy. By ensuring the learned system preserved this asymmetry, we obtained a sparse model consistent with wormhole dynamics.

Learning procedure to produce a sparse quantum system that captures gravitational dynamics. A single coupling consists of all six possible connections between a given group of four fermions.

Working with Jafferis and a handful of collaborators from Caltech, Fermilab, and Harvard, we subjected the new quantum system to numerous tests to determine if it showed gravitational behavior beyond signatures induced by different energy shockwaves. For example, while quantum mechanical effects can transmit information across a quantum system in a diverse set of ways, information that travels in spacetime — including through a wormhole — must be causally consistent. This and other signatures were verified on classical computers, confirming that the dynamics of the quantum system were consistent with a gravitational interpretation as viewed through the dictionary of the holographic principle.

Implementing the traversable wormhole as an experiment on a quantum processor is an extraordinarily delicate process. The microscopic mechanism of information transfer across qubits is highly chaotic: imagine an ink drop swirling in water. As a particle falls into a wormhole, its information gets smeared over the entire quantum system in the holographic picture. For the negative energy shockwave to work, the scrambling of information must follow a particular pattern known as perfect size winding. After the particle hits the negative energy shockwave, the chaotic patterns effectively proceed in reverse: when the particle emerges from the wormhole, it is as if the ink drop has come back together by exactly undoing its original turbulent spread. If, at any point in time, a small error occurs, the chaotic dynamics will not undo themselves, and the particle will not make it through the wormhole.

Left: Quantum circuit describing a traversable wormhole. A maximally entangled pair of qubits (“EPR pair”) are used as an entanglement probe to send a qubit through the wormhole. The qubit is swapped into the left side of the wormhole at time –t0; the energy shockwave is applied at time 0; and the right side of the wormhole is measured at time t1. Right: Photograph of the Google Sycamore quantum processor.

On the Sycamore quantum processor, we measured how much quantum information passed from one side of the system to the other when applying a negative versus a positive energy shockwave. We observed a slight asymmetry between the two energies, showing the key signature of a traversable wormhole. Due to the protocol’s sensitivity to noise, the Sycamore processor’s low error rates were critical to measuring the signal; with even 1.5x the amount of noise, the signal would have been entirely obscured.

Looking Forward

As quantum devices continue to improve, lower error rates and larger chips will allow deeper probes of gravitational phenomena. Unlike experiments such as LIGO that record data about gravity in the world around us, quantum computers provide a tool to explore theories of quantum gravity. We hope that quantum computers will help develop our understanding of future theories of quantum gravity beyond current models.

Gravity is only one example of the unique ability of quantum computers to probe complex physical theories: quantum processors can provide insight into time crystals, quantum chaos, and chemistry. Our work demonstrating wormhole dynamics represents a step towards discovering fundamental physics using quantum processors at Google Quantum AI.

You can also read more about this result here.

Acknowledgements

We would like to thank our Quantum Science Communicator Katherine McCormick for her help writing this blog post.

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Posted by Jason Wei and Yi Tay, Research Scientists, Google Research, Brain Team

In recent years, language models (LMs) have become more prominent in natural language processing (NLP) research and are also becoming increasingly impactful in practice. Scaling up LMs has been shown to improve performance across a range of NLP tasks. For instance, scaling up language models can improve perplexity across seven orders of magnitude of model sizes, and new abilities such as multi-step reasoning have been observed to arise as a result of model scale. However, one of the challenges of continued scaling is that training new, larger models requires great amounts of computational resources. Moreover, new models are often trained from scratch and do not leverage the weights from previously existing models.

In this blog post, we explore two complementary methods for improving existing language models by a large margin without using massive computational resources. First, in “Transcending Scaling Laws with 0.1% Extra Compute”, we introduce UL2R, which is a lightweight second stage of pre-training that uses a mixture-of-denoisers objective. UL2R improves performance across a range of tasks and even unlocks emergent performance on tasks that previously had close to random performance. Second, in “Scaling Instruction-Finetuned Language Models”, we explore fine-tuning a language model on a collection of datasets phrased as instructions, a process we call “Flan”. This approach not only boosts performance, but also improves the usability of the language model to user inputs without engineering of prompts. Finally, we show that Flan and UL2R can be combined as complementary techniques in a model called Flan-U-PaLM 540B, which outperforms the unadapted PaLM 540B model by 10% across a suite of challenging evaluation benchmarks.

UL2R Training

Traditionally, most language models are pre-trained on either a causal language modeling objective that enables the model to predict the next word in a sequence (e.g., GPT-3 or PaLM) or a denoising objective, where the model learns to recover the original sentence from a corrupted sequence of words, (e.g., T5). Although there are some tradeoffs in language modeling objectives in that causal LMs are better at long-form generation and LMs trained on a denoising objective are better for fine-tuning, in prior work we demonstrated that a mixture-of-denoisers objective that includes both objectives results in better performance on both scenarios.

However, pre-training a large language model on a different objective from scratch can be computationally prohibitive. Hence, we propose UL2 Repair (UL2R), an additional stage of continued pre-training with the UL2 objective that only requires a relatively small amount of compute. We apply UL2R to PaLM and call the resulting new language model U-PaLM.

In empirical evaluations, we found that scaling curves improve substantially with only a small amount of UL2 training. For instance, we show that by using UL2R on the intermediate checkpoint of PaLM 540B, we reach the performance of the final PaLM 540B checkpoint while using 2x less compute (or a difference of 4.4 million TPUv4 hours). Naturally, applying UL2R to the final PaLM 540B checkpoint also leads to substantial improvements, as described in the paper.

Compute versus model performance of PaLM 540B and U-PaLM 540B on 26 NLP benchmarks (listed in Table 8 in the paper). U-PaLM 540B continues training PaLM for a very small amount of compute but provides a substantial gain in performance.

Another benefit that we observed from using UL2R is that on some tasks, performance is much better than models trained purely on the causal language modeling objective. For instance, there are many BIG-Bench tasks that have been described as “emergent abilities”, i.e., abilities that can only be observed in sufficiently large language models. Although the way that emergent abilities are most commonly found is by scaling up the size of the LM, we found that UL2R can actually elicit emergent abilities without increasing the scale of the LM.

For instance, in the Navigate task from BIG-Bench, which measures the model’s ability to perform state tracking, all models except U-PaLM with less than 10²³ training FLOPs achieve approximately random performance. U-PaLM performance is more than 10 points above that. Another example of this is the Snarks task from BIG-Bench, which measures the model’s ability to detect sarcasm. Again, whereas all models less than 10²⁴ training FLOPs achieve approximately random performance, U-PaLM achieves well above even for the 8B and 62B models.

For two abilities from BIG-Bench that demonstrate emergent task performance, U-PaLM achieves emergence at a smaller model size due to its use of the UL2R objective.

Instruction Fine-Tuning

In our second paper, we explore instruction fine-tuning, which involves fine-tuning LMs on a collection of NLP datasets phrased as instructions. In prior work, we applied instruction fine-tuning to a 137B-parameter model on 62 NLP tasks, such as answering a trivia question, classifying the sentiment of a movie, or translating a sentence to Spanish.

In this work we fine-tune a 540B parameter language model on more than 1.8K tasks. Moreover, whereas previous efforts only fine-tuned a LM with few-shot exemplars (e.g., MetaICL) or zero-shot without exemplars (e.g., FLAN, T0), we fine-tune on a combination of both. We also include chain of thought fine-tuning data, which enables the model to perform multi-step reasoning. We call our improved methodology “Flan”, for fine-tuning language models. Notably, even with fine-tuning on 1.8K tasks, Flan only uses a small portion of compute compared to pre-training (e.g., for PaLM 540B, Flan only requires 0.2% of the pre-training compute).

We fine-tune language models on 1.8K tasks phrased as instructions, and evaluate them on unseen tasks, which are not included in fine-tuning. We fine-tune both with and without exemplars (i.e., zero-shot and few-shot) and with and without chain of thought, enabling generalization across a range of evaluation scenarios.

In the paper, we instruction–fine-tune LMs of a range of sizes to investigate the joint effect of scaling both the size of the LM and the number of fine-tuning tasks. For instance, for the PaLM class of LMs, which includes models of 8B, 62B, and 540B parameters. We evaluate our models on four challenging benchmark evaluation suites (MMLU, BBH, TyDiQA, and MGSM), and find that both scaling the number of parameters and number of fine-tuning tasks improves performance on unseen tasks.

Both scaling up to a 540B parameter model and using 1.8K fine-tuning tasks improves the performance on unseen tasks. The y-axis is the normalized average over four evaluation suites (MMLU, BBH, TyDiQA, and MGSM).

In addition to better performance, instruction fine-tuning a LM enables it to respond to user instructions at inference time, without few-shot exemplars or prompt engineering. This makes LMs more user-friendly across a range of inputs. For instance, LMs without instruction fine-tuning can sometimes repeat the input or fail to follow instructions, but instruction fine-tuning mitigates such errors.

Our instruction–fine-tuned language model, Flan-PaLM, responds better to instructions compared to the PaLM model without instruction fine-tuning.

Putting Them Together

Finally, we show that UL2R and Flan can be combined to train the Flan-U-PaLM model. Since Flan uses new data from NLP tasks and enables zero-shot instruction following, we apply Flan as the second method after UL2R. We again evaluate on the four benchmark suites, and find that the Flan-U-PaLM model outperforms PaLM models with just UL2R (U-PaLM) or just Flan (Flan-PaLM). Further, Flan-U-PaLM achieves a new state-of-the-art on the MMLU benchmark with a score of 75.4% when combined with chain of thought and self-consistency.

Combining UL2R and Flan (Flan-U-PaLM) leads to the best performance compared to just using UL2R (U-PaLM) or just Flan (Flan-U-PaLM). Performance is the normalized average over four evaluation suites (MMLU, BBH, TyDiQA, and MGSM).

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Overall, UL2R and Flan are two complementary methods for improving pre-trained language models. UL2R adapts the LM to a mixture-of-denoisers objective using the same data, whereas Flan leverages training data from over 1.8K NLP tasks to teach the model to follow instructions. As LMs become even larger, techniques such as UL2R and Flan that improve general performance without large amounts of compute may become increasingly attractive.

Acknowledgements

It was a privilege to collaborate on these two papers with Hyung Won Chung, Vinh Q. Tran, David R. So, Siamak Shakeri, Xavier Garcia, Huaixiu Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc V. Le, Mostafa Dehghani, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Ed H. Chi, Jeff Dean, Jacob Devlin, and Adam Roberts.

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