Skip to main content

Physics-Inspired PFGM++ Trumps Diffusion-Only Models in Generating Realistic Images

  Recent years have witnessed astonishing progress in generative image modeling, with neural network-based models able to synthesize increasingly realistic and detailed images. This rapid advancement is quantitatively reflected in the steady decrease of Fréchet Inception Distance (FID) scores over time. The FID score measures the similarity between generated and real images based on feature activations extracted from a pretrained image classifier network. Lower FID scores indicate greater similarity to real images and thus higher quality generations from the model. Around 2020, architectural innovations like BigGAN precipitated a substantial leap in generated image fidelity as measured by FID. BigGAN proposed techniques like class-conditional batch normalization and progressive growing of generator and discriminator models to stabilize training and generate higher resolution, more realistic images compared to prior generative adversarial networks (GANs).  The introduction of BigGAN and

New Quantum Computing Approaches: Photonic Chips, Topological Qubits and Trapped Atoms Show Promise

Quantum computing has seen remarkable progress in recent years. After the first demonstrations of “quantum supremacy” or “quantum advantage”, companies are now publishing roadmaps projecting commercial quantum computers with millions of qubits within a decade. 

Meanwhile, new quantum computing approaches based on photons, atoms and topology are catching up to the early leaders using superconducting circuits and ion traps.

The fundamental building blocks of quantum computers are qubits (quantum bits). Unlike regular bits, qubits can exist in a superposition of 0 and 1, allowing massive parallelism. When entangled together, qubits enable certain computations like simulation of quantum systems to be done exponentially faster. The catch is quantum states are fragile and error-prone. The threshold for useful applications is estimated to be between hundreds of thousands to millions of physical qubits.

In 2019, Google announced its 53-qubit Sycamore processor achieved quantum supremacy by sampling the output distribution of a pseudo-random quantum circuit. This sampling would take 10,000 years on a classical supercomputer but their quantum processor did it in 200 seconds. However, the computational task itself was not very useful other than proving a point. In 2021, a 100 photon quantum computer from China also showed quantum advantage doing a similar sampling task.

In 2022, IBM announced demonstrating quantum “utility” using its 127-qubit Eagle processor to simulate time evolution of a quantum spin system. While an impressive physics simulation, the notion of “utility” was still debatable. IBM plans to scale up to over 1000 physical qubits by end of 2022 and eventually to more than one million qubits by 2030 using a modular architecture. Google has also outlined a roadmap to scale up its Sycamore quantum processors but without specific timelines.

The early leaders in the race for quantum computing are based on superconducting circuits (Google, IBM, Rigetti etc) and trapped ions (IonQ, Honeywell Quantum etc). Superconducting qubits require complex cryogenic systems to cool them down to millikelvin temperatures. Ion trap qubits involve manipulating individual atoms trapped by electric fields and also require cooling to cryogenic temperatures.

In the past couple of years, new approaches have shown significant progress to rival the early leaders. Photonic quantum computing uses photons as qubits. While qubits are prone to errors, photonics has the advantage of room temperature operation. Challenges include scaling up the optical elements and single photon sources into integrated photonic chips. Startups like PsiQuantum, Xanadu and QuiX are at the forefront along with academic groups.

For example, Xanadu has built a 216-qubit photonic processor called Borealis using a layered architecture. The Canadian company PsiQuantum is developing qubits based on squeezed states of light and aims to have over 1 million photonic qubits by around 2025 based on silicon photonics chips manufactured by GlobalFoundries. Researchers in the Netherlands, Germany and China have all demonstrated prototype photonic chips with tens of qubits.

Atoms held in optical tweezers is emerging as another promising approach. Here, individual neutral atoms are trapped in arrays using focused laser beams. This enables flexible 3D qubit configurations. Startups like ColdQuanta, Atom Computing and Pasqal are developing qubits encoded in atomic states. Coherence times exceeding tens of seconds have been shown. So far these systems have operated with tens of qubits but scaling promises to be easier than in ion traps.

Interest is rising in topological quantum computing where the qubits are protected from errors by topological properties like conservation of angular momentum. Microsoft has been the pioneer but faced setbacks in their approach using exotic quasiparticles called Majorana zero modes. However, in 2022, they reported a breakthrough re-demonstrating these elusive particles in a superconducting-semiconducting nanowire architecture. Google and Quantinuum have also now shown advances in creating alternative topological qubits.

Most big tech companies have now published vague quantum computing roadmaps. Microsoft expects functional systems in less than 10 years. IBM aims for more than 1000 qubits this year and 1 million by 2030. Google is investing heavily but hasn't revealed timelines. Amazon released its first quantum computer in late 2021 but is discreet about next steps. Similar claims of million qubit computers by around 2030 have been made by startups like PsiQuantum, ColdQuanta, Atom Computing and others.

A key challenge is scaling up qubits without losing qubit performance and fidelity. This requires advances in materials, fabrication and control electronics. Modular architectures built up from sub-modules containing arrays of qubits will be critical. Packaging multiple modules with sophisticated interconnects then enables scaling up to very large numbers. Companies are also architecting comprehensive software and cloud platforms to leverage NISQ (noisy intermediate-scale quantum) processors available today. Hybrid quantum-classical algorithms that combine classical and quantum hardware are an active area of research.

Realistically, while reaching the million qubit milestone is important, useful applications may emerge much sooner than that. Focus is increasing on quantum machine learning as a potential killer app for NISQ processors. Other promising near term uses are quantum chemistry, optimization, quantum finance and sampling applications. 

Some believe quantum networking for communication security may prove commercially viable earlier than general purpose quantum computing. However, there are still skeptics who think engineering a fault-tolerant, fully error-corrected quantum computer may not be possible at all.

Geopolitical tensions are also catalyzing quantum progress. Facing technology sanctions, China is investing heavily and has unveiled a $15 billion National Laboratory for Quantum Information this year. The European Union plans to launch a quantum communication infrastructure connecting major cities by 2030. Quantum computing is seen as a critical technology where US dominance is threatened especially by China. This concern was a major factor behind the US National Quantum Initiative Act enacted in 2018 which is now funding more than $1 billion in quantum research and development each year.

The steady pace of advances and growing investments by both tech giants and startups suggest quantum computing systems able to surpass classical supercomputers at useful applications could become a reality in the next 10 to 15 years. But there are still deep technical challenges to be overcome before this vision materializes. 

Pioneering physicists and engineers are pushing the boundaries of manipulating matter and light at nanoscales to usher in this quantum future. Concerted long term efforts across materials, software, computer science and commercialization will be key to eventually unlocking the full potential of quantum computing.

Popular posts from this blog

DALL-E 3 Review: This New Image Generator Blows Mid-Journey Out of the Water

    For the seasoned AI art aficionado, the name DALL-E needs no introduction. It's been a game-changer sin ce its inception, pushing the boundaries of what's possible in the realm of generative AI. However, with the advent of DALL-E 3, we're standing on the precipice of a revolution.  In this comprehensive exploration, we'll dissect the advancements, capabilities, and implications of DALL-E 3, aiming to provide you with a thorough understanding of this groundbreaking technology. DALL-E 3 vs. its Predecessors: A Comparative Analysis Before we plunge into the specifics of DALL-E 3, let's take a moment to reflect on its predecessors. DALL-E 2, while impressive in its own right, faced its share of critiques. Mid-Journey and SDXL (Stable Diffusion XL), with their unique strengths, carved out their niche in the world of AI art. The discourse surrounding Bing Image Creator, a technical extension of DALL-E 2, also played a role in shaping expectations. However, the questio

The Future is Now: Exploring Hyperwrite AI's Cutting-Edge Personal Assistant

  In this feature, we'll be delving into the evolution of AI agents and the groundbreaking capabilities of Hyperwrite AI's personal assistant. From its early days with Auto GPT to the recent strides in speed and efficiency, we'll uncover how this technology is reshaping the landscape of AI assistance. Auto GPT: A Glimpse into the Past The journey commences with Auto GPT, an initial endeavor at automating actions using GPT-4 and open-source software. While it offered a limited range of capabilities, it provided a sneak peek into the potential of AI agents. We'll take a closer look at its features and how it laid the foundation for more advanced developments. Web-Based Implementation: Making AI Accessible The transition to web-based implementation rendered the technology more accessible, eliminating the need for individual installations. We'll delve into the improved user interface and enhanced functionalities that came with this transition, while also acknowledging t

GPT 4 Vision: ChatGPT Gets Vision Capabilities and More in Major New Upgrades

 Artificial intelligence (AI) has made immense strides in recent years, with systems like ChatGPT showcasing just how advanced AI has become. ChatGPT in particular has been upgraded significantly, gaining capabilities that seemed unbelievable just a short time ago. In this extensive article, we'll dive into these new ChatGPT features, including integrated image generation through DALL-E 3, vision capabilities with GPT-4, and an overhauled conversation mode. Beyond ChatGPT, there are many other exciting AI advancements happening. New generative video AI models are producing remarkably smooth and detailed animations. Open source voice cloning now allows near-perfect voice mimicking with just seconds of audio. And video games are being created featuring AI-generated characters that can hold natural conversations. Read on for an in-depth look at these innovations and more. ChatGPT Upgrades: Integration with DALL-E 3 Earlier this year, OpenAI unveiled DALL-E 3, their most advanced image