ҹɫֱ��

Last year, we joined forces with RIKEN, Japan's largest comprehensive research institution, to install our hardware at RIKEN’s campus in Wako, Saitama. This deployment is part of RIKEN’s project to build a quantum-HPC hybrid platform consisting of high-performance computing systems, such as the supercomputer Fugaku and ҹɫֱ�� Systems.  

Today, marks the first of many breakthroughs coming from this international supercomputing partnership. The team from RIKEN and ҹɫֱ�� joined up with researchers from Keio University to show that quantum information can be delocalized (scrambled) using a quantum circuit modeled after periodically driven systems.  

"Scrambling" of quantum information happens in many quantum systems, from those found in complex materials to black holes.  Understanding information scrambling will help researchers better understand things like thermalization and chaos, both of which have wide reaching implications.

To visualize scrambling, imagine a set of particles (say bits in a memory), where one particle holds specific information that you want to know. As time marches on, the quantum information will spread out across the other bits, making it harder and harder to recover the original information from local (few-bit) measurements.

While many classical techniques exist for studying complex scrambling dynamics, quantum computing has been known as a promising tool for these types of studies, due to its inherently quantum nature and ease with implementing quantum elements like entanglement. The joint team proved that to be true with their latest result, which shows that not only can scrambling states be generated on a quantum computer, but that they behave as expected and are ripe for further study.

Thanks to this new understanding, we now know that the preparation, verification, and application of a scrambling state, a key quantum information state, can be consistently realized using currently available quantum computers. Read the paper , and read more about our partnership with RIKEN here.  

In our increasingly connected, data-driven world, cybersecurity threats are more frequent and sophisticated than ever. To safeguard modern life, government and business leaders are turning to quantum randomness.

What is quantum randomness, and why should you care?

The term to know: quantum random number generators (QRNGs).

QRNGs exploit quantum mechanics to generate truly random numbers, providing the highest level of cryptographic security. This supports, among many things:

• Protection of personal data
• Secure financial transactions
• Safeguarding of sensitive communications
• Prevention of unauthorized access to medical records

Quantum technologies, including QRNGs, could protect up to $1 trillion in digital assets annually, according to a recent by the World Economic Forum and Accenture.

Which industries will see the most value from quantum randomness?

The World Economic Forum report identifies five industry groups where QRNGs offer high business value and clear commercialization potential within the next few years. Those include:

1. Financial services
2. Information and communication technology
3. Chemicals and advanced materials
4. Energy and utilities
5. Pharmaceuticals and healthcare

In line with these trends, recent by The Quantum Insider projects the quantum security market will grow from approximately $0.7 billion today to $10 billion by 2030.

When will quantum randomness reach commercialization?

Quantum randomness is already being deployed commercially:

• Early adopters use our Quantum Origin in data centers and smart devices.
• Amid rising cybersecurity threats, demand is growing in regulated industries and critical infrastructure.

Recognizing the value of QRNGs, the financial services sector is accelerating its path to commercialization.

• Last year, HSBC conducted a combining Quantum Origin and post-quantum cryptography to future-proof gold tokens against “store now, decrypt-later” (SNDL) threats.
• And, just last week, JPMorganChase made headlines by using our quantum computer for the first successful demonstration of certified randomness.

On the basis of the latter achievement, we aim to broaden our cybersecurity portfolio with the addition of a certified randomness product in 2025.

How is quantum randomness being regulated?

The National Institute of Standards and Technology (NIST) defines the cryptographic regulations used in the U.S. and other countries.

• NIST’s SP 800-90B framework assesses the quality of random number generators.
• The framework is part of the FIPS 140 standard, which governs cryptographic systems operations.
• Organizations must comply with FIPS 140 for their cryptographic products to be used in regulated environments.

This week, we announced Quantum Origin received , marking the first software QRNG approved for use in regulated industries.

What does NIST validation mean for our customers?

This means Quantum Origin is now available for high-security cryptographic systems and integrates seamlessly with NIST-approved solutions without requiring recertification.

• Unlike hardware QRNGs, Quantum Origin requires no network connectivity, making it ideal for air-gapped systems.
• For federal agencies, it complements our "U.S. Made" designation, easing deployment in critical infrastructure.
• It adds further value for customers building hardware security modules, firewalls, PKIs, and IoT devices.

The NIST validation, combined with our peer-reviewed papers, further establishes Quantum Origin as the leading QRNG on the market.  

--

It is paramount for governments, commercial enterprises, and critical infrastructure to stay ahead of evolving cybersecurity threats to maintain societal and economic security.

ҹɫֱ�� delivers the highest quality quantum randomness, enabling our customers to confront the most advanced cybersecurity challenges present today.

The marriage of AI and quantum computing is going to have a widespread and meaningful impact in many aspects of our lives, combining the strengths of both fields to tackle complex problems.

Quantum and AI are the ideal partners. At ҹɫֱ��, we are developing tools to accelerate AI with quantum computers, and quantum computers with AI. According to recent independent analysis, our quantum computers are the world’s most powerful, enabling state-of-the-art approaches like Generative Quantum AI (Gen QAI), where we train classical AI models with data generated from a quantum computer.

We harness AI methods to accelerate the development and performance of our full quantum computing stack as opposed to simply theorizing from the sidelines. A paper in Nature Machine Intelligence reveals the results of a recent collaboration between ҹɫֱ�� and Google DeepMind to tackle the hard problem of quantum compilation.

The work shows a classical AI model supporting quantum computing by demonstrating its potential for quantum circuit optimization. An AI approach like this has the potential to lead to more effective control at the hardware level, to a richer suite of middleware tools for quantum circuit compilation, error mitigation and correction, even to novel high-level quantum software primitives and quantum algorithms.

An AI power-up for circuit optimization

The joint ҹɫֱ��-Google DeepMind team of researchers tackled one of quantum computing’s most pressing challenges: minimizing the number of highly expensive but essential T-gates required for universal quantum computation. This is important specifically for the fault-tolerant regime, which is becoming increasingly relevant as quantum error correction protocols are being explored on rapidly developing quantum hardware. The joint team of researchers adapted AlphaTensor, Google DeepMind’s reinforcement learning AI system for algorithm discovery, which was introduced to improve the efficiency of linear algebra computations. The team introduced AlphaTensor-Quantum, which takes as input a quantum circuit and returns a new, more efficient one in terms of number of T-gates, with exactly the same functionality!

AlphaTensor-Quantum outperformed current state-of-the art optimization methods and matched the best human-designed solutions across multiple circuits in a thoroughly curated set of circuits, chosen for their prevalence in many applications, from quantum arithmetic to quantum chemistry. This breakthrough shows the potential for AI to automate the process of finding the most efficient quantum circuit. This is the first time that such an AI model has been put to the problem of T-count reduction at such a large scale.

A quantum power-up for machine learning

The symbiotic relationship between quantum and AI works both ways. When AI and quantum computing work together, quantum computers could dramatically accelerate machine learning algorithms, whether by the development and application of natively quantum algorithms, or by offering quantum-generated training data that can be used to train a classical AI model.

Our recent announcement about Generative Quantum AI (Gen QAI) spells out our commitment to unlocking the value of the data generated by our H2 quantum computer. This value arises from the world’s leading fidelity and computational power of our System Model H2, making it impossible to exactly simulate on any classical computer, and therefore the data it generates – that we can use to train AI – is inaccessible by any other means. ҹɫֱ��’s Chief Scientist for Algorithms and Innovation, Prof. Harry Buhrman, has likened accessing the first truly quantum-generated training data to the invention of the modern microscope in the seventeenth century, which revealed an entirely new world of tiny organisms thriving unseen within a single drop of water.

Recently, we announced a wide-ranging partnership with NVIDIA. It charts a course to commercial scale applications arising from the partnership between high-performance classical computers, powerful AI systems, and quantum computers that breach the boundaries of what previously could and could not be done. Our President & CEO, Dr. Raj Hazra spoke to CNBC recently about our partnership. Watch the video here.

As we prepare for the next stage of quantum processor development, with the launch of our Helios system in 2025, we’re excited to see how AI can help write more efficient code for quantum computers – and how our quantum processors, the most powerful in the world, can provide a backend for AI computations.

As in any truly symbiotic relationship, the addition of AI to quantum computing equally benefits both sides of the equation.

To read more about ҹɫֱ�� and Google DeepMind’s collaboration, please read the scientific paper .