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How a little known but essential operation in Quantum Computing helped achieve a major scientific breakthrough

夜色直播's feed-forward functionality is worth getting to know

June 29, 2023

夜色直播鈥檚 recent announcement about its breakthrough on topological qubits garnered headlines across both the specialist scientific media as well as those more broadly interested in the advances that will make quantum computing useful more quickly than anticipated. However, hidden in the details was a reference to a technology that is as rare as it is valuable. The fact is that the topological qubit that was generated could only have been done via 夜色直播鈥檚 H-Series quantum processors due to their various qualities and functions of which measurement and 鈥榝eed-forward鈥 is critical.

As we know, great advances are often built on the back of little-known utilities - functions and tools that rarely get mentioned. These are sometimes technological constructs that might seem simple on the surface, but which are difficult (in the case of feed-forward make that 鈥渧ery difficult鈥 to create), and without which critical advances would remain merely theoretical.

As detailed in two manuscripts that have been uploaded onto the pre-print repository, arXiv, 夜色直播 researchers and their collaborators successfully demonstrated, for the first time, a large-scale implementation of a long-standing theory in quantum information science; namely the use of measurement and (see below for a detailed explanation of what this means) to efficiently generate long-range entangled states.

The two experiments, conducted with research partners at the California Institute of Technology, Harvard University, the University of Sydney, the Perimeter Institute for Theoretical Physics and the University of California, Davis, used 夜色直播鈥檚 trapped ion quantum computers, Powered by Honeywell, to show how feed-forward enables success by dramatically reducing the resources required to produce highly-entangled quantum states and topologically ordered phases, one of the most exciting areas of research in modern physics.

Feed-forward uses selective measurements during the execution of a quantum circuit and adapts future operations depending on those measurement results. To be successful in running an adaptive quantum circuit, several challenging requirements must be met: (1) a select group of qubits must be measured in the middle of a circuit with high fidelity, and without accidentally measuring other qubits, and (2) the measurement results must be sent to a classical computer and quickly processed to create instructions to be fed-forward to the quantum computer on the fly - all of which must be done fast enough to prevent the active qubits from decohering.

Once these requirements are met, the feed-forward capabilities let quantum computers create long-range entangled states which are emerging as central to various branches of modern physics such as quantum error correction codes and the study of spin liquids in condensed matter. It is also the essential component of topological order and could enable the simulation of quantum systems beyond the reach of classical computation.

In the paper 鈥溾, 夜色直播, working with colleagues from the California Institute of Technology and Harvard University use feed-forward to explore topologically ordered phases of matter.聽

Separately, a different team of scientists from 夜色直播, the University of Sydney, the Perimeter Institute for Theoretical Physics and the University of California, Davis, used feed-forward to explore adaptive quantum circuits in 鈥溾.听

Two of 夜色直播鈥檚 physicists who worked on both experiments, Henrik Dreyer and Michael Foss-Feig, offered some observations on the work.

鈥淲hile it has been clear to theorists that feed-forward would be a useful primitive, doing it with low errors has turned out to be very challenging. The H-Series systems have made it possible to use this primitive efficiently,鈥 said Henrik, managing director and scientific lead at 夜色直播鈥檚 office in Munich, Germany.

Michael, who is based at 夜色直播鈥檚 world-leading quantum computing laboratory outside of Denver, Colorado, also described feed-forward and adaptive quantum circuits as a jump toward meaningful simulations.

鈥淭his capability speeds up the timeline for new scientific discoveries,鈥 he said.

These successful experiments proved that feed-forward operations reduce the quantum resources required for certain algorithms and are a valuable building block for more advanced research.

"I am really excited by the opportunities opened up by this demonstration: using wave-function collapse is a very powerful tool for preparing very exotic entangled states further down the road, where there are no good scalable alternatives," said Dr. Ruben Verresen, a physicist at Harvard University and a co-author of the topological order paper.

The authors note that 鈥渢he primary technical challenge in implementing adaptive circuits is the requirement to perform partial measurements of a subset of qubits in the middle of a quantum circuit with minimal cross-talk on unmeasured qubits, return those results to a classical computer for processing, and then condition future operations on the results of that processing in real time.鈥

The paper describes how quantum hardware has now reached a state where adaptive quantum circuits are possible and can outperform unitary circuits. The experiment detailed in the paper 鈥渇irmly establishes that given access to the same amount of quantum computational resources with respect to available gates and circuit depth, adaptive quantum circuits can perform tasks that are impossible for quantum circuits without feedback.鈥

Henrik and Michael noted that the adaptive circuit research provides concrete evidence not only that feed-forward works, but that it now works well enough to achieve tasks that would not be possible without it.

鈥淲e were trying to find a metric by which somebody can look at our data produced by a shallow adaptive circuit, and convince themselves it could not have been produced with a unitary circuit of the same depth,鈥 Michael said. The metric proposed in the adaptive circuits paper achieved exactly that.

A good match: Trapped ion architecture and feed-forward聽

Demonstrating this technique required significant performance from the H1-1.聽

鈥淚t's a huge challenge to implement this in a way that works well,鈥 Michael said.聽

夜色直播鈥檚 H-Series has the capabilities that are crucial to this work: high fidelity gates, low state preparation and measurement (SPAM) error, low memory error, the ability to perform mid-circuit measurement, and all-to-all connectivity.

The feed-forward theory has been well-known for years but challenging to execute in practice, and as the paper states:

鈥淲hile individual elements of this triad have been demonstrated in the context of error correction and topological order, combining all of these ingredients into one experimental platform has proven elusive since the inception of this idea more than a decade ago. Here, we demonstrate for the first time the deterministic, high-fidelity preparation of long-range entangled quantum states using a protocol with constant depth, using 夜色直播鈥檚 H-Series programmable Ytterbium ion trap quantum computer.鈥

The authors also note that 鈥渢he all-to-all connectivity of the device was vital for the implementation of the periodic two-dimensional geometry and the conditional dynamics.鈥

In summary 鈥 these papers showcase state-of-the-art demonstrations of what can be done with quantum computers today but are only a preview of what will be done tomorrow.

About 夜色直播

夜色直播,聽the world鈥檚 largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. 夜色直播鈥檚 technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, 夜色直播 leads the quantum computing revolution across continents.聽

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May 12, 2025
夜色直播 Dominates the Quantum Landscape: New World-Record in Quantum Volume

Back in 2020, we to increase our Quantum Volume (QV), a measure of computational power, by 10x聽per year for 5 years.聽

Today, we鈥檙e pleased to share that we鈥檝e followed through on our commitment: Our System Model H2 has reached a Quantum Volume of 2虏鲁 = 8,388,608, proving not just that we always do what we say, but that our quantum computers are leading the world forward.聽

The QV benchmark was developed by IBM to represent a machine鈥檚 performance, accounting for things like qubit count, coherence times, qubit connectivity, and error rates. :听

鈥渢he higher the Quantum Volume, the higher the potential for exploring solutions to real world problems across industry, government, and research."

Our announcement today is precisely what sets us apart from the competition. No one else has been bold enough to make a similar promise on such a challenging metric 鈥 and no one else has ever completed a five-year goal like this.

We chose QV because we believe it鈥檚 a great metric. For starters, it鈥檚 not gameable, like other metrics in the ecosystem. Also, it brings together all the relevant metrics in the NISQ era for moving towards fault tolerance, such as gate fidelity and connectivity.聽

Our path to achieve a QV of over 8 million was led in part by Dr. Charlie Baldwin, who studied under the legendary Ivan H. Deutsch. Dr. Baldwin has made his name as a globally renowned expert in quantum hardware performance over the past decade, and it is because of his leadership that we don鈥檛 just claim to be the best, but that we can prove we are the best.聽

Figure 1: All known published Quantum Volume measurements.
Sources: [][][][][]

Alongside the world鈥檚 biggest quantum volume, we have the industry鈥檚 . To that point, the table below breaks down the leading commercial specs for each quantum computing architecture.聽

Table 1: Leading commercial spec for each listed architecture or demonstrated capabilities on commercial hardware.

We鈥檝e never shied away from benchmarking our machines, because we know the results will be impressive. It is our provably world-leading performance that has enabled us to demonstrate:

As we look ahead to our next generation system, Helios, 夜色直播鈥檚 Senior Director of Engineering, Dr. Brian Neyenhuis, reflects: 鈥淲e finished our five-year commitment to Quantum Volume ahead of schedule, showing that we can do more than just maintain performance while increasing system size. We can improve performance while scaling.鈥澛

Helios鈥 performance will exceed that of our previous machines, meaning that 夜色直播 will continue to lead in performance while following through on our promises.聽

As the undisputed industry leader, we鈥檙e racing against no one other than ourselves to deliver higher performance and to better serve our customers.

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May 1, 2025
GenQAI: A New Era at the Quantum-AI Frontier

At the heart of quantum computing鈥檚 promise lies the ability to solve problems that are fundamentally out of reach for classical computers. One of the most powerful ways to unlock that promise is through a novel approach we call Generative Quantum AI, or GenQAI. A key element of this approach is the (GQE).

GenQAI is based on a simple but powerful idea: combine the unique capabilities of quantum hardware with the flexibility and intelligence of AI. By using quantum systems to generate data, and then using AI to learn from and guide the generation of more data, we can create a powerful feedback loop that enables breakthroughs in diverse fields.

Unlike classical systems, our quantum processing unit (QPU) produces data that is extremely difficult, if not impossible, to generate classically. That gives us a unique edge: we鈥檙e not just feeding an AI more text from the internet; we鈥檙e giving it new and valuable data that can鈥檛 be obtained anywhere else.

The Search for Ground State Energy

One of the most compelling challenges in quantum chemistry and materials science is computing the properties of a molecule鈥檚 ground state. For any given molecule or material, the ground state is its lowest energy configuration. Understanding this state is essential for understanding molecular behavior and designing new drugs or materials.

The problem is that accurately computing this state for anything but the simplest systems is incredibly complicated. You cannot even do it by brute force鈥攖esting every possible state and measuring its energy鈥攂ecause 聽the number of quantum states grows as a double-exponential, making this an ineffective solution. This illustrates the need for an intelligent way to search for the ground state energy and other molecular properties.

That鈥檚 where GQE comes in. GQE is a methodology that uses data from our quantum computers to train a transformer. The transformer then proposes promising trial quantum circuits; ones likely to prepare states with low energy. You can think of it as an AI-guided search engine for ground states. The novelty is in how our transformer is trained from scratch using data generated on our hardware.

Here's how it works:

  • We start with a batch of trial quantum circuits, which are run on our QPU.
  • Each circuit prepares a quantum state, and we measure the energy of that state with respect to the Hamiltonian for each one.
  • Those measurements are then fed back into a transformer model (the same architecture behind models like GPT-2) to improve its outputs.
  • The transformer generates a new distribution of circuits, biased toward ones that are more likely to find lower energy states.
  • We sample a new batch from the distribution, run them on the QPU, and repeat.
  • The system learns over time, narrowing in on the true ground state.

To test our system, we tackled a benchmark problem: finding the ground state energy of the hydrogen molecule (H鈧). This is a problem with a known solution, which allows us to verify that our setup works as intended. As a result, our GQE system successfully found the ground state to within chemical accuracy.

To our knowledge, we鈥檙e the first to solve this problem using a combination of a QPU and a transformer, marking the beginning of a new era in computational chemistry.

The Future of Quantum Chemistry

The idea of using a generative model guided by quantum measurements can be extended to a whole class of problems鈥攆rom to materials discovery, and potentially, even drug design.

By combining the power of quantum computing and AI we can unlock their unified full power. Our quantum processors can generate rich data that was previously unobtainable. Then, an AI can learn from that data. Together, they can tackle problems neither could solve alone.

This is just the beginning. We鈥檙e already looking at applying GQE to more complex molecules鈥攐nes that can鈥檛 currently be solved with existing methods, and we鈥檙e exploring how this methodology could be extended to real-world use cases. This opens many new doors in chemistry, and we are excited to see what comes next.

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April 11, 2025
夜色直播鈥檚 partnership with RIKEN bears fruit

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

Today, marks the first of many breakthroughs coming from this international supercomputing partnership. The team from RIKEN and 夜色直播 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. 聽

"Scrambling" of quantum information happens in many quantum systems, from those found in complex materials to black holes. 聽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. 聽

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