Some of the more pressing and intractable problems in physics may be closer to being answered, such as the nature of superconductivity and other exotic properties, thanks to work done by a team at Quantinuum using the H2-1 trapped-ion quantum computer.  

Detailed in a scientific paper available on arXiv, the team used the H2-1 device to measure the “Loschmidt amplitude”, which quantifies how much a quantum system has changed after some time has passed (for the experts: this is the inner product between the time-evolved state and the initial state). Measuring the Loschmidt amplitude is central to several proposed quantum computing algorithms, including one described in the seminal work of Lu, Banuls and Cirac (2019). Their algorithm is a non-variational, hybrid quantum-classical scheme aimed at obtaining equilibrium properties of quantum systems. This is the first experimental demonstration of the quantum computation required for this algorithm.

To sweeten the pot, the research team measured the Loschmidt amplitude of a beloved, much-studied, and not-fully-understood model called the “Fermi-Hubbard” model. The Fermi-Hubbard model is used, among other things, to help scientists understand superconductivity, which is very challenging to explore fully with classical computing methods. When Richard Feynman “launched” the field of quantum computing with a famous talk in 1981, it was exactly this type of system he proposed we study with quantum computers: large quantum-mechanical systems that are difficult or impossible to effectively simulate classically. Using quantum computers to gain greater insights into the Fermi-Hubbard model could take us one step closer to understanding the behavior of high-temperature superconductors, a valuable goal with the potential to transform multiple industries.

A measurement of the Loschmidt amplitude is difficult because it is a “global observable”, meaning that any error in the quantum calculation will have an impact on the final results. This work highlights the outstanding precision of Quantinuum’s System Model H2 quantum computers. In particular, the trapped ion architecture allows for almost perfect state preparation and measurement, which is a necessary condition for such kind of calculations. Until now, this model had not been simulated with more than 16 qubits, in part because the gate operations applied are so complex. This paper explores the model on 32 qubits and includes a number of difficult elements; such as Schrodinger cat states, deep circuits, and complex Hamiltonians, making for a powerful demonstration of the H2-1 system capabilities. 

While this work is certainly a “NISQ”-era result, it shows that quantum computing can achieve interesting milestones without error correction – highlighting the fact that quantum methods may offer real advantages over classical methods in the near future. In addition, the team noted that while analog quantum simulators have made substantial progress in the study of exotic systems over the past decade, using a quantum computer to study these same systems allows for a wider exploration of the parameter space than Nature herself allows in laboratory simulations.

A more complex version of the algorithm will need to be implemented in the future to unlock the secrets of materials like superconductors, but in the meantime this work highlights the fact that Quantinuum is closing in on the answer to extremely relevant open questions, so far intractable with existing classical methods.

The IEEE International Conference on Quantum Computing and Engineering – or IEEE Quantum Week – is September 17 – 22 this year. Quantinuum is pleased to support IEEE’s efforts to bring engineers, scientists, researchers, students and others together to learn and encourage collaborations to advance quantum computing.

At the event, the Quantinuum team will be participating in a variety of sessions vital to the growth of the quantum ecosystem. Topics include photonics and fault tolerance strategies for scaling quantum computers, optimizing circuit compilation incorporating ZX-calculus as a simplification tool, and culture and policies.

Please see the complete list of sessions featuring Quantinuum team members below.

Tutorials

Quantum circuit compilation and classical control with TKET, Tuesday, Sept 19, 10:00am - 4:30pm, presented by Callum MacPherson, Technical support, training and outreach officer, and Lewis Wright, Quantum Algorithms Scientist

Quantum in Pictures in Practice, Wednesday, Sept 20, 10:00am – 4:30pm, presented by Lia Yeh, Research Engineer, Thomas Cervoni, Public Engagement and Academic Relations, and Harny Wang, Senior Research Fellow

Workshops

Quantum Computing Market Success Requires an Application-level Programming Model that Delivers Performance, Tuesday, Sept 19, 10:00am – 4:30pm, with Megan Kohagen, Lead Application Engineer

Quantum Computing for Natural Sciences: Technology and Applications, Wednesday, Sept 20, 10:00am – 4:30pm, with Lia Yeh, Research Engineer

Classical Control Systems for Quantum Computing, Wednesday, Sept 20, 10:00am – 4:30pm, with David Liefer, Chief Electrical Engineer

Emerging Technologies for Scaling Trapped-ion Quantum Systems, Thursday, Sept 21, 10:00am – 4:30pm, with Patty Lee, Chief Scientist for Hardware Technology Development

Quantum Algorithms for Financial Applications​, Friday, Sept 22, 10:00 – 4:30pm, with David Amaro, Senior Research Scientist

Technology Roadmapping for Quantum Computing​, Friday, Sept 22, 1:00 – 2:30pm, with Patty Lee, Chief Scientist for Hardware Technology Development

Panels

What’s in your photonics for quantum toolbox?, Monday, Sept 18, 10:00 – 11:30am, with Mary Rowe, Integrated Photonics Technical Manager

From the Capitol to the Laboratory: How Industry and Academia can Leverage National Policy for Funding of QIS, Wednesday, Sept 20, 3:00 – 4:30pm, with Ryan McKenney, Associate General Counsel, Compliance and Director of Government Relations

Real-Time decoding for in fault-tolerant era​, Thursday, Sept 21, 3:00 – 4:30pm, with Natalie Brown, Senior Advanced Physicist

Changing DEIA Culture and Environment in Industry​, Friday, Sept 22, 10:00 – 11:30am, with Sam Parsons, HR Director

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*All sessions are listed in Washington time, Pacific Time Zone

In a series of recent technical papers, Quantinuum researchers demonstrated the world-leading capabilities of the latest H-Series quantum computers, and the features and tools that make these accessible to our global customers and users.

Our teams used the H-Series quantum computers to directly measure and control non-abelian topological states of matter [1] for the first time, explore new ways to solve combinatorial optimization problems more efficiently [2], simulate molecular systems using logical qubits with error detection [3], probe critical states of matter [4], as well as exhaustively benchmark our very latest system [5].

Part of what makes such rapid technical and scientific progress possible is the effort our teams continually make to develop and improve workflow tools, helping our users to achieve successful results. In this blog post, we will explore the capabilities of three new tools in some detail, discuss their significance, and highlight their impact in recent quantum computing research.

Leakage Detection Gadget in pyTKET

“Leakage” is a quantum error process where a qubit ends up in a state outside the computational subspace and can significantly impact quantum computations. To address this issue, Quantinuum has developed a leakage detection gadget in pyTKET, a python module for interfacing with TKET, our quantum computing toolkit and optimizing compiler. This gadget, presented at the 2022 IEEE International Conference [6], acts as an error detection technique: it detects and excludes results affected by leakage, minimizing its impact on computations. It is also a valuable tool for measuring single-qubit and two-qubit spontaneous emission rates. H-Series users can access this open-source gadget through pyTKET, and an example notebook is available on the pyTKET GitHub repository. 

Mid-Circuit Measurement and Qubit Reuse (MCMR) Package

The MCMR package, built as a pyTKET compiler pass, is designed to reduce the number of qubits required for executing many types of quantum algorithms, expanding the scope of what is possible on the current-generation H-Series quantum computers. 

As an example, in a recent paper [4], Quantinuum researchers applied this tool to simulate the transverse-field Ising model and used only 20 qubits to simulate a much larger 128 site system (there is more detail below on this work). By measuring qubits early in the circuit, resetting them, and reusing them elsewhere, the package ingests a raw circuit and outputs an optimized circuit that requires fewer quantum resources. Previously, a scientific paper [7] and blog post on MCMR were published highlighting its benefits and applications. H-Series customers can download this package via the Quantinuum user portal.

Quantinuum H2-1 Emulator Release

To enable efficient use of Quantinuum’s 2^nd generation processor, the System Model H2, Quantinuum has released the H2-1 emulator to give users greater flexibility with noise-informed state vector emulation. This emulator uses the NVIDIA's cuQuantum SDK to accelerate quantum computing simulation workflows, nearly approaching the limit of full state emulation on conventional classical hardware. The emulator is a faithful representation of the QPU it emulates. This is accomplished by not only using realistic noise models and noise parameters, but also by sharing the same software stack between the QPU and the emulator up until the job is either routed to the QPU or the classical computing processors. Most notable is that the emulator and the QPU use the same compiler allowing subtle and time-dependent errors to be appropriately represented. The H2-1 emulator was initially released as a beta product alongside the System Model H2 quantum computer at launch. It runs on a GPU backend and an upgraded global framework now offering features such as job chunking, incremental resource distribution, mid-execution job cancellation, and partial result return. Detailed information about the emulator can be found in the H2 emulator product datasheet on the Quantinuum website. H-Series customers with an H2 subscription can access the H2-1 emulator via an API or the Microsoft Azure platform.

Enabling Recent Works

Quantinuum's new enabling tools have already demonstrated their efficacy and value in recent quantum computing research, playing a vital role in advancing the field and achieving groundbreaking results. Let's expand on some notable recent examples.

All works presented here benefited from having access to our H-Series emulators; of these two significant demonstrations were the “Creation of Non-Abelian Topological Order and Anyons on a Trapped-Ion Processor” [1] and “Demonstration of improved 1-layer QAOA with Instantaneous Quantum Polynomial” [2]. These demonstrations involved extensive testing, debugging, and experiment design, for which the versatility of the H2-1 emulator proved invaluable, providing initial performance benchmarks in a realistic noisy environment. Researchers relied on the emulator's results to gauge algorithmic performance and make necessary adjustments. By leveraging the emulator's capabilities, researchers were able to accelerate their progress.

The MCMR package was extensively used in benchmarking the System Model H2 quantum computer’s world-leading capabilities [5]. Two application-level benchmarks performed in this work, approximating the solution to a MaxCut combinatorics problem using the quantum approximate optimization algorithm (QAOA) and accurately simulating a quantum dynamics model using a holographic quantum dynamics (HoloQUADS) algorithm, would have been too large to encode on H2's 32 qubits without the MCMR package. Further illustrating the overall value of these tools, in the HoloQUADS benchmark, there is a "bond qubit" that is particularly susceptible to errors due to leakage. The leakage detection gadget was used on this "bond qubit" at the end of the circuit, and any shots with a detected leakage error were discarded. The leakage detection gadget was also used to obtain the rate of leakage error per single-qubit and two-qubit gates, two component-level benchmarks.

In another scientific work [4], the MCMR compilation tool proved instrumental to simulating a transverse-field Ising model on 128 sites, using 20 qubits. With the MCMR package and by leveraging a state-of-the-art classical tensor-network ansatz expressed as a quantum circuit, the Quantinuum team was able to express the highly entangled ground state of the critical Ising model. The team showed that with H1-1's 20 qubits, the properties of this state could be measured on a 128-site system with very high fidelity, enabling a quantitatively accurate extraction of some critical properties of the model.

Key Takeaways

At Quantinuum, we are entirely devoted to producing a quantum hardware, middleware and software stack that leads the world on the most important benchmarks and includes features and tools that provide breakthrough benefit to our growing base of users.  In today's NISQ hardware, "benefit" usually takes the form of getting the most performance out of today’s hardware, continually pushing what is considered to be possible. In this blog we describe two examples: error detection and discard using the “leakage detection gadget” and an automated method for circuit optimization for qubit reuse. “Benefit” can also take other forms, such as productivity. Our emulator brings many benefits to our users, but one that resonates the most is productivity. Being a faithful representation of our QPU performance, the emulator is an accessible tool which users have at their disposal to develop and test new, innovative algorithms. The tools and features Quantinuum releases are driven by users’ feedback; whether you are new to H-Series or a seasoned user, please reach-out and let us know how we can help bring benefit to your research and use case.

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Footnotes:

[1] Mohsin Iqbal et al., Creation of Non-Abelian Topological Order and Anyons on a Trapped-Ion Processor (2023), arXiv:2305.03766 [quant-ph]

[2] Sebastian Leontica and David Amaro, Exploring the neighborhood of 1-layer QAOA with Instantaneous Quantum Polynomial circuits (2022), arXiv:2210.05526 [quant-ph]

[3] Kentaro Yamamoto, Samuel Duffield, Yuta Kikuchi, and David Muñoz Ramo, Demonstrating Bayesian Quantum Phase Estimation with Quantum Error Detection (2023), arXiv:2306.16608 [quant-ph]

[4] Reza Haghshenas, et al., Probing critical states of matter on a digital quantum computer (2023),
arXiv:2305.01650 [quant-ph]

[5] S. A. Moses, et al., A Race Track Trapped-Ion Quantum Processor (2023), arXiv:2305.03828 [quant-ph]

[6] K. Mayer, Mitigating qubit leakage errors in quantum circuits with gadgets and post-selection, 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Broomfield, CO, USA, (2022), pp. 809-809, doi: 10.1109/QCE53715.2022.00126.

[7] Matthew DeCross, Eli Chertkov, Megan Kohagen, and Michael Foss-Feig, Qubit-reuse compilation with mid-circuit measurement and reset (2022), arXiv:2210.08039 [quant-ph]

In the last 6 months, Quantinuum H-Series hardware has demonstrated explosive performance improvement. Quantinuum’s System Model H1-1, Powered by Honeywell, has demonstrated going from 2¹⁴ = 16,384 quantum volume (QV) announced in February 2023 to now 2¹⁹ = 524,288, with all the details and data released on our GitHub repository for full transparency. At a quantum volume of 524,288, H1-1 is 1000x higher than the next best reported quantum volume.

We set a big goal back in 2020 when we launched our first quantum computer, HØ. HØ was launched with six qubits and a quantum volume of 2⁶ = 64, and at that time we made the bold and audacious commitment to increasing the quantum volume of our commercial machines 10x per year for 5 years, equating to a quantum volume of 8,388,608 or 2²³ by the end of 2025. In an industry that is often accused of being over-hyped, a commitment like this was easy to forget. But we did not forget. Diligently, our scientists and engineers continued to achieve world-record after world-record in a tireless and determined pursuit to systematically improve the overall performance of our quantum computers. As seen in Figure 1, from 2020 to early 2023, we have steadily been increasing the quantum volume to demonstrate that increased qubit count while reducing errors directly translates to more computational power. Just within 2023 we’ve had multiple announcements of quantum volume improvements.  In February we announced that H1-1 had leapfrogged 2¹⁴ and achieved a quantum volume of 2¹⁵. In May 2023, we launched H2-1 with 32 qubits at a quantum volume of 2¹⁶. Now we are thrilled to announce the sequential improvements of 2¹⁷, 2¹⁸, and 2¹⁹, all on H1-1.

Importantly, none of these results were “hero results”, meaning there are no special calibrations made just to try to make the system look better. Our quantum volume data is taken on our commercial systems interwoven with customer jobs. What we experience is what our customers experience. Instead of improving at 10x per year as we committed back in 2020, the pace of improvement over the past 6 months has been 30x, accelerating at least one year from our 5-year commitment. While these demonstrations were made using H1-1, the similarities in the designs of H1-2 (now upgraded with 20 qubits) and H2-1, our recently released second generation system, make it straightforward to share the improvements from one machine to another and achieve the same results.

In this young and rapidly evolving industry, there are and will be disagreements about which benchmarks are best to use. Quantum volume, developed by IBM, is undeniably rigorous. Quantum volume can be measured on any gate-based machine. Quantum volume has been peer-reviewed and has well defined assumptions and processes for making the measurements. Improvements in QV require consistent reductions in errors, making it likely that no matter the application, QV improvements translate to better performance. In fact, to realize the exponential increase in power that quantum computers promise, it is required to continue to reduce these error rates. The average two-qubit gate error with these three new QV demonstrations was 0.13%, the best in the industry. We measure many benchmarks, but it is for these reasons that we have adopted quantum volume as our primary system-wide benchmark to report our performance.

Putting aside the argument of which benchmark is better, year-over-year improvements in a rigorous benchmark do not happen accidentally. It can only happen because the dedicated, talented scientists and engineers that work on H-Series hardware have a deep understanding of its error model and a deep understanding of how to reduce the errors to make overall performance improvements. Equally important the talented scientists and engineers have mastery of their domain expertise and can dream-up and then implement the improvements. These validated error models become the bedrock of future systems’ design, instilling confidence that those systems will have well understood error models, and the performance of those systems can also be systematically improved and ultimate performance goals achieved. Taking nothing away from those talented scientists and engineers, but having perfect, identical qubits and employing our quantum charge coupled device (QCCD) architecture does give us an advantage that all the other architectures and other modalities do not have.

What should potential users of H-Series quantum computers take away from this write-up (and what do current users already know)?

1. Quantinuum is committed to systematically improving the core performance of our quantum computing hardware. The better the fundamental performance, the lower the overhead will be when doing error mitigation, error detection, and ultimately error correction. This provides confidence in our ability to deliver fault-tolerant compute capabilities.
2. Progress on your research, use-case, or application can be accelerated by getting access to H-series technology because our quantum computers can do circuits that other technologies cannot. “It actually works!” exclaim excited first-time users.
3. Quantinuum intends to continue to be the quantum computing company that quietly over-delivers, even on big goals.

^1. https://github.com/CQCL/quantinuum-hardware-quantum-volume

^2. https://quantum-journal.org/papers/q-2022-05-09-707/

Quantinuum’s 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 Quantinuum’s H-Series quantum processors due to their various qualities and functions of which measurement and ‘feed-forward’ 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 “very difficult” 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, Quantinuum 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 feed-forward (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 Quantinuum’s 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 “Topological Order from Measurements and Feed-Forward on a Trapped Ion Quantum Computer”, Quantinuum, working with colleagues from the California Institute of Technology and Harvard University use feed-forward to explore topologically ordered phases of matter. 

Separately, a different team of scientists from Quantinuum, 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 “Experimental Demonstration of the Advantage of Adaptive Quantum Circuits”. 

Two of Quantinuum’s physicists who worked on both experiments, Henrik Dreyer and Michael Foss-Feig, offered some observations on the work.

“While 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,” said Henrik, managing director and scientific lead at Quantinuum’s office in Munich, Germany.

Michael, who is based at Quantinuum’s world-leading quantum computing laboratory outside of Denver, Colorado, also described feed-forward and adaptive quantum circuits as a jump toward meaningful simulations.

“This capability speeds up the timeline for new scientific discoveries,” 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 “the 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.”

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 “firmly 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.”

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.

“We 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,” Michael said. The metric proposed in the adaptive circuits paper achieved exactly that.

A good match: Trapped ion architecture and feed-forward 

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

“It's a huge challenge to implement this in a way that works well,” Michael said. 

Quantinuum’s 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:

“While 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 Quantinuum’s H-Series programmable Ytterbium ion trap quantum computer.”

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

In summary – 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.

Quantinuum has unveiled our new optics and electromechanical engineering laboratory space at our largest operating site, in Broomfield Colorado.

Not only is the new space more than twice as big as the space we occupy today, but it will also house a clean, quiet, temperature controlled, state-of-the-art laboratory that befits the excellence of the work we do here.

Our hardware engineering and teams are focused on optimizing, customizing, and miniaturizing the components that power the H-Series quantum computers, demanding that our teams blur the boundaries between discrete disciplines, such as bulk optics and micro photonics, or embedded software and control electronics. Getting the handover right between different deeply skilled experts requires proximity and an intimate understanding of how one workflow blends into the other.

This is why this new lab space will make such a powerful contribution to our hardware success: because it is built around the needs of deeply connected, multidisciplinary teams, entirely focused on our goal of designing and building the first truly useful, breakaway quantum computer.

Six things you need to know about Quantinuum’s new lab

1. State-of-the-Art Facility: Our new lab, with its state-of-the-art design and facilities, creates an environment for collaboration that will spur advances in the fields of optics, photonics, electromechanical engineering, and others.
   
   
2. Expansive and Efficient Space: The lab is not only going to be much larger than what we have today, it is also highly functional, with high ceilings and cloud superstructures over optics benches for better movement and workflow, which will afford our teams greater flexibility and access to every angle of the technology they are creating.
   
   
3. Advancing Micro and Nano Optics: As we aim to reduce our beam delivery size, we're drawing together the work of different teams, creating a dynamic environment where optics transitions into photonics, yielding new potential in terms of size, scale and performance. The location of certain teams will enable such cross-disciplinary workflows.
   
   
4. Custom Fabrication Capabilities: The new optics lab, together with our highly capable chip and trap fabrication capabilities, is designed to enable generational transitions from off-the-shelf, multipurpose optical components to fabricating more compact and fit-for-purpose devices: Think of moving from Functionality on a Table, through Functionality on a Chip, to Functionality on a Trap.
   
   
5. Greater Collaboration and Integration: The new laboratory layout improves the way some teams are co-located, with an eye always toward helping to enhance collaboration. For example, the new engineering space was designed to facilitate close cooperation between the electronics and software teams who design our control systems, as well as the mechanical teams who package the hardware and the test teams who validate it.  Such proximity between disciplines supports faster decision-making and more efficient resolution of interdisciplinary challenges, leading to a virtuous circle of accelerated design and development, faster innovation, and better quantum computers.
   
   
6. Ready for Future Expansion: As we prepare for the next stage of the journey towards fault-tolerant quantum computing, where demand for quantum compute will scale exponentially everywhere, this new laboratory space is fit for the future as well as meeting the demands of today.

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