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Understanding Locally Encoded Defects in Quantum Circuits

The realm of quantum computing is continually evolving, with researchers and scientists making strides in understanding the intricacies of quantum systems. One such concept that has gained significant attention in recent years is the concept of Locally Encoded Defects (LED) in quantum circuits. LED is a method used to approximate a fixed-point state with zero correlation length. This approach is based on the measurement of qubits in a quantum system and the calculation of stabilizer and Wilson loop values, which help identify local fluctuations and anyonic excitations.

LED involves a local decoder that works to remove these fluctuations. Additionally, the coarse-graining of the lattice is performed to reduce uncorrected errors. This strategy has proven to be beneficial in detecting topological order and distinguishing between topological and trivial states. It provides a powerful tool for characterizing topologically ordered states in experimental quantum systems and offering new insights into the nature of different regimes in these systems.

As detailed in a research article, the use of LED in quantum circuits can greatly improve their performance and reliability. Quantum computers, being inherently different from classical computers, are susceptible to errors due to their quantum nature. However, the application of LED can help in identifying and rectifying these errors, thus improving the overall performance of these systems.

Moreover, LED is not just limited to error correction. It could also be utilized for enhancing the computational capabilities of quantum systems. The current research in this area is focused on exploring the potential applications of LED and how it can be leveraged to make quantum computing more practical and efficient.

Despite the promising advancements, understanding atomic-like quantum systems, especially solid-state atom-like systems, impurity-based qubits in semiconductors, and defects in 2D and 3D materials, remains a challenge. The Quantum Staging Group (QSG) has been working to fill this gap by promoting materials science for the development of quantum information sciences and quantum sensing.

In a recent workshop held at the 2022 MRS Spring Meeting, QSG brought together scientists with experimental and theoretical expertise in materials for quantum technologies. The goal was to discuss key near-term challenges to further promote and accelerate the development of solid-state atom-like systems with applications in quantum technologies.

The workshop addressed four main themes, including unifying perspectives on relevant length scales for quantum systems, addressing materials challenges in quantum information technologies, the role of electrical noise in atomic-like systems, and predictive challenges for atomic-like quantum systems. It is clear that a multi-disciplinary approach, involving both theoretical and experimental expertise, is essential for advancing our understanding of atomic-like quantum systems.

In conclusion, the concept of Locally Encoded Defects in quantum circuits holds immense potential for the future of quantum computing. From improving the performance and reliability of quantum computers to helping characterize topologically ordered states in experimental quantum systems, LED is paving the way for a better understanding of the quantum world. However, it is crucial to continue addressing the challenges and to foster collaboration and knowledge sharing in this field to unlock the full potential of quantum technologies.

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Understanding Locally Encoded Defects in Quantum Circuits: A Promising Tool for Quantum Computing - Medriva

Breakthrough technology accelerates development of algorithms for practical use in quantum computers Fujitsu Limited

Tokyo, February 19, 2024

Fujitsu today announced the development of a novel technique on a quantum simulator that speeds up quantum-classical hybrid algorithms, which have been proposed as a method for the early use of quantum computers, achieving 200 times the computational speed of previous simulations. For quantum circuit computations using conventional quantum and classical hybrid algorithms, the number of times of quantum circuit computation increases depending on the scale of the problem to be solved. Larger-scale problems that require many qubits, including simulations in the materials and drug discovery fields, may even require several hundred days.

The newly developed technology enables simultaneous processing of a large number of repetitively executed quantum circuit computations distributed among multiple groups. Fujitsu has also devised a way to simplify problems on a large scale with less loss of accuracy by using one of the world's largest-scale quantum simulators (1) it has developed. Fujitsu has made it possible to perform computations on a quantum simulator in just one day, which would take an estimated 200 days to complete with conventional methods. As a result, it is now possible to complete simulations of large-scale quantum computation within a realistic timeframe and to simulate the behavior of larger molecules computed by a hybrid quantum-classical algorithm, leading to algorithm development.

Fujitsu plans to incorporate this technology into its hybrid quantum computing platform to accelerate research into the practical application of quantum computers in various fields, including finance and drug discovery. Additionally, Fujitsu will not only apply this technology to quantum simulators, but also to accelerate quantum circuit computations on actual quantum computers.

Although the development of fault-tolerant quantum computers (FTQC (2) ) is currently progressing worldwide, current quantum computers face many problems, such as the inability to eliminate the effects of noise. At the same time, in order to demonstrate the usefulness of quantum computers ahead of FTQC, practical applications for small and medium-sized quantum computers (Noisy Intermediate-Scale Quantum Computer, NISQ) with noise tolerance of 100 to 1,000 qubits are being studied.

By applying VQE (3), a typical NISQ algorithm, Fujitsu, for example, has developed a quantum simulator for quantum application development (4) and has been working to speed up quantum circuit computation itself. However, in VQE, the number of iterations of quantum circuit computation increases as the size of the problem increases, so it takes a very long time to perform computation, especially for large problems requiring many qubits, and it is estimated that it takes several 100 days for a quantum simulator. Therefore, it was difficult to develop quantum algorithms for practical use.

In response to this problem, Fujitsu has developed a technology that achieves 200 times higher the performance speed of conventional technologies by simultaneously distributing multiple repetitively executed quantum circuit computations and reducing the amount of quantum circuit computations by reducing accuracy degradation.

Quantum-classical hybrid algorithms seek a quantum circuit that provides the lowest energy state, for example, the ground state of a molecule, by alternating between the process of performing quantum circuit computation and the process of optimizing quantum circuit parameters (5) using a classical computer. However, for parameter optimization of quantum circuits by classical computers, it is necessary to prepare a large number of quantum circuits with small changes in parameters, perform quantum circuit computation for all of them sequentially, and derive the optimal parameters from the results. This requires considerable time for computation, especially for larger-scale problems. Increasing the number of nodes simply to speed up circuit computation has conventionally been limited by communication overhead, and new technologies were required.

Focusing on the fact that quantum circuits with small parameter changes can be executed without affecting each other, Fujitsu has developed a distributed processing technology that enables each group to execute different quantum circuits by dividing the computation nodes of the quantum simulator into multiple groups and using RPC (6) technology to submit quantum circuit computation jobs through the network. Using this technology, multiple quantum circuits with different parameters can be simultaneously distributed and calculated, and the computation time can be reduced to 1/70th of the conventional technology.

In addition, since the computation quantity in the quantum-classical hybrid algorithm is proportional to the number of terms in the equation of the problem to be solved, and the number of terms is the fourth power of the number of qubits in the general VQE, the computation quantity increases as the problem scale increases, and the result cannot be obtained in a realistic time. Through simulations of large molecules using 32 qubits of one of the world's largest 40 qubit quantum simulators, Fujitsu has found that the ratio of terms with small coefficients to the total number of terms increases as the scale increases, and that the effect of terms with small coefficients on the final results of calculations is minimal. By taking advantage of this characteristic, Fujitsu was able to achieve both a reduction in the number of terms in the equation and prevention of deterioration in computation accuracy, thereby reducing the quantum circuit computation time by approximately 80%.

By combining these two technologies, Fujitsu was able to demonstrate for the first time in the world that when distributed processing of 1024 compute nodes into 8 groups for a 32 qubit problem, it was possible to achieve a quantum simulation run time of 32 qubits in one day, compared to the previous estimate of 200 days. This is expected to advance the development of quantum algorithms for problems with a large number of qubits and the application of quantum computers to the fields of materials and finance.

We are investigating the application of quantum computers to materials development. Among them, the use of VQE in NISQ devices is an essential consideration. We expect that this acceleration technology will greatly speed up the principle verification of the VQE algorithm.

We are studying the use of VQE to calculate the energy of molecules related to semiconductor materials, to predict the electronic structure and physical properties of specific materials, and to optimize chemical reactions in semiconductor manufacturing processes. We hope that accelerating this process will enable us to quickly verify the principle and effectiveness of the VQE algorithm and discover its usefulness. NISQ devices whose use is limited by noise and errors will be considered with an eye toward these limitations.

The Sustainable Development Goals (SDGs) adopted by the United Nations in 2015 represent a set of common goals to be achieved worldwide by 2030. Fujitsus purpose to make the world more sustainable by building trust in society through innovation is a promise to contribute to the vision of a better future empowered by the SDGs.

Fujitsus purpose is to make the world more sustainable by building trust in society through innovation. As the digital transformation partner of choice for customers in over 100 countries, our 124,000 employees work to resolve some of the greatest challenges facing humanity. Our range of services and solutions draw on five key technologies: Computing, Networks, AI, Data & Security, and Converging Technologies, which we bring together to deliver sustainability transformation. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.7 trillion yen (US$28 billion) for the fiscal year ended March 31, 2023 and remains the top digital services company in Japan by market share. Find out more: http://www.fujitsu.com.

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All company or product names mentioned herein are trademarks or registered trademarks of their respective owners. Information provided in this press release is accurate at time of publication and is subject to change without advance notice.

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Fujitsu develops technology to speed up quantum circuit computation in quantum simulator by 200 times - Fujitsu

The scale-up challenge facing quantum computing (QC) is daunting and varied. Its commonly held that 1 million qubits (or more) will be needed to deliver practical fault tolerant QC. Its also a varied challenge because today there are many qubit types (superconducting, trapped ion, neutral atom, photonics, topological, quantum dots, etc.) each facing very different scale-up approaches; for example in solid state systems part of the challenge typically involves efficient interconnecting of stationary qubits, while neutral atom- and trapped ion-based systems can require actually moving the qubits around.

Recently, neutral atom specialist Atom Computing reported developing techniques that now permits it to full load an array of 1225 sites with its qubits, neutral atoms. Atom posted a paper in late January on the work, explaining some of the technical details. Atom first reported creating a 1225-site array with 1180 occupied sites last fall claiming to be the first to break the 1000 qubit threshold.

The new paper sheds more detail on Atoms approach for scaling up its arrays, which the company says is a significant step on its path to effective scaling and error correction.

In this protocol, the tweezers provide microscopic rearrangement of atoms, while the cavity-enhanced lattices enable the creation of large numbers of deep optical potentials that allow for rapid low-loss imaging of atoms. We apply this protocol to demonstrate deterministic filling (99% per-site occupancy) of 1225-site arrays. Because the reservoir is repeatedly filled with fresh atoms, the array can be maintained in a filled state indefinitely. We anticipate that this protocol will be compatible with mid-circuit reloading, which will be a key capability for running large-scale error-corrected quantum computations whose durations exceed the lifetime of a single atom in the system, write the researchers.

Currently the range of qubit-counts on quantum processors varies from single digits to hundreds. In December, IBM, whose QCs use superconducting qubits, introduced an 1121-qubit Condor QPU (not yet available) and a smaller 133-qubit Heron QPU thats optimized for combining with multiple QPUs into larger quantum systems. QuEra, another neutral atom-based QC developer, has a 256-qubit (Aquila) device, and in January it showcased a roadmap QuEra says will lead to 10,000 physical qubits and 100 logical qubits in the 2026 time-frame.

The race is on to scale up physical qubit counts and logical qubit counts (comprised of many physical qubits to implement error correction).

Heres a brief description from the paper:

Typically, tweezer rearrangement is performed by stochastically loading up to a single atom into each trap within an array, imaging the atoms to determine trap occupancy, and then rearranging atoms within the array to create a deterministically occupied sub-array. Crucially, the number of atoms contained in the final array with this approach is no greater than the number initially loaded. Further, because the initial loading is stochastic, the number of sites in the array must generally be substantially larger than de- sired final sub-array (though under certain conditions, near-deterministic loading can be achieved).

Recently, repeated loading of a buffer array from an optical dipole trap reservoir has demonstrated that one can decouple the filling of a six-site target array from a single loading of a cold reservoir. In this work, we ex- tend this concept to repeated loading of a reservoir array, from which we create a deterministically filled target array (typically 99% occupancy) of over 1200 171Yb atoms in 1225 sites. This is made possible by combining optical tweezer arrays with a cavity-enhanced optical lattice to provide both microscopic control and the large number of deep traps required for rapid, high-fidelity, low-loss imaging of large numbers of atoms.

The paper is best read in full.

Link to Atom Computing paper (Iterative assembly of 171Yb atom arrays in cavity-enhanced optical lattices), https://arxiv.org/abs/2401.16177

Top image: Atom Computings neutral atom-based quantum system. Source: Atom Computing

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Atom Computing Reports Advance in Scaling Up Neutral Atom Qubit Arrays - HPCwire