In recent years, quantum computing (QC) research has moved from the realm of theoretical physics and mathematics into real implementations. With many different potential hardware implementations, quantum computer architecture is a rich field with an opportunity to solve interesting new problems and to revisit old ones. This paper presents a QC architecture tailored to physical implementations with highly mobile and persistent quantum bits (qubits). Implementations with qubit coherency times that are much longer than operation times and qubit transportation times that are orders of magnitude faster than operation times lend greater flexibility to the architecture. This is particularly true in the placement and locality of individual qubits. For concreteness, we assume a physical device model based on electron-spin qubits on liquid helium (eSHe). Like many conventional computer architectures, QCs focus on the efficient exposure of parallelism.We present here a QC microarchitecture that enjoys increasing computational parallelism with size and latency scaling only linearly with the number of operations. Although an efficient and high level of parallelism is admirable, quantum hardware is still expensive and difficult to build, so we demonstrate how the software may be optimized to reduce an application's hardware requirements by 25% with no performance loss. Because the majority of a QC's time and resources are devoted to quantum error correction, we also present noise modeling results that evaluate error correction procedures. These results demonstrate that idle qubits in memory need only be refreshedapproximately once every one hundred operation cycles.
In recent years, quantum computing (QC) research has moved from the realm of theoretical physics and mathematics into real implementations. With many different potential hardware implementations, quantum computer architecture is a rich field with an opportunity to solve interesting new problems and to revisit old ones. This paper presents a QC architecture tailored to physical implementations with highly mobile and persistent quantum bits (qubits). Implementations with qubit coherency times that are much longer than operation times and qubit transportation times that are orders of magnitude faster than operation times lend greater flexibility to the architecture. This is particularly true in the placement and locality of individual qubits. For concreteness, we assume a physical device model based on electron-spin qubits on liquid helium (eSHe). Like many conventional computer architectures, QCs focus on the efficient exposure of parallelism.We present here a QC microarchitecture that enjoys increasing computational parallelism with size and latency scaling only linearly with the number of operations. Although an efficient and high level of parallelism is admirable, quantum hardware is still expensive and difficult to build, so we demonstrate how the software may be optimized to reduce an application's hardware requirements by 25% with no performance loss. Because the majority of a QC's time and resources are devoted to quantum error correction, we also present noise modeling results that evaluate error correction procedures. These results demonstrate that idle qubits in memory need only be refreshedapproximately once every one hundred operation cycles.
In recent years, quantum computing (QC) research has moved from the realm of theoretical physics and mathematics into real implementations. With many different potential hardware implementations, quantum computer architecture is a rich field with an opportunity to solve interesting new problems and to revisit old ones. This paper presents a QC architecture tailored to physical implementations with highly mobile and persistent quantum bits (qubits). Implementations with qubit coherency times that are much longer than operation times and qubit transportation times that are orders of magnitude faster than operation times lend greater flexibility to the architecture. This is particularly true in the placement and locality of individual qubits. For concreteness, we assume a physical device model based on electron-spin qubits on liquid helium (eSHe).
Quantum computer hardware is predicted to scale over hundreds of thousands of qubits coming online in the next decade. Despite significant theoretical and experimental QEC progress, quantum computer architecture has suffered a significant gap, with bottom-up physical-device-driven challenges largely disconnected from top-down QEC-code-driven considerations. In this work, we unify these two views, presenting a complete heterogeneous quantum computing architecture incorporating task-specific hardware selection and QEC encoding, and agnostic to code selection or physical qubit parameters. Our approach further enables special-purpose processing modules, and includes a full microarchitecture for fault-tolerant implementation of interfaces between quantum processing units and quantum memories. Using this architecture and a new fully featured compiler functioning across subsystems at the scale of 1,000 logical qubits, we schedule and orchestrate a variety of algorithms down to hardware-specific instructions; a detailed accounting of all operations reveals up to 551x reduction in algorithmic logical error and up to 138x reduction in physical-qubit overhead compared to a monolithic baseline architecture. We then consider the factorization of 2048-bit RSA-integers; using an experimentally demonstrated grid-coupling topology, factoring RSA-2048 requires 381k physical qubits and 9.2 days, which can be reduced to 4.9 days via addition of an algorithm-specific accelerator for the Adder subroutine (requiring 439k qubits). Finally, assuming hypothetical long-range coupling, implementing quantum memory using qLDPC codes reduces the resources required for factoring to just 190k qubits and under 10 days. These results and the tooling we have built indicate that heterogeneous quantum-computer architectures can deliver significant, verifiable benefits on realistic hardware.
This thesis addresses the design of a quantum computer (QC) architecture. Quantum error correction is a dominant factor as it is required to protect quantum bits (qubits) from decoherence. However, error correction's costs are substantial and call for a scale-up of 100X more qubits and instruction-level parallelism (ILP) on the order of 10,000 simultaneous operations per cycle. These are daunting challenges for a QC architect, who must balance demands in performance, control efficiency, and scalability of design.
Error modeling is important for designing a QC so that error correction implementations and microarchitecture designs may be evaluated for effectiveness. This thesis presents a new deterministic approach to error modeling that is on the order of 1000X faster than previous Monte Carlo error models. While the deterministic error model is memory limited, it is capable of evaluating useful problems containing on the order of a dozen logical qubits encoded in the [[7,1,3]] quantum error correcting code (QECC).
This dissertation presents a new QC processor architecture that leverages highly mobile qubits found in some QC technologies such as electron-spins on liquid helium (eSHe). The processor architecture presented here is designed to efficiently handle at least an order of magnitude more ILP than previously proposed designs, and it accommodates increasing ILP with size and latency costs scaling only linearly. Compilers are also crucial to a computer's performance and efficiency, and this dissertation describes new compiler optimizations including one that reduces hardware requirements by 25% with no performance loss.
Multiple QC processors may be organized into a tiled configuration to provide sufficient ILP across the entire QC. This dissertation proposes a new distributed block tiling strategy that distributes encoded block qubits across arrays of data tiles and offloads non-critical path error correction routines to separate ancilla tiles. This strategy reduces tile capacity requirements by over 100X with the [[21,3,5]] QECC and achieves a simulated speedup of 2.8X for the eSHe QC when compared to the traditional tiling approach.
In summary, this dissertation presents a novel QC architecture and design tools that improve performance and scalability while making efficient use of both control and qubit resources.
The assumption of maximum parallelism support for the successful realization of scalable quantum computers has led to homogeneous, "sea-of-qubits" architectures. The resulting architectures overcome the primary challenges of reliability and scalability at the cost of physically unacceptable system area. We find that by exploiting the natural serialization at both the application and the physical microarchitecture level of a quantum computer, we can reduce the area requirement while improving performance. In particular we present a scalable quantum architecture design that employs specialization of the system into memory and computational regions, each individually optimized to match hardware support to the available parallelism. Through careful application and system analysis, we find that our new architecture can yield up to a factor of thirteen savings in area due to specialization. In addition, by providing a memory hierarchy design for quantum computers, we can increase time performance by a factor of eight. This result brings us closer to the realization of a quantum processor that can solve meaningful problems
In a (future) quantum computer a single logical quantum bit (qubit) will be made of multiple physical qubits. These extra physical qubits implement mandatory extensive error checking. The efficiency of error correction will fundamentally influence the performance of a future quantum computer, both in latency/speed and in error threshold (the worst error tolerated for an individual gate). Executing this quantum error correction requires scheduling the individual operations subject to architectural constraints. Since our last talk on this subject, a team of researchers at Sandia National Labortories has designed a logical qubit architecture that considers all relevant architectural issues including layout, the effects of supporting classical electronics, and the types of gates that the underlying physical qubit implementation supports most naturally. This is a two-dimensional system where 2-qubit operations occur locally, so there is no need to calculate more complex qubit/information transportation. Using integer programming, we found a schedule of qubit operations that obeys the hardware constraints, implements the local-check code in the native gate set, and minimizes qubit idle periods. Even with an optimal schedule, however, parallel Monte Carlo simulation shows that there is no finite error probability for the native gates such that the error-correction system would be benecial.more » However, by adding dynamic decoupling, a series of timed pulses that can reverse some errors, we found that there may be a threshold. Thus finding optimal schedules for increasingly-refined scheduling problems has proven critical for the overall design of the logical qubit system. We describe the evolving scheduling problems and the ideas behind the integer programming-based solution methods. This talk assumes no prior knowledge of quantum computing.« less
Computers reduce human effort and also focus on increasing the performance to push the technology forward. Many approaches have been devised to increase the performance of the computers. One such way is to reduce the size of the transistors used in the systems. Another very significant tactic is to use quantum computers. It proved to be very effective when used to factor large numbers. It was found that it could decrypt codes in 20 minutes which took billions of years with classical computers. This was a great motivation for focusing on this topic. A quantum computer allows a ‘quantum bit’ or qubit to have three states − 0, 1, and 0 or 1. The last state is the coherent state. This enables an operation to be performed on two diverse values at the same time. However, this brings out a problem of decoherence. It becomes difficult to perform the computation using quantum computers. A quantum computer is desired to have five capabilities - scalable system, initializable state, long decoherence time, universal set of quantum gates, high efficiency measurements. Architecture of the quantum computer is the new research area. It is affected by quantum arithmetic, error management, and cluster-state computing. Without it, the quantum algorithms would not prove to be as efficient. To fully utilize the power of a quantum computer, the algorithms used should be based on quantum parallelism. This paper discusses a few of the basic concepts used in quantum computing briefly. The paper also focuses on different architectures of quantum computer available in literature. However, the problem of a practical large-scale quantum computer still persists.