Robust Cache-Aware Quantum Processor Layout

Quantum computation has taken over as one of the largest current research areas in computer architecture and information theory. With the potential to make a large number of factorization-based encryption methods obsolete, companies and governments around the globe are racing to build the first large-scale quantum computer. Currently, most quantum computers are noisy intermediate-scale quantum (NISQ), using a relatively small collection of unreliable qubits. While error correction methods exist, they require a large number of ancilla qubits to protect the data qubits which is not practical for use on current NISQ machines. However, following the Dowling-Neven Law, available qubits on a superconducting chip are growing at an exponential rate similar to Moore’s Law. Looking toward larger scale quantum machines, we examine a method to increase usable qubit density of quantum machines implementing error correction by using quantum caches that utilize simpler error correction codes. Alternatively, this also allows for the design of reliable systems while meeting the performance and qubit requirements for quantum algorithms. We modify the Qiskit quantum simulation library to work with caches and investigate the effects of region size and topology on the swap characteristics of algorithm execution. We also present our results and discuss recommended topologies for each algorithm. Lastly, we present mix scale-out simulations to examine the impact of cache on future large-scale machines. The default central cache topology gains a maximum performance increase of 2.15 times compared to the worst topology, which creates a robust cache-aware quantum processor layout.

[1]  Lov K. Grover A fast quantum mechanical algorithm for database search , 1996, STOC '96.

[2]  Travis S. Humble,et al.  Quantum supremacy using a programmable superconducting processor , 2019, Nature.

[3]  Bin Li,et al.  Soft error resilience in Big Data kernels through modular analysis , 2016, The Journal of Supercomputing.

[4]  Andrew W. Cross,et al.  The IBM Q experience and QISKit open-source quantum computing software , 2018 .

[5]  Mark Um,et al.  Single-qubit quantum memory exceeding ten-minute coherence time , 2017, 1701.04195.

[6]  Rui Chao,et al.  Quantum Error Correction with Only Two Extra Qubits. , 2017, Physical review letters.

[7]  Bin Li,et al.  Design configuration selection for hard-error reliable processors via statistical rules , 2014, Microprocess. Microsystems.

[8]  D. Deutsch,et al.  Rapid solution of problems by quantum computation , 1992, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences.

[9]  Robert Wille,et al.  Determining the minimal number of swap gates for multi-dimensional nearest neighbor quantum circuits , 2015, The 20th Asia and South Pacific Design Automation Conference.

[10]  Koen Bertels,et al.  An Experimental Microarchitecture for a Superconducting Quantum Processor , 2017, 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[11]  Moinuddin K. Qureshi,et al.  Not All Qubits Are Created Equal: A Case for Variability-Aware Policies for NISQ-Era Quantum Computers , 2018, ASPLOS.

[12]  Shor,et al.  Scheme for reducing decoherence in quantum computer memory. , 1995, Physical review. A, Atomic, molecular, and optical physics.

[13]  Bin Li,et al.  Universal rules guided design parameter selection for soft error resilient processors , 2011, (IEEE ISPASS) IEEE INTERNATIONAL SYMPOSIUM ON PERFORMANCE ANALYSIS OF SYSTEMS AND SOFTWARE.

[14]  Bin Li,et al.  A framework for evaluating comprehensive fault resilience mechanisms in numerical programs , 2015, The Journal of Supercomputing.

[15]  C. Monroe,et al.  Scaling the Ion Trap Quantum Processor , 2013, Science.

[16]  S. Goyal,et al.  Geometry of the generalized Bloch sphere for qutrits , 2011, 1111.4427.

[17]  Bin Li,et al.  Comprehensive and Efficient Design Parameter Selection for Soft Error Resilient Processors via Universal Rules , 2014, IEEE Transactions on Computers.

[18]  Frederic T. Chong,et al.  Quantum Memory Hierarchies: Efficient Designs to Match Available Parallelism in Quantum Computing , 2006, 33rd International Symposium on Computer Architecture (ISCA'06).

[19]  Zaixin Lu,et al.  Enforcing Crash Consistency of Evolving Network Analytics in Non-Volatile Main Memory Systems , 2019, 2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT).

[20]  Umesh V. Vazirani,et al.  Quantum complexity theory , 1993, STOC.

[21]  Bin Li,et al.  Predicting Architectural Vulnerability on Multithreaded Processors under Resource Contention and Sharing , 2013, IEEE Transactions on Dependable and Secure Computing.

[22]  Ying Li,et al.  Resource costs for fault-tolerant linear optical quantum computing , 2015, 1504.02457.

[23]  A. Steane Multiple-particle interference and quantum error correction , 1996, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[24]  Dmitri Maslov,et al.  Experimental comparison of two quantum computing architectures , 2017, Proceedings of the National Academy of Sciences.

[25]  C. Monroe,et al.  Architecture for a large-scale ion-trap quantum computer , 2002, Nature.

[26]  Mark Oskin,et al.  Microcoded Architectures for Ion-Tap Quantum Computers , 2008, 2008 International Symposium on Computer Architecture.

[27]  A S Sørensen,et al.  Coupling nitrogen-vacancy centers in diamond to superconducting flux qubits. , 2010, Physical review letters.

[28]  T. Aaron Gulliver,et al.  Constructions of good entanglement-assisted quantum error correcting codes , 2016, Designs, Codes and Cryptography.

[29]  Zaixin Lu,et al.  NVGraph: Enforcing Crash Consistency of Evolving Network Analytics in NVMM Systems , 2020, IEEE Transactions on Parallel and Distributed Systems.

[30]  Liang Jiang,et al.  New class of quantum error-correcting codes for a bosonic mode , 2016, 1602.00008.

[31]  Daniel R. Simon,et al.  On the power of quantum computation , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[32]  Peter W. Shor,et al.  Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1995, SIAM Rev..

[33]  Bin Li,et al.  Efficient Microarchitectural Vulnerabilities Prediction Using Boosted Regression Trees and Patient Rule Inductions , 2010, IEEE Transactions on Computers.

[34]  R. Barends,et al.  Superconducting quantum circuits at the surface code threshold for fault tolerance , 2014, Nature.

[35]  Lu Peng,et al.  Soft error resilience of Big Data kernels through algorithmic approaches , 2017, The Journal of Supercomputing.

[36]  Bin Li,et al.  Versatile prediction and fast estimation of Architectural Vulnerability Factor from processor performance metrics , 2009, 2009 IEEE 15th International Symposium on High Performance Computer Architecture.