Designing calibration and expressivity-efficient instruction sets for quantum computing
arXiv:2106.15490 · doi:10.1109/ISCA52012.2021.00071
Abstract
Near-term quantum computing (QC) systems have limited qubit counts, high gate (instruction) error rates, and typically support a minimal instruction set having one type of two-qubit gate (2Q). To reduce program instruction counts and improve application expressivity, vendors have proposed, and shown proof-of-concept demonstrations of richer instruction sets such as XY gates (Rigetti) and fSim gates (Google). These instruction sets comprise of families of 2Q gate types parameterized by continuous qubit rotation angles. However, having such a large number of gate types is problematic because each gate type has to be calibrated periodically, across the full system, to obtain high fidelity implementations. This results in substantial recurring calibration overheads even on current systems which use only a few gate types. Our work aims to navigate this tradeoff between application expressivity and calibration overhead, and identify what instructions vendors should implement to get the best expressivity with acceptable calibration time. We develop NuOp, a flexible compilation pass based on numerical optimization, to efficiently decompose application operations into arbitrary hardware gate types. Using NuOp and four important quantum applications, we study the instruction set proposals of Rigetti and Google, with realistic noise simulations and a calibration model. Our experiments show that implementing 4-8 types of 2Q gates is sufficient to attain nearly the same expressivity as a full continuous gate family, while reducing the calibration overhead by two orders of magnitude. With several vendors proposing rich gate families as means to higher fidelity, our work has potential to provide valuable instruction set design guidance for near-term QC systems.
arXiv comment14 pages, 11 figures
References in corpus (42)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum Machine Learning
- A variational eigenvalue solver on a quantum processor
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- A Quantum Approximate Optimization Algorithm
- Characterizing Quantum Supremacy in Near-Term Devices
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- State preservation by repetitive error detection in a superconducting quantum circuit
- Quantum algorithms for quantum chemistry and quantum materials science
- Time Optimal Control in Spin Systems
- Validating quantum computers using randomized model circuits
- Superconducting qubit in waveguide cavity with coherence time approaching 0.1ms
- Efficient Z-Gates for Quantum Computing
- Experimental Comparison of Two Quantum Computing Architectures
- Benchmarking an 11-qubit quantum computer
- Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
- A blueprint for demonstrating quantum supremacy with superconducting qubits
- Optimal Quantum Circuits for General Two-Qubit Gates
- Optimal Creation of Entanglement Using a Two--Qubit Gate
- Quantum Circuits for General Multiqubit Gates
- Quantum-assisted quantum compiling
- Quantum Approximate Optimization Algorithm for MaxCut: A Fermionic View
- Resource-Aware Quantum Programming with General Recursion and Quantum Control
- Demonstrating a Continuous Set of Two-qubit Gates for Near-term Quantum Algorithms
- Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers
- Concrete Categorical Model of a Quantum Circuit Description Language with Measurement
- Unsupervised Machine Learning on a Hybrid Quantum Computer
- Compiling quantum circuits to realistic hardware architectures using temporal planners
- Quantum Circuit Placement
- Implementation of the XY interaction family with calibration of a single pulse
- Observation of separated dynamics of charge and spin in the Fermi-Hubbard model
- Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation
- Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets
- A Rudimentary Quantum Compiler
- Quantum Circuits for Quantum Channels
- A Rudimentary Quantum Compiler(2cnd Ed.)
- The Snake Optimizer for Learning Quantum Processor Control Parameters
- Fixed-Depth Two-Qubit Circuits and the Monodromy Polytope
- Programming a quantum computer with quantum instructions
- Heuristics for Quantum Compiling with a Continuous Gate Set
- Enhancing a Near-Term Quantum Accelerator's Instruction Set Architecture for Materials Science Applications
Cited by in corpus (9)
- Quantum Instruction Set Design for Performance
- Optimal synthesis into fixed XX interactions
- 2QAN: A quantum compiler for 2-local qubit Hamiltonian simulation algorithms
- One Gate Scheme to Rule Them All: Introducing a Complex Yet Reduced Instruction Set for Quantum Computing
- Co-Designed Architectures for Modular Superconducting Quantum Computers
- Quantum-classical tradeoffs and multi-controlled quantum gate decompositions in variational algorithms
- MIRAGE: Quantum Circuit Decomposition and Routing Collaborative Design using Mirror Gates
- Parallel Driving for Fast Quantum Computing Under Speed Limits
- Software mitigation of coherent two-qubit gate errors